摘要:

Analyst, June 1996, Vol. 121 () 697 Foreword Sensors and Signals 111 This was the third in a series of meetings dedicated to bringing together scientists working in the areas of sensors and chemometrics. The Proceedings of the first two meetings were published in Analytical Proceedings (now Analytical Com- munications), 1991, 28, 102-122 and The Analyst, 1993, 118, 3 15-445, respectively. Fifteen papers from the meeting are presented in this issue. The meeting was organized by the Republic of Ireland Sub-region in association with the Chemo- metrics and Electroanalytical Groups of the Royal Society of Chemistry, and held in the very pleasant surroundings of the Grand Hotel in Malahide, Co. Dublin, on 26-27 October 1995. The meeting was organized into four main scientific themes dealing with chemometrics/sensor arrays, chemical sensors, biosensors, and biomedical applications of sensor technology.There were three main Keynote Lectures. The first was given by Professor Phil Brown (University of Kent at Canterbury) who gave an overview of both algebraic and graphical statistical procedures for relating instrument measurements to ‘true’ measurements. The second was given by Professor Jerry Guilbault (University College Cork), who traced the history of biosensors from the early 1960s to the present day, and included some results of his current research in the area of piezoelectric detection of toxins. An interview with Professor Guilbault can be read on p. 84N of this issue, as can one with Rune Lundin of EDT Instruments, who gives an industrialist’s view of sensor technology (p.86N). The third Keynote Lecture was presented by Professor Gordon Wallace (University of Wollongong) who presented a stimulating lecture entitled ‘Conducting Poly- mers-Soft, Intimate Intermediaries for Biomolecular Events’. There was also a successful poster session and instrument exhibition. No meeting, especially in Ireland, would be quite complete, however, without a little craic, which was provided after dinner in the form of some traditional Irish music from my colleague and co-host of the meeting, Dr. Dermot Diamond, and his wife Tara. Revelries continued into the wee hours of the morning with Dermot joining forces on the fiddle with Brendan Gleeson (co-star with Me1 Gibson in Braveheart). It is hoped that all who took part benefitted from the scientific and cultural exchanges, and further appreciated the need for greater understanding and overlap between the two disciplines. It is hoped to organize a further meeting at some future date, yet to be decided. Malcolm R. Smyth Dublin City University

ISSN:0003-2654    

DOI:10.1039/AN9962100697

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. I21 (699-703) 699 Conducting Polymers and the Bioanalytical Sciences: New Tools for Biomolecular Communications* A Review S. B. Adeloju" and G. G. Wallaceb3t a Centre for Electrochemical Research and Analytical Technology, Department of Chemistry, University of Western Sydney Nepean, P.O. Box 10, Kingswood, NSW 2747, Australia Intelligent Polymer Research Laboratory, Department of Chemistry, University of Wollongong, Northfelds Avenue, Wollongong, NSW 2522, Australia This review covers the evolution of conducting polymers and their use in the bioanalytical sciences. It is the controlled dynamic behaviour of these unique materials that enables such diverse and high-level performance to be achieved. The construction and application of conducting polymers for use as biosensors are the particular emphasis of this paper.Biocompati bility is briefly discussed. Keywords: Conducting polymers; biomolecular communications; biosensors; review Introduction A quest at the forefront of modem technology involves the development of new materials that will permit more effective communication. It is common knowledge that our marked advances in intersystem (e.g., person to person) communica- tions over the last two decades can be attributed to the discovery and development of advanced materials. We still find ourselves, however, somewhat lacking in our ability to communicate with the biomolecular world. While conventional materials are extremely efficient in transmitting and processing electronic information, they are lacking in their ability to extract relevant information from biomolecular systems.It is reasonable, therefore, to assume that our next quantum leap in communica- tions, at the biomolecular level, will also be materials dependent. Such ability will, of course, revolutionize numerous practical technologies such as bioseparations, biosensing and bio- materials, and this in turn will lead to new breakthroughs in medical technologies. The bioanalytical sciences (small-scale bioseparations and biosensing) are poised to reap the most immediate benefits from the development of such materials. However, these new materials that facilitate biomolecular communications will prove invaluable to those involved in studies into the human system and take us some steps closer to unravelling the remaining medical 'mysteries' we encounter in everyday life.Materials considered for effective biomolecular transduction must be: ( i ) capable of pre-determined molecular interactions that can be modified in situ in a controlled fashion; * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. t To whom correspondence should be addressed. (ii) capable of transducing the energy transfer arising from such interactions into electrical signals that are easily mon- itored; (iii) biocompatible and, hence, cause minimal and reversible disturbance to the working environment. Two groups of materials with such properties that have emerged over the last decade are conducting electroactive polymers and hydrogels. This paper examines the usefulness of conducting electro- active polymers in communicating with biosystems and high- lights the fact that in many cases the (controlled) dynamic behaviour of these systems is what makes it possible for them to perform complex and sophisticated functions at the molecular level.' However, before delving into specific discussion on conducting polymers, it is interesting to consider the evolution of the use of polymers in the bioanalytical sciences.The development of polymers in the analytical sciences has been a steady evolution. They have found applications in membrane separations technologies2,3 where the physical properties (porosity) are used to separate unwanted species. More recently, sys tems based on ion exchange, electrically facilitated transport (e.g., electrodialysis) or chemically facili- tated transport4y5 have been introduced and these have assisted in improving our ability to separate more complex mixtures. Another area of separation science in which polymers have also played an important role is in chromatograpy.6-8 The inherent multifunctionality of most polymers can also be utilized in achieving complex separations of biomolecular components.9 In fact, in some cases, it is mandatory to have multifunctional materials capable of a range of interactions in order to effect separations.More recently, polymer-coated silica has been used to combine the mechanical properties of silica with the chemical versatility of polymers for protein separations. 10 The most recent foray involving the use of polymers in the analytical sciences has been in the more modern area of sensors.The widespread application of polymers in this area is not surprising, given their evolution and use in the analytical sciences for the separation and isolation of the components of complex mixtures by physical and chemical methods. These interactions have enabled polymers to be used in development of a wide range of sensors and, not surprisingly, polymers have proved particularly useful in the area of biosensorsll owing to the ability to attach a range of functionalities. Polymers in general are excellent transducing materials since their physical properties (e.g., conductivity) respond to various chemical and/or physical stimuli. However, the discovery of a unique class of polymers, i.e., inherently conducting polymers,700 Analyst, June 1996, Vol.121 has accelerated advances in the development of polymer sensing technologies. In so doing, these novel materials have also impacted on other areas of analytical sciences such as ~hromatography~~J3 and membrane science. I4,l5 However, it is in the area of chemical and biological sensing that most impact on the bioanalytical sciences has been witnessed, and it is this area on which we shall focus in this paper. Conducting Polymers in the Bioanalytical Sciences Polymers such as polypyrroles, polythiophenes and poly- anilines (1-111) are proving to be most useful molecular structures for these applications. The materials are produced using a simple oxidation procedure, e.g., It is this process that highlights the utility of polypyrroles and the limitations of most other conducting polymers as far as biosensors are concerned.Polypyrroles can be electropoly- merized from aqueous media at neutral pH, which allows the incorporation of a wide range of counter ions (see later). Other conducting polymers are more limited in this regard. For example, aniline is soluble only in acidic media and thiophene in organic solvents. As the name implies, these materials are inherently conduct- ing. Unlike conventional polymers, they are capable of conducting electricity and hence capable of functioning as electrode materials. This permits the detection of conventional electron transfer processes as encountered, for example, with many metal ions. The polymers discussed here are also electroactive, that is, they are capable of undergoing oxidation/ reduction themselves according to, for example, If the presence of a target analyte in any way alters this process, then this can be used as a method of detection, taking advantage of the controlled dynamic behaviour of these materials.Conducting polymers can also function as a potentiometric device, in which case the observed potential is determined by the activity of the ions in solution. The fact that these simple structures are easily modified during synthesis (different A-) and in situ (use of electrical stimuli) permits the design and assembly of polymers capable of a myriad of molecular interactions. The desired behaviour can be readily induced by a judicious choice of the polymeric molecular components, the method of assembly and the choice of electrical stimuli.This approach, in its simplest form, has been found to be useful for the chemical sensing of a range of substances on polypyrole films17-23 as shown in Table 1. Molecular Components Inherently conducting polymer structures (in the conducting state) are comprised of a charged polymeric backbone, balanced by incorporated counter ions. To introduce selected function- ality into the structure, functional groups may be attached to the polymer or appropriate counter ions may be used. Covalent attachment The first approach has been used by Foulds and Lowe24 and others25926 to great effect. They attached enzymes or other functional groups (e.g., IV and V ) that permit direct electron transfer between proteins and the conducitng polymer sub- s trate.H Othersz7 have used covalent attachment to anchor enzymes to the conducting polymer backbone. In general, covalent attach- + A' ment results in a lowering of the intrinsic conductivity of the polymer and gives inferior mechanical properties. Samuelson et a1.28 attached proteins by biotinylating the polymer back- bone. Incorporation of appropriate counter ions A simpler, more versatile, approach is to incorporate func- tionality via the counter ion employed. Previous workers have shown that conducting polymers containing C1- as counter ionAnalyst, June 1996, Vol. 121 70 1 will absorb protein from solution, the degree of absorption being dependent on the oxidation state and hence the con- ductivity of the polymer.29 However, these polymers do not possess the appropriate mechanical properties for use as biosensors.It has been established that optimum electronic and mechanical properties are achieved in polypyrroles by the use of sulfonated aromatic compounds such as VI and VII as counter ions. Modification of the functional groups attached to the aromatic ring induces different molecular recognition and hence molecular transport properties,30 in addition to different electronic properties.31 The use of such polymers for the direct detection of proteins has been investigated.32 Sulfonated dyes can be incorporated into conducting polymers as the counter ions to induce interactions with particular proteins.33 A further route to inducing molecular functionality involves the incorporation of functional polyelectrolytes such as VIII-X.- 7% oso, Vlll Dextran sulfate 0SO.J' IX Chondroitin sulfate X Heparin removed from the anchoring (sulfonate) sites in the eventual conducting polymer structure. Chondroitin sulfate, dextran sulfate and heparin have all been incorporated. The incorpor- ated polyelectrolytes retain their inherent bioactivity. Even complex and more delicate molecular entities can be directly incorporated as counter ions. For example, in our laboratories, either antibodies34 or enzymes35 have been incorporated. The degree of activity has been quantified by using radiolabelling, amino acid analysis and confirmation of bioactivity with radiolabelled binding and/or enzymes studies. Perhaps the ultimate incorporation is to borrow molecular systems from nature, e.g., microorganisms36 or living mam- malian cells.37 In recent work, yeast was coated on to a conducting polymer and the metabolic products were monitored electrochemically.In the presence of toxic substances metabo- lism was not observed as this serves as the sensing mech- anism.36 Red blood cells have been incorporated3* by the use of polyelectrolytes which act to stabilize the cells in the presence of the monomer. The polyelectrolyte also facilitates the growth of an adherent, conducting, uniform film (Fig. 1). In some cases, a biocomponent of interest may be oxidized/ reduced directly. For example, it has been shown that cytochrome c oxidation/reduction can be facilitated with polypyrroles containing an electron transfer mediator such as Fe(CN)64- (Fig.2 ) . Alternatively, the biomolecular event may generate an electroactive species, as in the determination of glucose on a polypyrrole-glucose oxidase e l e ~ t r o d e . ~ ~ > ~ 9 This involves conversion of glucose into gluconic acid, thus generating peroxide as the electroactive species (see below). A limitation of this approach is the fact that polypyrroles are Table 1 Chemical sensing of various substances using polypyrrole-based sensors Analyte Alcohol Mercury Silver Ascorbic acid Phosphate Carbamate F-, C1-, Br- Quinone Hydroquinone Proteins Counter ion incorporated into polypyrrole Ref. Tetrafluoroborate 16 Dinitrocarbamate 17 C104-, C1-, Br-, EDTA 18 Hexacyanoferrate(I1) 19 Perchlorate 20 Perchlorate 20 Chloride and perchlorate 21 Benzenesulfonate 22 Benzenesulfonate 22 p-Toluenesulfonate 23 This opens up new possibilities for the introduction of various fUnCtiOnalitieS. This is a particularly attractive route since (as is the case with sulfonated dyes above) the recognition sites are Fig.1 structure. Red blood cells incorporated throughout a conducting polymer702 Analyst, June 1996, Vol. 121 attacked by the peroxide produced particularly when positive potentials are applied.39 P-D-glucose + 0 2 ---+ gluconic acid + H202 Enzymic reaction H202 -+ 2H+ + 0 2 + 2e- Detection process The biochemical reaction of interest may also consume an electroactive product, and this can be used as the signal generation mechanism. An example here is the catalytic conversion of sulfite to sulfate on a polypyrrole-sulfite oxidase electr~de:~O GOD SOD S032- + O2 + H20 --+ S042- + H202 The measured amperometric response in this case resulted predominantly from the enzyme-catalysed reaction and to a lesser extent from the conductivity changes on the polymer surface.Another interesting example involves the catalytic decom- position of urea to carbon dioxide and ammonia on a polypyrrole-urease electrode:35,41 Urease NH2CONH2 - C02NH3 H20 The catalytic reaction causes a change in the conductivity/ resistance of the polymer, resulting in a change in the over-all cell potential: Consequently, the catalytic reaction forces the polypyrrole film to be oxidized (shifts the cell potential in the positive direction) and, hence, results in a positive amperometric response. An even more indirect method involves modification of the polymer oxidation/reduction response (see below) in the presence of a biomolecular component or due to a biochemical reaction.It is envisaged that this mechanism predominates for detection of proteins at P T S - - ~ ~ or antib0dy-containing3~ electrodes. Eoverall = Eapplied + IRdrop [lo& 1 2 1 2 1 Table 2 provides a list of some of the recently reported polypyrrole-based amperometric biosensors. It is clearly evid- ent from range of examples cited that the electroimmobil- ization of the bioactive substrates into conducting polymer provides an excellent basis for the fabrication of novel biosensors. Biocompatibility Other workers have shown that hydrogels are inherently biocompatible materials.The high water content ensures that the surface energy of these materials is such that minimum disturbance is caused to the bioenvironment. Recently we have shown that conducting polymers can be integrated throughout such structures and that conducting polymers, if designed appropriately, can be made to have hydrogel-like properties. The first approach is achieved by casting electrodes into hydrogels. The electropolymerization is then initiated after imbibing monomer throughout the gel (Fig. 3). The conducting polymer grows to 'fill' the dimensions of the gel. However, the Table 2 Some of the reported polypynole-based amperometric bio- sensors Anal yte Glucose NADH Dopamine Cholesterol Ethanol Penicillin Urea Thaumatin Sulfite Biological component Detection method Ref.Alcohol dehydrogenase Amperometric 42 Glucose oxidase Amperometric 39 and aldehyde dehydrogenase Whole banana cells Amperometric 43 Cholesterol oxidase Amperometric 44 Alcohol dehydrogenase Amperometric 45 Penicillinase Amperometric 46 Urease Potentiometric 35 Anti-thaumatin Pulsed amperometric 34 Sulfite oxidase Amperometric 40 I I I I I I I I I I I I I -0.4 -0.2 0 0.2 0.4 0.8 EN versus AgIAgCI Fig. 2 Cyclic voltammograms obtained using a p~lypyrrole-Fe(CN)~~-- coated platinum disc electrode. Voltammograms recorded in (a) 0.1 moll-' potassium phosphate buffer solution, (6) the buffer solution plus 0.1 mmol 1-' cytochrome c and (c) the buffer solution plus 0.2 mmol 1-' cytochrome c. Solution pH = 10.0; scan rate, 20 mV s-I.Results shown are for the fifth potential cycle. Fig. 3 (clear) hydrogel. Conducting polymer (dark) grown electrochemically throughout aAnalyst, June 1996, Vol. 121 703 resultant structure still contains approximately 90% water, confirming that a fibrillar conducting polymer network has been created maintaining an open porous structure. The open porous nature is confirmed by the fact that relatively large molecules such as sulfonated dyes of relative high molecular mass ( > 300) can be released with the use of small electrical stimuli. The second approach involves the growth of conducting polymers using polyelectrolytes as counter ions, as discussed previously. This results in the formation of a composite material with relatively high water contents.These materials retain the conductivity and electroactivity as observed with stand-alone conducting polymer films. The biocompatibility of these materials is evidenced by the ability to culture mammalian cells on them.37J* Conclusions and Future Developments All of these pursuits require the ability to interact/communicate with biomolecular systems and, with minimum disturbance, to monitor and control them. Perusal of the literature suggests that the monitoring and control of such interactions constitute an ‘impossibly’ complex task. However, the fundamental molecu- lar interactions that occur are well known. Of course, these processes become more complex as the molecular entities (the individual components) increase in size and become hetero- geneous; then the shape of the molecule, its symmetry and multifunctionality become important. It is obvious that such biomolecular interactions are not homogeneous, single-point or linear as a function of time.It appears that it is the simple arrangement of the molecular interactions, both temporally and spatially, that results in the complex performance characteristics of biosystems. They are dynamic molecular heterogeneous structures. The beauty and complexity lies in how the nature of that heterogeneity changes with time. The materials required to monitor and control such systems will therefore be required to have excellent temporal and spatial resolution. Conducting polymers with controllable, dynamic properties go some way towards providing these requirements. The authors are grateful to the Australian Research Council for continued financial support.References 1 2 3 4 5 6 7 8 9 10 Polymer Surface Dynamics, ed. Andrade, J. D., Plenum Press, New York, 1988. Kesling, R. E., Synthetic Polymer Membranes, McGraw-Hill, New York, 1971. Mulder, M., Basic Principles of Membrane Technology, Kluwer, Dordrecht, 1991. Inclusion Aspects of Membrane Chemistry, ed. Osa, T., and Atwood, J. L., Kluwer, Dordrecht, 1991. Klein, E., Affinity Membranes, Wiley, New York, 1991. Snyder, L. R., and Kirkland, J. J., Introduction to Modern Liquid Chromatography, Wiley, New York, 1974. Henry, M. P., in High Performance Liquid Chromatography in BioTechnology, ed. Hancock, W. S., Wiley, New York, 1990, Fausnaugh, F. L., Pfannkoch, E., Gupta, S., and Regnier, F. E., Anal. Biochem., 1984,137,464.Kennedy, L. A., Kopaciewicz, W., and Regnier, F. E., J . Chroma- togr., 1986, 359, 73. Schomburg, G., Trends Anal. Chem., 1991, 10, 163. pp. 205-26 1. 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Diagnostic Polymer Sensors, ed. Usmani, A. M., and Ahrmal, N., ACS Symposium Series, No. 556, American Chemical Society, Washington, DC, 1994. Hodgson, A. J., Lewis, T. W., Maxwell, K. M., Spenser, M. J., and Wallace, G. G., J. Liq. Chromatogr., 1990, 13, 3091. Ge, H., Gilmore, K., Ashraf, S., Too, C. O., and Wallace, G. G., J . Liq. Chromatogr., 1993, 16, 7. Mirmosheni, A., Price, W. E., Wallace, G. G., and Zhao, H., J . Intell. Muter. Syst. Struct., 1993, 4, 43. Zhao, H., Price, W.E., and Wallace, G. G., J . Membr. Sci., 1995,100, 239. Ikariyama, Y., Galiatsatos, C., Heineman, W. R., and Yamauchi, W., Sens. Actuators, 1987, 12, 455. Imisides, M., O’Riordan, D. M. T., and Wallace, G. G., in Electrochemistry, Sensors and Analysis, ed. Smyth, M. R., and Vos, J. G., Elsevier, New York, 1989, p. 293. Wallace, G. G., and Yuping, L., J . Electroanal. Chem., 1988, 247, 145. Mao, H., and Pickup, P. G., J . Electroanal. Chem., 1989, 265, 127. Ikariyama, Y., and Heineman, W. R., Anal. Chem., 1987, 58, 1803. Dong, S., and Che, G., Talanta, 1991, 38, 1 I 1. Haimerl, A., and Merz, A., J . Electroanal. Chem., 1987, 220, 55. Lu, W., Zhao, H., and Wallace, G. G., Anal. Chim. Acta, 1995, 315, 27. Foulds, N. C., and Lowe, C. R., Anal. Chem., 1988, 60, 2473.Gamier, F., Youssoufi, H. K., Srivastava, P., and Yassar, A., J . Am. Chem. Soc., 1994, 116, 8813. Cooper, J. M., Morris, D. G., and Ryder, K. S., J . Chem. Soc., Chem. Commun., 1995,697. Yon-Hin, B. F. Y., Smolander, M., Crompton, T., and Lowe, C. R., Anal. Chem., 1993, 65, 2067. Samuelson, L. A., Kaplan, D. L., Lim, J. O., Kamath, M., Marx, K. A., and Tripathy, S. K., Thin Solid Films, 1994, 242, 50. Smith, A. B., and Knowles, C. J., J . Appl. Polym. Sci., 1991, 43, 399. Price, W. E., Wallace, G. G., and Zhao, H., J . Membr. Sci., 1994,87, 47. Talaie, A., and Wallace, G. G., Synth. Met., 1994, 63, 83. Lu, W., and Wallace, G. G., Anal. Chim. Acta, in the press. Lu, W., and Wallace, G. G., Electroanalysis, submitted for publica- tion. Sadik, 0. A., John, M. J., Wallace, G. G., Banett, D., Clarke, C., and Larry, D. G., Analyst, 1994, 119, 1997. Adeloju, S. B., Shaw, S. J., and Wallace, G. G., Anal. Chim. Acta, 1993,281, 621. Palmquist, E., and Kriz, C. B., Biosens. Bioelectron., 1994, 9, 551. Hodgson, A. J., Gilmore, K. J., Small, C., Wallace, G. G., Mackenzie, I., Ogata, N., and Aoki, T., Supramol. Sci., 1994, 1, 77. Hodgson, A. J., John M., Campbell, T., Georgevich, A., Woodhouse, S., Aoki, T., Ogata, N., and Wallace. G. G., SPlE Conf. Smart Muter. Struct., 1996, 2716, 164. Belanger, D., Nadreau, J., and Fortier, G., J . Electroanal. Chem., 1989, 274, 143. Adeloju, S. B., Shaw, S. J., and Wallace, G. G., Electroanalysis, 1994, 6, 865. Adeloju, S. B., Shaw, S. J., and Wallace, G. G., Anal. Chim. Acta, in the press. Kajiya, Y., Matsumoto, H., and Yoneyama, H., J . Electroanal. Chem., 1991,319, 185. Deshpande, M. V., and Hall, E. A. H., Biosens. Bioelectron., 1990,5, 431. Kajiya, Y., Tsuda, R., and Yoneyama, H., J . Electroanal. Chem., 1991,301, 155. Yabuki, S., Shinohara, H., Ikariyama, Y., and Aizawa, M., J . Elec- troanal. Chem., 1990,277, 179. Nishizawa, M., Matsue, T., and Uchida, I., Anal. Chem., 1992, 64, 2642. Paper 6100282J Received January 15, I996 Accepted March 21, I996

ISSN:0003-2654    

DOI:10.1039/AN9962100699

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (705-709) 705 Surface Modification of Thin Film Gold Electrodes for Improved ln Vivo Performance* Mark Hyland, James A. McLaughlin, Dao-Min Zhou and Eric T. McAdams The Northern Ireland BioEngineering Centre, University of Ulster at Jordanstown, Shore Road, Newtownabbey, Co. Antrim, N . Ireland, BT37 OQB Thin film gold electrodes on flexible PTFE substrates are produced for medical implantation. The electrical interface impedance of the electrodes is measured in vitro as a function of surface roughness of the gold as quantified using atomic force microscopy. Alternating current impedance measurements show a linear relationship between the reactive component of the impedance and the surface roughness. Surface features below a few tens of nanometres root mean square deviation from the average surface height are shown to result in a reduction in interface impedance and improved matching to a 1% NaCl solution.The surface roughness of the gold is controlled using an electrolytic etch in an NaCl solution which causes electropitting and therefore allows the electrical characteristics of the interface to be influenced. Keywords: Electrodes; thin-film gold; surface modification; electropitting Introduction Medical sensors serve as an interface between living and electronic systems. If such sensors are to be used as human implants then the characterization of the materials used is crucial. Such materials must be classed as biocompatiblel and the sensor must not affect the properties and behaviour of the living system. Modification of the surface of the material can be used to tailor properties such as interface impedance and interface adhesion by chemical and physical modification of the surface structure.The interface properties of the film can thus be changed without affecting the bulk properties of the material. In this way, for example, it is possible to render surfaces extremely hydrophilic, this being attractive in many circumstances such as infection control. Such treatment can obviate protein adsorption and induce nonthrombicity, making electrode implants more practicable.2.3 Without this treatment biofouling of the elec- trodes can occur within 2 to 3 min of implantation. It is also known that in addition to the electrical characteristics, the shape, mass, or surface roughness of a device can influence its bi~compatibility.~ The surface of an implantable electrode therefore needs to be optimized according to the environment in which it is to be used, and the purpose of the implant.The application specific to this study is the monitoring of disorders of the human nervous system using PTFE-Au subdural electrodes. Specifically, brain activity during strokes or epiletic seizure can be detected using electrical impedance topography. With an array of such sensors, a three dimensional representation of electrical activity can be gained and areas of the brain responsible for such disorders can be identified with high resolution. The Au layer is deposited in thin film form to * Presented at Sensors and Signals 111, Malahide, Co.Dublin, Ireland, October 26-27, 1995. facilitate the microfabrication of uniform electrodes with reproducible properties. The inert nature of both the gold and the PTFE ensures that the electrode-body fluid interface characteristics remain constant and the integrity of the elec- trodes is maintained. The control of the surface properties are also of great importance to chemical and biochemical sensors used outside of the body. Surface area effects and matching of the electrode to biogel materials are crucial for good signal propagation. This work aims to assess the suitability of PTFE-Au films for use as subdural electrodes and how the performance of such devices is affected by surface modification at the PTFE-Au and Au- analyte interface. Experimental Electrode Production PTFE films of 5 x 10-5 m thickness were used as substrates for electron beam evaporation of thin film gold layers.The PTFE is ultrasonically cleaned in Decon 90 solution and rinsed with deionized water prior to treatment. A Leybold Heraeus L560 coating plant with an ESV6 (6KeV) electron gun is used to deposit gold from graphite crucibles. Base pressures of 8 X 10-6 mbar were achieved using a turbomolecular pump. Film thickness is 5 X m, monitored using an oscillating quartz crystal driven at 6 MHz and calibrated against a Taylor Hobson Talystep. Deposition rates of 5 8, s-1 are measured. Plasma modification of the PTFE surface is produced using a 13.56 MHz RIE 80 Plasma Technology System. A range of samples were exposed at various powers, from 8 to 215 W for times ranging from 30 s up to 360 s.Gas flow was controlled with a mass flow controller and spectral grade gas types nitrogen, argon and oxygen bled in at flow rates of 0.6 ml s-l. In all cases, dc bias level was monitored. Base pressures were typically 1 mTorr and the plasma was excited at 50 mTorr. The effect of such treatment on surface composition and function- ality is assessed using a Kratos XSAM 800 X-ray photoelectron spectroscopy (XPS) system. Spectra were recorded using non- monochromated MgKa X-rays (hv = 1253.6 eV) with the anode operating at a power level of 120 W (10 mA emission current, 12 kV applied voltage) to avoid X-ray beam damage during acquisition. The electron energy analyser was operated in the low magnification mode at an electron take off angle of 90".A low energy electron flood source was used while acquiring spectra to compensate for any build up of charge on the surface. Spectra were obtained from pristine PTFE and a series of polymer samples exposed to various nitrogen plasma conditions. The Cls, Fls, 01s and Nls peaks were recorded. The area under each peak, calculated after removal of a suitable background, in this case a Shirley type integral, was used to obtain an estimate of the % atomic concentration of each of the species detected by using an appropriate software routine which includes the required sensitivity factors and accounts for the characteristics of the analyser.706 Analyst, June 1996, Vol. 121 The effect of surface modification on the adhesion between Au and PTFE was investigated using a Quad Group Sebastian five-A z-axis pull tester.The test consisted of bonding of an aluminium stud to the thin metal film and pulling vertically at a constant preset rate, until the film detached from the PTFE. Insulating Si02 layers were electron beam evaporated onto the PTFE-Au film in order to eliminate stray impedance during the measurement of the electrical interface characteristics. The insulator is deposited to thickness of 2 X 10-7 m in exactly the same manner described for the gold deposition. Methods of Gold Electrode Surface Modification The capacitive, resistive and inductive components of the interface impedance at the Au-body fluid interface are determined by the electric field interaction at the surface. The magnitude and direction of such a field will depend upon the local radius of curvature and orientation of the film, i.e., the film morphology. Additionally, an increase in the surface area of a film allows a greater charge transfer, implying that films with rougher surfaces will have a lower interface resistance.Manipulation of the film surface morphology is achieved through the techniques detailed below, and the characteristics of the interface impedance are measured using ac impedance spectroscopy. The interface characteristics can then be related to the surface morphology of the film, determined using atomic force microscopy (AFM). All electrodes investigated have a surface area of 4 cm2 and the measurements are made on the central region of the film after surface modification.(i) Chemical etching A wet chemical etch similar to that reported by Zytez and Despress was used. The standard solution is made from 400 g KI, 100 g I2 and 400 ml of water, the etch rate being reduced by further diluting with water 1 : 20 (0.1 to 0.2 pm min-1). Gold- PTFE electrodes are etched for times varying from 5 to 100 s. The samples are washed in deionized water and dried under nitrogen prior to analysis. (ii) Plasma modification The Au-PTFE film is placed onto the cathode of a Plasma Technology 13.56 MHz parallel plate reactive ion etcher RIE 80. Gas flow was maintained at 10 ml s-1 using a mass flow controller. Base pressures of 5 mTorr were obtained, the working pressure being 90 mTorr. A range of samples were exposed to an Ar plasma for 5-60 s using powers of 10 W and 40 W.(iii) Electrolytic etching The Au-PTFE electrode is used as the anode in an Au-Pt cell using a 0.1 moll-' NaCl solution as the electrolyte. A Thurlby PL320 power supply is used to deliver a 20 mA current for times ranging from 20 to 1600 s. After removal from the electrolyte, samples are washed in deionized water and dried under nitrogen before examination. AC Impedance Measurements The in vitro monitoring of the electrode-body interface impedance is achieved by modelling the system using a 1% NaCl solution as the analyte. The signal is processed using a Solartron 1260 frequency response analyser and a 1286 electrochemical interface under computer control using Zplot software. The frequency range studied is 10 kHz-1 Hz. The exciting signal amplitude is 5 mV rms.The output is compared to that of the equivalent circuit model shown in Fig. 1. This consists of an impedance Zcpa, with a constant phase angle in parallel with a resistance R,, representing both charge transfer across the interface, and Z,, the diffusion impedance. Zcpa represents the double layer capacitance distorted by the surface roughness effects. The double layer capacitance can be replaced by Zcpa = K(jco)-B, where K (Qs-P) is a measure of the magnitude of the non-faradaic impedance. B is a measure of the deviation from purely capacitive behaviour. Both K and p decrease as the interface topography becomes increasingly rough. A small resistance Rtotal is connected in series with the above combination and represents the sum of the resistances of the leads, the electrode and the electrolyte solution. Atomic Force Microscopy (AFM) AFM measures the interaction forces between an Si tip and the sample surface.The atoms at the end of the tip are subject to the short range, mainly repulsive forces such as quantum mechan- ical tunnelling. The atoms further away, however, are subject to attractive forces such as Van der Waals. Consequently, a position can be found where the forces on the whole tip sum to zero and the tip sits in equilibrium. When the tip is scanned over the sample, it is confined to this position and thus follows the surface morphology exactly. A Burleigh SPM ARIS-3300 microscope is used in contact mode. Feature heights are calibrated using a VLSI standards incorporated surface topo- graphy reference STR3-180 with a nominal step height of 18 nm.A statistical analysis program enables the height of each pixel to be displayed in the form of a histogram and the distribution of heights can be seen. In this way, the average surface height is determined and the root mean square deviation (ZmJ from this value is used as an indication of the surface roughness of the sample. The microsurface roughness of such electrodes is known to effect the interface impedance more than that on the macro scale$ consequently, the area for statistical analysis is set at 1 pm2 for all samples in the series. Results and Discussion Modification of PTFE Thin film flexible gold electrodes on PTFE substrates have been produced using the method described. Interface adhesion is enhanced under the action of nitrogen plasma modification of the PTFE as described previously.7 The surface of the PTFE is fluorine depleted as measured by XPS.Calibrated inks are used to measure an increase in the surface energy of the PTFE from 37 to 58 dyne cm-1 (1 dyne = 10-5 N), explaining the increased wettability of the PTFE after plasma treatment. Fig. 2 illustrates the effect of the increase in PTFE surface energy on the Au adhesion. The spread of adhesion strengths measured over ten pull tests are shown for each of the plasma modification techniques. The lower limits can be assumed to be indicative of samples which showed poor contact between the Au surface and the A1 pull stud during the mounting process. Consequently, the measured strengths are lower because of peeling of the Au layer or the application of a higher removal pressure than indicated.The upper limits of all the measurements are therefore taken to be indicative of the Au-PTFE adhesion strengths. Fig. 1 Equivalent circuit model of electrolyte-analyte interface.Analyst, June 1996, Vol. 121 707 AFM The surface morphology of the gold films produced using the above methods are investigated using AFM. The surface roughness is quantified from the Z, values. Fig. 3(a) shows the Au-PTFE (modified) and 3(b) the Au-PTFE electrode mor- phology with no surface treatment of the Au layer. Sample (b) shows a large macroscale roughness compared with ( a ) because of the improved wettability of the Au to the modified PTFE surface. However, for Au-PTFE (modified), Z, = 18.6 X 10-9 m whereas the Au-PTFE/Au electrode shows a surface roughness of Z,, = 5.5 x 10-9 m over a 1 pm2 sample area.The AFM study of the Au surface shows that the energy of the PTFE drastically changes the morphology of the Au epilayer. It can be assumed that the growth mechanisms which occur on the two substrates are quite different, with the unmodified PTFE giving rise to large island growth. The higher surface energy of the modified PTFE facilitates the growth and coalescence of many nucleation sites giving rise to a smoother macrostructure of the Au. AFM images of the Au surfaces produced using methods (i) and (ii) above are shown in Fig. 4. The wet etching of the Au can be seen to produce a surface with poor uniformity and measurements of interface impedance could not be related to the etch conditions.Efforts to pattern the surface of the Au using photolithography in conjunction with a wet chemical etch are 180 'E 160 o, 140 0 $ 120 rn c 100 80 60 $ 40 .- c 2 20 0 t 1 L Untreated Ar plasma 0, plasma N, plasma Fig. 2 Z-axis adhesion of plasma treated Au-PTFE interface. continuing. Method (ii) gave rise to extremely uniform films throughout the entire range of plasma powers and times used. The Au surface is etched uniformly by the Ar plasma as the surface energy of the gold film is constant. Modification of the surface of the Au electrode using method (iii), above, gave rise to the Au surface morphologies with evidence of electropitting. Treated Au surfaces are shown in Fig. 5(a) 100 s etch at 20 mA current, and 5(b) 300 s at 20 mA Fig.4 plasma etch after a 40 W, 20 s exposure. AFM of Au surface after (a) chemical etch for 5 s and (h) after Fig. 3 morphology. (a) AFM of Au-PTFE (modified) morphology and (6) Au-PTFE Fig. 5 (b) 300 s at 20 mA. AFM of Au surface after electrolytic etch for ( a ) 100 s at 20 mA and708 Analyst, June 1996, Vol. 121 current. The morphology of the untreated Au surface is shown in Fig. 3(a). Ten 1 pm2 areas are investigated using AFM for each sample. The mean Z, and standard deviation for all of the gold modification techniques over the range of conditions investigated is shown in Table 1. The etch profile produced using the electropitting process is shown in Fig. 6; it can be seen that the Z,, of the Au surface q ; 4 hL 15 10 5 0 0 200 400 600 800 1000 1200 1400 1600 Etch time/s Fig.6 Z,, of electrolytically etched sample versus etch time. -5000 - -4000 c -3000 2 -2000 -1000 0 0 1250 2500 RsIR Fig. 7 Impedance of (a) Au-PTFE and (b) Au-PTFE (modified) electrodes in 1% NaCl solution. %:; U- 10 5 0 100 200 300 Etch time/s Fig. 8 Bulk resistance of Au layer versus electrolytic etch time. first decreases and then increases. This behaviour can be explained by postulating that the electrolytic etch preferentially removes any surface features at the start of the etch. As the etch continues at a constant rate, the gold is etched preferentially in areas where the applied field will be locally different from the rest of the surface such as along grain boundaries and surface defects.AC Impedance Measurements Fig. 7 illustrates the change in interface impedance between (a) Au-PTFE and (b) Au-PTFE (modified) electrodes in a 1% NaCl solution. The interface impedance is lower for sample (b) because the surface roughness of the gold over a I pm2 area is higher than for the unmodified sample. Consequently, the interface imped- ance can be said to be more heavily dependent upon the microscale roughness of the electrode. The bulk resistance was monitored for each electrolytically etched sample and plotted in Fig. 8. The rise in bulk resistance of the electrode indicates a constant reduction in film thickness with etch time. The non-Faradaic component of the interface impedance of each of the etched samples is shown in Fig. 9 against the measured Z,, of the sample.The graph was fitted to a straight line with a gradient of -0.66 using a least squares fit. The interface impedance can be extrapolated to zero at a surface roughness of 42.4 k 1.2 X m. Surface features of a few tens of namometres can therefore be said to constitute the crossover between micro- and macro-surface roughness. Feature heights of around a few tens of nanometres will give rise to films with higher interface impedance as the Helmholtz double layer capacitance of the film is changed. Features below this height, despite the improved charge transfer effects, will suffer from increased interface impedance due to a loss in surface area of the electrode and a consequential reduction in charge collection. 5 10 15 20 25 30 35 =rms/nm Fig.9 roughness of Au electrode. Non-Faradaic component of interface impedance versus surface Table 1 Measured root mean square deviation (Z,,) of modified gold surfaces Plasma etch Chemical etch 40 W 10 w Electrolytic etch Time/s Zms/nrn Time/s Zms/nm Time/s Z,Jnm 0 18.6 f 0.9 0 18.6 f 0.9 0 18.6 f 0.9 5 42.2 f 11.8 5 4.3 f 0.1 5 6.1 f 0.5 10 18.3 f 4.5 20 5.8 f 0.4 20 5.8 i 0.3 50 37.0 f 5.6 30 5.2 f 0.5 30 7.2 f 0.3 100 25.3 f 5.6 60 5.3 k 0.6 60 6.5 i 0.5 Time/s Z,Jnm 0 18.6 f 0.9 50 8.6 f 0.6 100 3.8 k 0.6 300 4.5 f 0.6 600 10.7 k 0.7 1600 33.0 f 0.9Analyst, June 1996, Vol. 121 709 Conclusion Surface modification of the PTFE film in a nitrogen plasma prior to metallization results in improved adhesion of Au electrodes. The deposited Au films possess a smooth macro- morphology.The surface roughness of the Au electrodes can be controlled using the electropitting process. This etching technique pro- duces a constant reduction in film thickness and a surface roughening after an initial smoothing off of surface features. The interface impedance monitored during this process in- dicates that it will reach its minimum value when the surface has feature heights of around a few tens of nanometres. Typically, the desired interface characteristics of an electrode are depend- ent upon the in vivo location and whether the location is to be sensed or stimulated. This method of electrode production can produce thin flexible films which can be patterned to any desired electrode shape with the desired interface impedance and biocompatibility. This fundamental study has applications in the areas of bio and chemical sensors where the electrode-electrolyte interface can be better controlled. Improved reproducibility, sensitivity, and selectivity are all advantages associated with thin film sensor fabrication as opposed to traditional screen printing techniques. The authors would like to thank the Surface Science Laboratory, University of Ulster at Coleraine, for access to XPS equip- ment. References Williams, D. F., J. Biomed. Eng., 1989, 11, 185. Hayward, J. A., and Chapman, D., Biomaterials, 1984, 5, 135. Ishira, K., Ueda, T., and Nakabayishi, N., Polym. J. (Tokyo), 1990, 22, 355. McAdams, E. T., McLaughlin, J. A., and Holder, D. S., in Neurosensors: a review of some of the fundamental electrode parameters, 1992, pp. 226-234. Zytez, M. C., and Despres, A. M., in Extended Abstracts of the 13th AVS Symposium, Herbick and Held printing Co., Pittsburgh, PA, USA, 1966, p. 169. Zhou, D. M., McAdams, E. T., Hyland, M., and McLaughlin, J. A., Analyst, unpublished work. McLaughlin, J. A., Macken, D., Meenan, B. J., McAdams, E. T., Maguire, P. D., Key Eng. Muter., 1995, 99-100, 331. Paper 5/08344C Received December 22, 1995 Accepted April 3, 1996

ISSN:0003-2654    

DOI:10.1039/AN9962100705

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (711-714) 71 1 Moisture-activated, Electrically Conducting Bioadhesive Interfaces for Biomedical Sensor Applications* A. David Woolfson Biomedical and Environmental Sensor Technology Centre, School of Pharmacy, Queen's University of Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast, Northern Ireland, UK BT9 7BL An electrically conducting interface for biomedical sensors, formed by casting and drying a polymer blend containing poly(methy1 vinyl ether-maleic anhydride), glycerin as plasticizer, polyvinylpyrrolidone as viscosity builder and sodium chloride as the conducting electrolyte, is described. The resulting flexible bioadhesive film can adhere to a biological substrate under adverse conditions such as high humidity or when the biological substrate is immersed in water or a physiological fluid.The technology allows the design, for example, of an electrode for the monitoring of foetal heart rate and other physiological and analytical sensors. The effect on interface bioadhesion of varying both the composition of the polymer blend and the ambient moisture conditions on storage were investigated in respect of bioadhesion to a model substrate (wet neonate porcine skin) and the mechanical behaviour of the interface. Optimum bioadhesive performance was obtained from films formulated from blends (pH 5 ) containing copolymer-plasticizer (2 + 1) and 5% m/m polyvinylpyrrolidone. Moisture conditions on storage had a significant effect on both the subsequent bioadhesive performance and the mechanical properties of the stored films.Formulation and hydration interactions were identified using 2 X 2 factorial design experiments. Data analysis was by a two-way ANOVA with repeated measures. Keywords: Poly(methy1 vinyl ether-maleic anhydride); bioadhesion; hydrogel; biomedical sensor; elasticity Introduction The process of adhesion occurs at the interface between two solid phases and an adhesive layer, hindering the separation of the two adhering phases. Adhesion is referred to as bioadhesion when at least one adhering phase is biological in nature. Of particular interest is Type 3 bioadhesion, in which an artificial material, typically polymeric, adheres to a biological substrate. Type 3 bioadhesion has been used increasingly in the develop- ment of novel drug delivery systems2 but also has important implications for the design of electrically conducting, water- activated interfaces for biomedical sensors.The use of polymeric, electrically conducting, bioadhesive hydrogel films, cast on to Ag/AgCl inks screen-printed on suitable substrates, can enhance the flexibility and conform- ability of the resultant biomedical sensor. An example of the use of this technology is the design of a novel foetal scalp ele~trode.~ The bioadhesive hydrogel forms a tack-free ad- * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. hesive film. Hence there is no requirement for an additional pressure-sensitive adhesive surround. Since the adhesive is moisture activated, it adheres to biological substrates under adverse conditions such as high humidity, where excessive perspiration can lead to the failure of conventional pressure- sensitive adhesive bonds.The bioadhesive interface can provide secure attachment of a sensor even when the biological substrate is immersed in water or a physiological fluid, e.g., in the non-invasive monitoring of foetal heart rate.3 Moisture-activated bioadhesive films derived from a copoly- mer of methyl vinyl ether and maleic anhydride (PMVE-MA) and doped with a suitable electrolyte have proved of particular interest as electrically conducting interfaces for biosensors.3 It is of importance to evaluate fully the bioadhesive performance of such films with a moist biological substrate in order to ensure reliable attachment of the bioadhesive biosensor.Hence in this work, factorial design experiments were used to highlight interactions between formulation components with respect to film bioadhesion in vitro to neonate porcine skin. Hydration effects4 on bioadhesive performance and on film mechanical properties were also studied in order to design an optimum bioadhesive sensor interface. Experimental Materials Polyvinylpyrrolidone (PVP) (Kollidon 90, USP grade) was obtained from BASF (Ludwigshafen, Germany). Gantrez AN-139, a PMVE-MA copolymer, was provided by ISP (Manchester, UK). All other chemicals were of analytical- reagent grade. Preparation of Bioadhesive Films The required mass of PMVE-MA was added to water maintained between 95 and 100 "C and the mixture was stirred vigorously until a clear solution was formed.Excipients were added sequentially as required and the blend mass was adjusted to its final value with water. Bioadhesive films were prepared by knife-casting the blend on to a polyester-lined glass plate surrounded by a PVC barrier, followed by air drying for 24 h at ambient temperature to produce clear hydrogel films. In Vitro Adhesion Measurements The adhesion of all films was quantitatively evaluated using a bioadhesion tester based on a linear variable displacement transformer (LVDT).5 Full-thickness, hairy porcine skin, thoroughly wetted by immersion in water for 10 s, was attached with cyanoacrylate adhesive to an upper pedestal linked to the LVDT via a sensor. The film ( 1 cm2) was attached with double- sided adhesive tape to a lower, moveable platform that was712 Analyst, June 1996, Vol.121 driven by a stepping motor. The test procedure was as described previously.5 Skin Model Full-thickness neonate porcine skin was used as a model for human skin. Excess subcutaneous fat was removed and the skin samples were washed in distilled water, placed between layers of aluminium foil and stored at - 18 "C until required. Viscosity Measurements All viscosity measurements were carried out using a Brookfield Model LVT viscometer, (Brookfield Engineering Laboratories, Stoughton, MA, USA). All measurements were carried out in triplicate at room temperature using an appropriate spindle size. Film Hydration Clear, flexible PMVE-MA hydrophilic polymeric films of surface area approximately 600 mm2 and thickness approxi- mately 0.2 mm were produced on a non-woven polyester substrate, thickness 50 pm.All films tested were freshly cast and were initially conditioned for a 4 h equilibration period in an atmosphere of 52% relative humidity at ambient temperature before being transferred to storage at the required constant relative humidity. A freshly cast film was placed on the weigh-pan extension of an Oertling Model NA264 balance. The balance was interfaced with an IBM XT computer with custom software for continuous mass recording. Temperature and humidity were also con- tinuously monitored (Psion Model LZ64 Organizer Datalogger, with Model R5SF humidity probe and Model THSF tem- perature probe). Relative humidities were altered by using an appropriate saturated salt solution in the tray of the humidity cabinet (LTE, Oldham, UK).Mass changes were recorded for four replicate film samples for each humidity over a 24 h period at 23 k 1.5 and 35 k 1 .5 "C. The equilibrium water contents of the films were determined according to the equation equilibrium water content of film = wet mass of film - dry mass of film wet mass of film (1) Determination of Film Elasticity The elastic modulus and elongation at break of films stored at various relative humidities for 7 d were determined using a Model TA-XT2 texture analyser (Stable Micro Systems, Haslemere, UK). The sample was clamped using an upper and lower flat-faced metal grip laminated with smooth rubber. The distance between the grips was set at 30 mm and this distance therefore represented the length of the film under stress. A cross-head speed of 2 mm s-1 (strain rate) was used for all measurements.The resultant force-time profiles were analysed using the on-board software (Dimension 3.7E). Only the results for films which were observed to break in the middle during testing were included in the results. The mean of five replicates was taken for each film. Statistical Analysis ANOVA (two-way with repeated measures) was performed with Minitab and the Newman-Kuels Multiple Range Test6 with Kwikstat, both PC versions. Curve fitting was performed with Minim software (Apple Macintosh version). Results and Discussion Effect of Formulation Components on Interface Bioadhesion Porcine skin samples, as a model substrate,7 were thoroughly hydrated before use and excess water was removed prior to the determination of adhesive strength.Films were thus applied in the dry state directly to the wet substrate and adhered immediately upon hydration. The copolymer concentration in the aqueous blend was directly related to the bioadhesive performance of the resulting cast film (Table 1). Films cast from blends (pH 5) containing 10% m/m copolymer were significantly more adhesive ( P < 0.001) than those formed from lower (6 and 8% m/m) PMVE- MA blend concentrations. The plasticizer concentration did not significantly ( P > 0.05) influence the bioadhesive properties of the films. However, inclusion of glycerol as a plasticizer is necessary with PMVE-MA owing to the high glass transition temperature of the latter.* In clinical applications, it is often necessary to reposition a physiological sensor following its initial application.Films cast from blends (pH 5) containing 10% m/m PMVE-MA and 5% m/m plasticizer exhibited sufficient adhesion to permit this (Table 2), although adhesion was reduced with each successive substrate removal-adhesion cycle. This is an important property of the bioadhesive interface compared with conventional hydrogel interfaces that require re-application at the new site. Blends formulated from aqueous solutions of PMVE-MA plasticized with glycerol had pH = 2 and therefore exhibited extremely low viscosities ( < 250 cP). By forming the disodium salt of PMVE-MA, the Brookfield viscosity could be increased to a maximum value of approximately 1200 CP at about pH 5.At low pH values, PMVE-MA films exhibited consistently lower adhesive properties in vitro, probably owing to thinner films being cast from the lower viscosity blend. Films cast from very low viscosity blends tended to be extremely thin and were therefore difficult to handle during manufacture. A bioadhesive film interface gradually dissolves when in contact with water. However, the interface can be made more durable by casting a thicker film from a higher viscosity blend formed through the use of a viscosity builder such as PVP. Thus, PMVE-MA blends containing PVP demonstrated significant increases in their viscosities. For a blend containing 5% PVP in Table 1 In vitro bioadhesion to wet neonate porcine skin of films cast from PMVE-MA aqueous blends (pH 5 ) plasticized with glycerin Adhesion f SIB Glycerol 6% m/m 8% m/m 10% m/m 2 0.361 k 0.034 0.355 f 0.050 Too brittle 4 0.530 & 0.049 0.470 & 0.066 1.616 k 0.269 6 0.31 1 f 0.025 0.423 f 0.025 1,570 k 0.122 8 0.244 f 0.045 0.464 f 0.037 1.766 k 0.204 10 0.182 k 0.008 0.48 1 k 0.022 1.197 k 0.049 (% w/w> PMVE-MA PMVE-MA PMVE-MA Table 2 In vitro bioadhesion to wet neonate porcine skin of films cast from 10% m/m PMVE-MA aqueous blends (pH 5 ) plasticized with glycerin (5% m/m), initially and on first and second removal-adhesion cycles Adhesion following removal- adhesion cycles & s/N Initial adhesion * S / N First cycle Second cycle 1.917 k 0.352 1.577 _+ 0.294 1.167 k 0.296Analyst, June 1996, Vol.121 713 combination with PMVE-MA (15% m/m) and glycerol (7.5% m/m), the Brookfield viscosity was 370000 cP.For all concentrations of PVP, an increase in the copolymer concentra- tion from 10 to 15% m/m (with the copolymer-to-plasticizer proportions maintained at 2 + 1) resulted in an increase in film adhesion. Thus, for a blend containing 5% PVP in combination with PMVE-MA (15% m/m) and glycerol (7.5% m/m), the bioadhesion of the resulting cast film to wet neonate porcine skin was 3.040 +_ 0.415 N compared with 1.597 If: 0.303 N for a film produced from a blend of 5% PVP in combination with PMVE-MA (10% m/m) and glycerol (5% m/m). A 2 X 2 factorial experiment, involving two formulation components at two different concentrations, was used to study possible interactive formulation effects.The components were PMVE-MA (10 and 15% m/m) and PVP (1 and 5% m/m). A two-way ANOVA with repeated measures demonstrated that an increase in the copolymer concentration of the blend, from 10 to 15% m/m, significantly increased the mean in vitro bioadhesion forces for the films ( P < 0.001). An increase in the PVP concentration of the blend, from 1 to 5% m/m, also significantly enhanced in vitro film bioadhesion. The combination of an increase in both the copolymer and PVP concentrations in the blends was shown to exert a significant effect on the in vitro bioadhesion of the resultant films. This interactive effect may have been partly direct, owing to a physical interaction between the copolymer and PVP, and partly indirect, owing to the higher concentration of PMVE-MA present in a film cast from a higher viscosity PMVE-MA-PVP blend.The effect on film adhesion of increasing the blend copolymer concentration from 10 to 15% m/m in the presence of 5 and 7.5% m/m glycerol, respectively, was investigated using a 2 X 2 factorial design and a two-way ANOVA with repeated measures. Increasing the copolymer concentration in the blend again resulted in a significant increase in film bioadhesion (P < 0.001 ), whereas increasing the glycerol concentration from 5 to 7.5% m/m had no significant effect ( P = 0.29). There was no statistically significant interaction between polymer and plasticizer. Sodium chloride was added in various concentrations (0.01-0.2% m/m) to a blend of PMVE-MA (15% m/m), PVP (5% m/m) and glycerol (7.5% m/m) in order to render the cast films electrically conductive.The concentration of sodium chloride present in the casting blend had no significant effect (P = 0.13) on the adhesion of the resulting cast film interfaces. This is an essential property of the system, allowing secure attachment of the electrically conducting film interface to the wet substrate. Effect of Film Hydration on Interface Bioadhesion and Flexibility A film mass versus time profile was obtained using a continuous gravimetric sytem. At low humidities, the initial rapid mass loss was succeeded by a steady-state mass at equilibrium, achieved between 21 and 42 h from the start of the storage period. Conversely, film storage at higher humidities resulted in an initial rapid mass gain, again succeeded by a steady-state mass at equilibrium. A linear (r2 = 0.986) relationship was found between the percentage change in mass and percentage relative humidity for films stored at 23 k 1.5 "C for 7 d at various humidities: mass change (96) = 0.288 (% relative humidity) - 12.239 (2) At zero relative humidity, the decrease in film mass was 12.239% of its initial value, assuming that all the films cast initially had the same water content.The theoretical mass of a film at zero relative humidity represented its theoretical dry mass [eqn. (I)]. The film equilibrium water content at various humidities was calculated from eqn. (1) using the mean wet film mass at the steady state recorded for each film between 42 and 58 h. The relationship between film equilibrium water content and percentage relative humidity was linear (r2 = 0.98): equilibrium water content = 0.242 (% relative humidity) + 1.634 (3) The values for the film equilibrium water contents were corrected to allow for the mean water uptake during the bioadhesion testing procedure.The relationship between bio- adhesion and the corrected equilibrium water content of bioadhesive polymeric films was best described by second- order polynomial equations following storage for 7 d at 23 +_ 1.5 "C (r2 = 0.946) and 35 2 1.5 "C (r2 = 0.988). Hence there was an optimum film water content for maximum in vitro bioadhesion, in agreement with the wet adhesion theory of Chen and Cyr.9 This theory envisages bioadhesion as a dynamic process in which polymer chains are released from their restraining dry lattice forces by the action of hydration.The chains then interpenetrate with the substrate matrix. Maximum adhesive strength is attained when ideal matching occurs between the active adhesive sites on the polymeric chains and the substrate, increasing the magnitude of short-range dis- persive forces. the entire process is hydration dependent. Overhydration results in the formation of a wet, slippery mucilage with little or no adhesive strength. Bioadhesion increased with increasing film water content up to a maximum adhesion of around 1.5 N at about 20% m/m water content. This adhesion was much lower than the adhesion of films stored at room temperature, where the maximum bioadhesion was around 2.5 N for films with a water content around 15% m/m.The effect of storage temperature (23 and 35°C) at two humidity levels (30 and 90% RH) on the bioadhesion of films stored for 7 d was investigated using a 2 X 2 factorial design with a two-way ANOVA (repeated measures, n = 8). Increasing the ambient storage temperature from 23 1.5 to 35 k 1.5 "C significantly decreased the bioadhesion at both humidity levels ( P < 0.001). Bioadhesion forces for films stored at 30 and 90% RH at 35 "C were not statistically different from each other at the 95% confidence limits (Newman-Keuls multiple range test). At higher storage temperatures, humidity did not exert such a critical effect on film bioadhesion. However, the film bioadhesion was significantly influenced by the combined effects of temperature and humidity Increased film storage temperatures at lower humidities were shown to decrease film bioadhesion significantly.Under these conditions, increased cross-linking of polymer chains is favoured. When subsequently hydrated, polymer chains would no longer be free to uncurl and present their active sites for bioadhesion. Hence it was observed that, for films stored at 35°C and 20% RH for 7 d, there was a complete loss of bioadhesion. For bioadhesive electrode interfaces, it is important to determine the effect of moisture content on the elasticity or flexibility of films, as this will affect the conformability of such films to body contours. Film flexibility can be quantified by measuring the elastic modulus or Young's modulus, the ratio of applied stress and corresponding strain in the region of approximately linear deformation.The lower the ratio, the more elastic and flexible is the film. Fig. 1 shows that, as the film water content increased, the computed elastic modulus de- creased exponentially. Water contents greater than 1 1 % m/m resulted in extremely flexible films. An increased water content, above this apparently critical point, plasticized the polymeric network, resulting in the uncurling of polymer chains, which could then bend and flex more readily. This produced a more ductile polymeric film that should conform more readily to body contours when the bioadhesive sensor is used in vivo. ( P < 0.001).714 Analyst, June 1996, Vol. 121 Table 3 relates the water content of films to their percentage elongation at break, calculated as the increase in film sample length at the moment of break expressed as a percentage of its original length.This parameter, which reflects the plasticizing iij ” 10000 10081 28.703 x 10 -0.086X f 2 = 0.948 I 1 I I 1 0 7.684 13.734 19.657 25.834 water content (yo) Fig. 1 Relationship between the elastic modulus of stored bioadhesive films and the relative humidity and corresponding water content on storage over a 7 d period. Table 3 Relationship between percentage elongation at break and water content for bioadhesive films stored at various relative humidities Water content RH (%) (% m/m) Elongation at break k s (5%) 20 6.47 2.81 k 0.61 30 8.89 30.97 f 2.24 40 11.31 462.67 k 57.27 57 15.43 652.37 k 43.64 66 17.6 1 646.68 f 51.59 10000 I T 2ooo: 0 0 25 50 75 100 RH (“10) I I 1 I J o 7.684 13.734 19.657 25.834 water content ( O h ) Fig.2 Relationship between the work of failure of stored bioadhesive films and the relative humidity and corresponding water content on storage over a 7 d period. nature of water in bioadhesive polymeric films, gives a measure of the ability of films to stretch and thus reflects the nature of the chains in the polymeric network. Films with water contents below 10% m/m were inflexible and unstretchable whereas those above 10% m/m showed a dramatic change in the percentage elongation values. Increased hydration allows untangling of chains and a degree of stretching prior to break. Another film mechanical parameter that may be assessed is the tensile energy at break, sometimes referred to as the work of failure.This is calculated by integrating the area under the force-time curve and is a measure of the energy required to break the bonds in the film polymeric network. It can be regarded as a method of assessing film mechanical strength. Fig. 2 shows the results obtained. At the lowest water content tested (6% m/m), the energy required to break the film is low because films are ‘brittle’ and glassy. At intermediate water contents, the energy required to break the film increases. Initial plasticization of the film polymeric network due to the presence of more water increases elasticity and therefore more energy is required to disrupt the film structure. At a water content greater than 10% m/m, the energy required to break the film decreases again as the film films become ‘over-hydrated’.Thus, as the cross-linked network structure in the polymeric system is weakened, the elasticity decreases so that less energy is required to break these bonds. Conclusions The results of this study clearly demonstrate the importance of temperature and humidity in respect of storage conditions for sensors fabricated using bioadhesive, electrically conducting interfaces. In particular, the use of moisture-controlling pack- aging is essential if optimum performance is to be obtained. Further evaluation of the system under controlled storage conditions of temperature, humidity and packaging will there- fore be required in respect of the maintenance of mechanical and bioadhesive properties of the cast films. In vivo validation of in vitro stability data will also be necessary. References 1 2 3 4 5 6 7 8 9 Park, K., Cooper, S . L. and Robinson, J. R., in Hydogels in Medicine and Pharmacy, ed. Peppas, N. A., CRC Press, Boca Raton, FL, 1987, Duchene, D., Touchard, F., and Peppas, N. A., Drug Dev. Znd. Pharm., 1988, 14,283. Woolfson, A. D., McCafferty, D. F., Anderson, J., McAdams, E., and McLaughlin, J., Zr. Pat. Appl., No. 930103, 1993. Smart, J. D., Kellaway, I. W., and Worthington, H. E., J . Pharm. Pharmacol., 1984, 36, 295. Woolfson, A. D., McCafferty, D. F., Gorman, S . P., McCarron, P. A., and Price, J., Int. J. Pharm., 1992, 84, 69. Zar, J. H., Biosratistical Analysis, Prentice-Hall, Englewood Cliffs, Montagne, W., and Yun, J. S . , J. Invest. Dermatol., 1961, 43, 11. Chung, K. H., Wu, C. S . , and Malawer, E. J. G., J. Appl. Polym. Sci., 1990,41, 793. Chen, J. L., and Cyr, G. H., Adhesion in Biological Systems, Academic Press, New York, 1970. V O ~ . 111, pp. 151-175. NJ, 1974, pp. 151-155. Paper 5107970E Received December 7,1995 Accepted January 30, I996

ISSN:0003-2654    

DOI:10.1039/AN9962100711

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (715-731) 715 ReactionlDiff usion With Nlichaelis-Menten Kinetics in Electroactive Polymer Films Part 1 The Steady-state Amperometric Response* Michael E. G. Lyonsa, James C. Greercl, Catherine A. Fitzgeralda, Thomas Bannona and Philip N. Barlettb a Physical Chemistry Laboratory, University of Dublin, Trinity College, Dublin 2 , Ireland b Department of Chemistry, University of Southampton, Highfield, Southampton, UK SO9 5NH The theoretical analysis of the steady-state amperometric response for a polymer-modified electrode system which exhibits Michaelis-Menten kinetics is discussed. In particular, the interplay between substrate diffusion within the polymer matrix and substrate reaction at the catalytic polymer sites is examined. A non-linear reaction/diffusion equation describing the substrate transport and reaction kinetics within the film is formulated and approximate analytical solutions for the substrate concentration profiles and corresponding current responses are developed.Four distinct limiting cases are developed and are represented schematically in a kinetic case diagram. The theoretical analysis is extended to consider the complicating situation of substrate diffusion in the Nernst diffusion layer adjacent to the polymer film. The allied problem of the response of a potentiometric sensor exhibiting Michaelis-Menten kinetics is also examined. The theoretical model developed in the paper is validated by examining the electro-oxidation of dopamine, adrenaline and noradrenaline at surfactant-doped polypyrrole-modified electrodes.Good agreement between the amperometric current response predicted from the theoretical model and the current response obtained experimentally from batch amperometry is obtained. Non-linear least-squares analysis of the batch amperometric data in tandem with the theoretical expression derived for the steady-state current response produces reasonable values for the Michaelis constant, K,, the catalytic rate constant, k,, and the substrate diffusion coefficient, D,, for each of the three catecholamine substrates examined. Keywords: Polymer-modified electrodes; Michaelis-Menten kinetics; reactionldiffusion theory; catecholamine oxidation; electroactive polymer films Introduction Considerable advances have been made during the last decade in the development of polymer-based materials for use as electrocatalysts and as chemical and biological sensors operat- ing in the batch amperometric mode. Useful summaries of recent advances in this area have been provided by Hillman,’ Lyons,24 Evans,s Wring and Hart6 and Murray.7 The analytical applications of polymer-based sensors have been surveyed in a monograph edited by Edelman and Wang.8 Presented at Sensors and Signals 111, Malahide, Co.Dublin, Ireland, 26-27 October 1995. Polymer-based chemically modified electrodes are usually fabricated as thin films deposited (via drop or spin coating or more usually via electropolymerization) on to an inert con- ductive support surface to form a three-dimensional conductive microstructure. The implications of the three dimensionality are obvious.A surface-deposited microstructure containing ap- proximately 10 nmol cm-2 of redox active sites localized in a layer 1 pm thick corresponds to a sensing element concentration of 0.1 mol dm-3. The operation of a chemically modified sensor under amperometric conditions is simple in concept: the substrate reacts with catalytically active sites immobilized in the film rather than at the underlying support electrode. Conse- quently, the redox chemistry of the substrate-product trans- formation is governed by the redox properties of the deposited layer. The immobilized redox sites mediate the electron transfer between substrate and electrode. This process is termed heterogeneous redox catalysisg.10 and is cyclic, as the catalyst/ mediator species in the film may be re-formed as a result of electron transfer at the electrodehayer interface.A question immediately arises as to the manner of the interaction between the immobilized catalytic/sensor sites and the substrate. Little emphasis has been placed on this problem in much of the work reported in the literature to date. Notable exceptions are found in papers by Gorton and co-workers,l1-13 the analysis reported by Andrieux and Saveant,14 which considered ‘ pre-activation’ CE type mechanisms at redox polymer modified electrodes, and a paper by Burke and O’Leary.15 In a number of recent papers,l”lg we and oth- ers,21*21 have provided experimental data which may suggest that the substrate binds to sites in the polymer film to form a complex, which subsequently decomposes to form product.Our work has shown that in many instances the steady-state amperometric current response exhibits biphasic behaviour with respect to substrate concentration. At low values of concentration, the reaction exhibits first-order kinetics, whereas for high substrate concentrations, the response is independent of the latter and zero-order kinetics pertain. This type of observation is well established in enzyme kinetics where it is described in terms of the Michaelis-Menten mechanism.22 Indeed, the concept has been extended to the area of amperometric enzyme electrodes by a number of workers during the last few years.23-26 Our previous work has been concerned with conductive metal oxide (e.g., RuO2) particles immobilized in Nafion18 and carbon paste matrices,27 and with electronically conductive polymers such as polypyrrole doped with counter-ions such as C1- or DBS- (dodecylbenzenesulfo- nate).28 The oxidation of substrates such as ascorbate, glucose and catechol all exhibited biphasic current-concentration profiles.The systems examined do not contain redox enzymes,716 Analyst, June 1996, Vol. 121 yet the behaviour observed is consistent with a Michaelis- Menten-type mechanism. Further, some years ago Burke and O’LearylS proposed that organic electro-oxidation processes mediated by hydrated metallic adatoms at sub-monolayer coverage proceed via a Michaelis-Menten-type process. It therefore appears that a tight binding-type Michaelis-Menten mechanism has some general degree of validity, at least for the materials examined to date.Clearly, in order to be able to approach the design of polymer-based amperometric chemical sensor devices in a logical and rational way, it is necessary to understand the underlying physical processes that come into play during device operation. This, in general, is a complex problem. However, significant progress may be made if one can develop simple mathematical models, based on the construction of suitable differential equations describing the coupled diffusion and chemical reaction processes, along with associated initial and boundary conditions which are physically reasonable. If the (usually time-dependent) boundary value problem can be solved, the current response may eventually be obtained in an analytical form.In many instances, one is dealing with time- dependent diffusion/reaction in a finite region. Further, the chemical reaction term may be of a complex form, as pertains for the case of Michaelis-Menten kinetics. Hence, the differ- ential equation governing the operation of the device may be non-linear and time dependent. In such a situation a complete solution may not be possible, and one must be content with the generation of approximate analytical solutions valid under specific circumstances. Of course, a complementary approach is to solve the differential equations using digital simulation methods.29.30 This approach affords an exact solution and, further, enables one to check the validity of the approximate analytical solutions. In this series of papers we examine the problem of coupled diffusion and chemical reaction in a finite region (a thin polymer film) under both steady-state and transient conditions, in which the chemical reaction term is described by a Michaelis-Menten equation.In this first paper of the series we examine the steady-state current response. The second paper discusses the more complicated problem of the transient current response. In a third paper, we shall consider the allied problem of pre-activation mechanisms, where a finite redox conductivity within the layer must be considered. Boundary Value Problem In this paper (and in Part 2) we consider a thin electrocatalytic- ally active film deposited on the surface of a conductive support to form a chemically modified electrode.We assume that the layer is of a uniform thickness L. We further assume that the catalytically active sites in the polymer film are uniformly dispersed and that the polymer film is electronically conductive so that charge percolation from site to site through the polymer matrix is not a rate-determining factor. The problem of finite redox conductivity is discussed in Part 3. Further, we initially neglect concentration polarization effects of substrate in solution. Hence the only factors being considered initially are those arising from substrate diffusion and chemical reaction within the polymer film. In particular, we assume that the chemical reaction of the substrate is not of a simple form. The substrate is considered to form a complex with the immobilized catalyst which subsequently decomposes to form a product.This two-step reaction sequence may be written as KM kC s + c -[SC]-P + C’ kk C‘-C respectively. This reaction sequence is the well known Michaelis-Menten tight binding mechanism, and KM and k, denote the Michaelis constant and catalytic rate constant, respectively. The differential equation quantifying the transport and kinetics within the polymer film may be written as a q x , t ) kcczs(x,t) = Ds- - (1) as(x,t> at ax2 KM + s(x,t) where s(x,t) denotes the concentration of substrate at any distance x in the film at any time t, Ds is the diffusion coefficient of substrate in the layer and cz denotes the total catalyst concentration in the film. This is a time-dependent non-linear partial differential equation. The non-linearity arises from the presence of the Michaelis-Menten kinetic term on the right- hand side of eqn.(1). We can write eqn. (1) in another way. Introducing a pseudo- first-order rate constant k = k,cdKM, we note that = D s - - - ( 2 ) KM aZs ks as at ax2 1 + s - This equation must be solved subject to the initial condition s = 0 when t = 0. Further, the solution of eqn. (2) must also satisfy the following boundary conditions: for t > 0, then when x = 0, as/& = 0; and when x = L, s = K S ~ , where K denotes the partition coefficient and sm represents the bulk concentration of substrate in solution. The latter boundary condition implicitly assumes that substrate diffusion effects in solution may be neglected. We shall extend the analysis and examine such effects later in the paper.To proceed further, we make the non-linear PDE outlined in eqn. (2) dimensionless by introducing the following parame- ters: where u, X and ‘t represent dimensionless concentration, distance and time parameters, respectively, a denotes a saturation parameter and y is related to the Thiele modulus @. The latter quantity is given by @ = L/XK, where XK denotes a characteristic reaction layer thickness. The Thiele modulus31 quantifies the fraction of the layer that is utilized in the catalytic reaction, since the characteristic reaction layer thickness XK provides an estimate of the distance travelled in the layer by the substrate before it interacts with the immobilized catalytic sites. One may show that eqn. (2) reduces to the following dimensionless form: au a2u yu aT a x 2 1 +au - ---- - whereas the initial and boundary conditions reduce to t = O 0 5 x 5 1 u = o (4) ( 5 ) t > o X = l u = l Hence the problem is defined totally in terms of eqns.(4) and (5). We wish to obtain an analytical expression for the concentration profile u(X,t) of substrate within the layer. From this concentration profile one can then obtain an analytical expression for the normalized current response y as follows. The fluxj is given by where C and C’ represent the catalytically active forms of the immobilized catalyst and S and P denote substrate and product,Analyst, June 1996, Vol. 121 717 Hence the normalized current response y is then given by 1 .- iL nFAKMDs = ay u(X,t)dX Y = 0 (7) where i denotes the time-dependent amperometric current response.Physically, the boundary value problem outlined in these equations corresponds to determining the time-dependent current response to a step in substrate concentration, obtained for a polymer-modified electrode operating in the batch amperometric mode. Steady-state Solution Neglecting Concentration Polarization of Substrate in Solution In a previous paper,18 we presented a preliminary analysis for the steady-state current response as z -+ m. We now recall, and extend, these results. Under steady-state conditions, one may set au/& = 0 in eqn. (4). In this event, the steady-state reaction/diffusion equation takes the form yu = o d2u dX2 1 + a ~ --- Again, this is a non-linear ordinary differential equation.Depending on whether the factor au << 1 or au >> 1, we can simplify this expression to generate equations which may readily be integrated to yield the concentration profile u(X). The normalized current y is then given by Y = a($) x= 1 (9) As shown previously,18 when au << 1 , corresponding to unsaturated kinetics, eqn. (8) may be integrated to yield (10) where y’/* = @ = L/XK. Typical concentration profiles are illustrated in Fig. 1 for various values of the Thiele modulus Q,. We also show typical three-dimensional surface plots (the latter being obtained using Mathematica, version 2.2, from Wolfram Research Europe, Long Hanborough, Oxfordshire, UK) for the situation of low and high Q, values. When @ is large, the substrate does not travel very far into the layer before it reacts with immobilized catalytic sites.Considerable concentration polarization of substrate is present in the film. The catalytic kinetics are so rapid that the substrate is converted into product in a thin reaction layer near the film/solution interface. This statement may be readily illustrated by examining the form of the concentration profile when @ = yl/2 is large. Under such conditions, we may simplify the expression for the concentra- tion profile and immediately write that cosh(fiX) = $exp(fiX) and sech(fi) = 2exp(-<) and so u = exp[ - fi( 1 - X)]. Physically, this expression corresponds to an exponential decay in concentration from an initial value of u = 1 at X = 1, with a time constant given by ~ 1 1 2 , in a direction going into the film.On the other hand, when @ is small, we note that u(X) = cosh( fiX)sech( fi) sech(fi)cosh(fiX) = 1 - - I + - =: 1 i :)i y,”’) Consequently, there is little concentration polarization of substrate within the film. The unsaturated catalytic kinetics need only be considered, and we can neglect substrate diffusional effects because the latter are rapid. On the other hand, when au >> 1, corresponding to the case where the catalvst is saturated bv substrate. ean. (8) integrates to vield (1 1) Y 2a u(X) = 1 + -(X2 - 1) This expression is valid for y/2a < 1. For values greater than this the concentration profile will be negative, a situation which 1 .o 0.8 0.6 U 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1 .o x Fig. 1 Typical normalized substrate concentration profiles u(X) [corre- sponding to the situation of unsaturated kinetics (a < l)] calculated using eqn.(10) as a function of normalized distance X = x/L. Each profile corresponds to a specific value of the Thiele modulus @ = y”* = L/XK, where L denotes the total layer thickness and XK is the characteristic reaction layer thickness. From top to bottom: @ = 0.1, 0.5, 1.0, 2.0, 5.0, 10.0,20.0 and 50.0. Also included are three-dimensional surfaces indicative of the concentration profiles u = u(X, @) (top and bottom figures). The top figure is valid for low @ values whereas the bottom figure pertains for larger values of @.718 Analyst, June 1996, V d . 121 is physically unreasonable. We present the concentration profiles calculated from eqn. (1 1) for various @ values in Fig.2. Again, a three-dimensional surface plot is also presented. Using eqns. (9) and (lo), we may show that for au -=-K 1, the normalized steady-state current is given by y = a*tanh(*) (12) This expression is the normalized current response for the situation where the catalyst is not saturated by the substrate. In Fig. 3(a) we illustrate a plot of eqn. (12) for a = 0.5. When the latter plot is expressed in double logarithmic format, as in Fig. 3(b), dog-leg behaviour is observed, a slope of unity being observed for small y, changing to a slope of 0.5 at high y. When y is small (i.e., y < 0.33), we note that tanh(fi) =: fi, and so we obtain Y = ay (13) This expression describes rate-determining unsaturated catalyst kinetics under conditions where diffusion of substrate through the film is facile. We label eqn.(13) as the current response expected for a case I situation. When y is large (Le., y > 3) then tanh(fi) =: 1 and so eqn. (12) reduces to y = a f i (14) 1 .o 0.8 0.6 0.4 0.2 This corresponds to a case I1 situation and defines the current response expected for thick films and unsaturated catalytic kinetics. The expressions presented in eqns. (13) and (14) are in good accord with Fig. 3(b). Further, for au >> 1, and using eqns. (9) and (1 1), we can show that Y = y (15) This simple result means that the rate-limiting process is the turnover of the saturated catalyst. Eqn. (15) defines a case 111 situation. As noted previously, this expression is only valid for y < 2a. It will not be valid for large y values and a different approach must be taken for this situation (which we shall call a case IV situation).When one has thick films, corresponding to large values of the Thiele modulus Q>, when a >> 1, it may be expected that the outermost regions of the film will be completely saturated, whereas the inner regions of the film will be unsaturated. This ‘mixed case’ situation is illustrated in Fig. 4, which shows two regions: an inner region RI and an outer region RII. The demarcation line between RI and RII is set at some value X = X*. Clearly, when X* = 0 the entire layer is saturated, whereas when X* = 1 the entire layer is unsaturated. In RI we have au < 1 and in RII au > 1 and, more importantly, at X* au = 1. Further, when X = 0, du/dX = 0 and when X = 1, u = 1 as before.Now, in region RI, au < 1 and so we solve (d2u/dX*) - yu = 0. This is a standard differential equation which has as solution u = Acosh(6X) + Bsinh(*X), where A and B are constants which are determined via the boundary conditions. ’ t Y 0.0 0.0 0.2 0.4 0.6 0.8 1 .o x Fig. 2 Typical normalized substrate concentration profiles u(X) (corre- sponding to the case of saturated kinetics, specifically when a = 15) calculated from eqn. (1 1) as a function of normalized distance X for various values of the Thiele modulus Q,. From top to bottom: Q, = 0.1, 1.0,2.0,3.0, 4.0, 5.0. This figure was constructed noting the restriction that Cpz < 2a. A three-dimensional u(X, a) surface is also included (lower figure). 4 1 I I I I 1 0 10 20 30 40 50 60 lo2 I I I I I 1 I y = cx y’I2tanh {y’”} slope = 0.5 10’ loo - lo-’ - Y - lo4 - slope =1 a= 0.5 1 o4 lo4 10-’ loo 10’ lo2 lo3 Y Fig.3 (a) Plot of steady-state current response y [where y is defined in eqn. (9)] expressed in normalized form as a function of the kinetic parameter y obtained using eqn. (12). This plot is valid for au < 1. The plot was constructed for a = 0.5. (6) Plot of eqn. (12) in double logarithmic format. The limiting forms of eqn. (12) for low and high y values are outlined as insets.Analyst, June 1996, Vol. 121 719 Further, differentiating this expression we obtain: du/dX = fi[Asinh(*X) + Bcosh(*X)]. Now in RI we note that (du/dX)+ = 0 = 0, and so B = 0. Also, when X = X*, au = 1 and u = l/a, hence: A = l/acosh( fix*). From this result, we immediately obtain that: ($) = fiAsinh(fiX*) = -tanh(fiX*) fi (16) x=x* a We now examine region RII.In this region we solve (d2u/dX2) - ( y / a ) = 0. This may be integrated to yield du/dX = (y/a)X + 35 30 25 20 II 15 10 5 0 y = 10 I I I I I 0 10 20 30 40 50 60 a Fig. 4 (a) Delineation of the two-region approach used to obtain eqn. (22), which corresponds to the complex situation where y > 1 and a: > 1. The inner region RI is unsaturated whereas the outer region RII is saturated. The demarcation line between these two regions is set at some value X* = 1 - (2a:/y)*'* [eqn. (21)]. Complete saturation occurs when X* = 0. (b) Plot of the normalized current response y as a function of the saturation parameter a: for y = 10. This curve was calculated using eqn.(26). The limiting expressions for a: << 1 and a: >> 1 are also presented as insets. C and u = (y/2a)X2 + CX + D , where again C and D denote integration constants. We immediately note that Y x=x* a (g) = - x * + c Now the fluxes must balance at X = X* and so from eqns. (16) and (17) we obtain Further, we note that the normalized current is given by = y + aC = y + fitanh(*X*) - yX* x= 1 (19) Y = a($) Hence, in order to solve for the normalized current y , we must evaluate X*. This is done as follows. When X = 1, u = 1 in RII and so we can write that: 1 = (y/2a) + C + D. Also, when X = X*, u = la, and so 1 a 2a D = - - l X * 2 - CX* From the latter two expressions we obtain, on elimination of D, 1 Y x*2 + cx* + - - --- c = o 2a a 2a which is a quadratic in X*.We recall the result for C in eqn. ( 18) and note that for large y, tanh(fiX*) = 1. Hence eqn. (18) reduces to c =--- f i Y x* a a If we now substitute this approximate expression for C into the quadratic equation in X* and simplify, we obtain This expression may be readily solved for X* to obtain where we have noted that 2a >> 1 and y >> 1. From eqn. (21), we can readily define the condition for complete saturation within the film, when y is large. Complete saturation occurs when X* = 0. Hence we note that under such conditions y = 2a. Therefore, for the 'mixed kinetics' case, the range of validity is Substituting the result obtained for X* in eqn. (21) into the normalized current expression defined in eqn.(1 9) and again taking the large y limit, one obtains (22) This is our desired result, which will be valid when one has thick films (corresponding to y >> I), when a > 1 but a < y/2, and when part of the layer is saturated and part unsaturated. Eqn. (22) therefore defines the current response expected for a case IV situation. We can arrive at a similar result by a direct integration of eqn. (8). If we multiply both sides of the equation by du/dX, and note the following identity 1 < a < $2. y = y + 6 - yx* = 6 (1 + a) =: s y720 Analyst, June 1996, Vol. 121 then one obtains On integrating, we obtain 1 + au a! a 2 where C is a constant of integration. We may assume that du/dX = 0 when u = 0 and so we note that C = 0, and therefore eqn. (24) reduces to d 2 5 [ au - ln(1 + au) (25) 1 This expression may not be further simplified to obtain an expression for the concentration profile, because the integral may not be expressed in closed form. We can, however, readily use eqn.(25) to evaluate the normalized current response y. To do this we note that when X = 1, u = 1 and so This therefore is the expression for the current response when the saturation parameter a = 1 and when y >> 1. This expression is illustrated in Fig. 4 for the case where y = 10. Now, when a << 1 we note that In (1 + a ) = a - (a2/2) and so eqn. (26) reduces to y =: a e. This has been derived previously in eqn. (14). On the other hand, when a >> 1 we note that In (1 + a ) = In a , and if we assume that a >> In a , then eqn.(26) reduces to y = V'm, as predicted in eqn. (22), where the two-region strategy was considered. Hence we see that eqn. (26) connects the two previous approximate solutions presented in eqns. (14) and (22). Indeed, eqn. (26) serves as a connecting equation which joins kinetic cases I1 and IV. The two limiting regions are illustrated in Fig. 4. So far we have not considered the situation of very thin films. In such a situation, substrate depletion in the matrix may be neglected and we may assume that u = 1 for all values of X. In short, substrate diffusion is rapid and there are no reaction layers in the film. Under such circumstances, eqn. (8) reduces to d2u y dX* 1 + a - This expression may be directly integrated to yield aY y = - l + a This is just a normalized form of the Michaelis-Menten equation, and corresponds to the situation where the chemical kinetics of the substrate reaction at the catalyst surface are rate determining.Eqn. (28) is illustrated in Fig. 5 for various y values. We note that when a << 1 then y = a y , as previously deduced in eqn. (1 3) (case I). Further, when a >> 1 then y = y , as deduced in eqn. (15) (case 111). Hence eqn. (28) connects these two limiting cases. The changeover from first-order to zero-order kinetics as a increases is readily apparent from Fig. 5. Eqn. (28) has been used previously to obtain values of K M and k, for the oxidation of glucose at Ru02-loaded carbon paste electrodes in 1.0 mol dm-3 NaOH27 and for the oxidation of ascorbate at electronically conductive polypyrrole elec- trodes .28 Albery et al.32 examined the kinetics of bound enzyme systems and developed an analysis which may be applied to the present system.They constructed empirically a global expres- sion that describes the current response when a and y are close to unity. Using the present notation, the expression derived by Albery et al.32 is given by ( I + a)VZ[a - In(1 + a)] y + V 2 y [ a - ln(1 + a)] tanh (29) This function, y (a), is presented in Fig. 6 for different y values (corresponding to different layer thicknesses). One notes from these curves that a transition from first- to zero-order kinetics is predicted as the normalized saturation parameter a is increased. This type of biphasic kinetic behaviour is characteristic of Michaelis-Menten kinetics.Note that the transition between the two regions is more extended when y is large. Hence, Michaelis-Menten-type biphasic behaviour is also to be expected when substrate diffusional effects, in addition to the catalytic reaction, are considered. It is interesting to note from Fig. 6 that the general expression, eqn. (29), which accounts fully for diffusion of substrate in the polymer matrix, is similar in form to the simpler Michaelis- 1 .o 0.8 0.6 Y 0.4 0.2 0.0 - y = 0.5 a y= 0.1 y= 0.01 _- I 0 5 10 15 20 25 a Fig. 5 Graphical representation of the steady-state current response y as a function of the saturation parameter 01 obtained using the Michaelis-Menten expression presented in eqn. (28). The limiting first- and zero-order kinetic responses at low and high 01 values are apparent.0 10 20 30 40 50 60 a Fig. 6 for the current response for different values of the kinetic parameter y. Schematic representation of Albery ef af.'s expression [eqn. (29)]Analyst, June 1994, Vol. 121 72 I Menten expression for thin films presented in eqn. (28). The expressions presented in eqns. (28) and (29) are directly compared in Fig. 7 for y = 0.1, 1 and 10. Now the parameter y is defined as the ratio of the pseudo-first-order rate constant k and the diffusional frequency of substrate within the film (Ds/ L*). When y is small, the reaction kinetics are much slower than substrate diffusion, and so the effect of the latter on the over-all current response will be negligible. Hence, we note for y = 0.1, corresponding to rapid substrate diffusion, the two expressions [i.e., the Michaelis-Menten approximation, eqn.(28), and the Albery expression, eqn. (29)] cannot be distinguished. Reason- able agreement is observed for y = 1, whereas for large y values (y = lo), the simple Michaelis-Menten equation overestimates the current. Under these conditions substrate diffusion becomes much slower than chemical reaction, and so cannot be neglected. The true current response will be lower than that predicted via use of the Michaelis-Menten expression. The data presented in Fig. 7 are transformed into Lineweaver-Burk form in Fig. 8. The important point to note from this figure is that linear Lineweaver-Burk behaviour (l/y proportional to l / a ) is to be expected regardless of whether or not substrate diffusion in the film influences the reaction kinetics.It should also be noted that the intercept of the Lineweaver-Burk plot is not sensitive to substrate diffusion effects, but the slope of the plot depends greatly on the latter, especially when y is large. In Fig. 9 we illustrate the variation of the normalized current response y [as predicted from eqn. (29)] with the parameter y for two different a values. In both the unsaturated and saturated situations (low and high a values, respectively), the current response increases as y increases. This is to be expected as the reaction kinetics (quantified via the rate constant k ) become more facile. Eqn. (29) underpins the entire theory, in that it reproduces the various limiting expressions already derived if suitable limiting values for y and a are chosen.In particular, for a >> 1 and for all y values up to an upper limit of y = 2a, one can show using eqn. (29) that , 7% This expression is not readily derived via direct integration of the differential equation, and is illustrated in Fig. 10. Eqn. (30) connects cases I11 and IV. This can be shown by noting that when y is small, eqn. (30) reduces to y = y, which defines the current response for case 111. Also, when y is large eqn. (30) reduces to y =: w, which of course defines the current response for case IV. This prediction is confirmed from Fig. 10. We now gather together the simple expressions obtained for the normalized flux y for each of the four kinetic cases. First, for a <c 1 and y <c 1, then y = a (case I). In contrast, for a << 1 and y >> 1, we have y = a& (case 11). Further, for a >> 1 and y << 1, we can write y = case 111).Finally, for a >> 1 and y >> 1, we have y = &((case IV). When the specific expressions for y, y and a are introduced into these limiting 0.15 0.10 Y 0.05 0.00 0 10 20 30 40 50 60 1.2 r I I I I I 1 0.0 I I 1 1 I I 0 10 20 30 40 50 60 a 0 ‘ I I I I I I 0 10 20 30 40 50 60 Fig. 7 Generalized Albery et al.’s expression for the normalized current response [eqn. (29)] as a function of the saturation parameter (Y, compared with the simpler Michaelis-Menten expression [eqn. (28)]. It is clear that a good agreement between the two theoretical expressions is obtained when y is small [(a) and (h)], but that marked deviations occur when y is large (c).Hence, when y is large use of the Michaelis-Menten expression will generally lead to an over-estimation of the current response. The y parameter quantifies the extent to which substrate diffusion in the film affects the reaction kinetics. 120 100 80 Y-’ 60 40 20 I 1 1 I I I 16 14 12 10 8 6 4 2 n 3.5 3.0 2.5 2.0 1.5 1 .o 0.5 0.0 I I I 1 I 1 I I I I 0 2 4 6 8 1 0 1 2 0 2 4 6 8 1 0 1 2 0 2 4 6 8 1 0 1 2 a-’ Fig. 8 Transformation of the curves presented in Fig. 7 into linear Lineweaver-Burk format. Both the simple Michaelis-Menten expression (which neglects the effect of substrate diffusion in the film) and the more complex Albery et al.’s expression (which includes the effect of substrate diffusion on the current response) predict linear Lineweaver-Burk behaviour.722 Analyst, June 1996, Vol.121 1.0 forms of the flux, a clear relationship between the current response and the fundamental experimental variables cz, L and sm are obtained. These mechanistic indicators are illustrated in Table 1. We note from this table that an unusual half-order dependence with respect to bulk substrate concentration sw is predicted for the case IV situation where one has a situation of thick films and high substrate concentrations. Further, each case corresponds to a unique set of dependences with respect to the triplet of experimentally variable parameters cx, L and sm. The point to note from this is that an approximate mathematical analysis enables one to make unambiguous predictions as to the expected current response, which may subsequently be checked experimentally.We can also present the results in terms of a kinetic case diagram (Fig. 11). At this point, it is instructive to introduce an alternative approach for solving eqn. (€9, which involves the introduction of the following 'magic' approximation: (4 - U a + u - (31) 1 + au (1 +a)* where 0 d u d 1. This approximation will only be valid for certain values of a and u. In Appendix I we show that the approximation presented in eqn. (3 1) will be valid for all values Y Fig. 9 (29) with the parameter y for (a) 01 = 0.1 and (b) 01 = 50. Variation of the normalized current response y calculated from eqn. 40 I I I I I I 1 y = (2ay) '"tan h { (924 ' "} I a=25 I I I I I 0 10 20 30 40 50 60 Y Fig.10 Plot of eqn. (30) obtained as a sub-case of the general expression for the current response presented in eqn. (29). This expression is valid for a x- 1 and for all y values up to a limit of y = 2a. Table 1 NLLS fitting parameters obtained via Sigmaplot analysis Substrate U b C Dopamine 10.01 13.73 68.85 Noradrenaline 28.13 4.42 124.80 Adrenaline 45.91 2.68 167.10 of u when the Michaelis-Menten kinetics are unsaturated (i.e., when a < 1). For saturated Michaelis-Menten kinetics, the approximation becomes inaccurate if significant substrate depletion occurs, i.e., if u falls to less than 0.8 at any point in the film. This will, of course, depend on the y value. The approximation will be a poor one for large y values. Using eqn. (31), we note that the steady-state reaction diffusion equation presented in eqn.(8) reduces to d2u y(a + u) dX2 (1 + a)2 (32) - - ~ - - 0 Hence the resultant reaction diffusion equation is transformed into linear form, which may readily be solved (see Appendix 11) to yield u(X) = (1 + a)cosh ( l z ) s e c h ( z ) - - a (33) We may readily show that the normalized current response is given by Y = a(:) = .fitah(-) fl (34) X = l l + a Clearly, when a 1, eqn. (34) reduces to y = a f i t a n h ( f l ) (35) which is identical in form with eqn. (12) [see also Fig. 3(a)]. Hence this expression connects cases I and 11. Further, when y is small and for all values of a, we can readily show that tanh[fi/(l + a)] = </(1+ a), and so eqn. (34) reduces to aY y " - l + a Of course, this is the simple Michaelis-Menten equation (connecting cases I and 111) previously outlined in eqn.(28). The Fig. 11 Kinetic case diagram for the steady-state reaction diffusion problem. The diagram consists of a plot of log y versus log 01. The four major sub-cases presented in the text are outlined. Also included as insets are the approximate analytical expressions for the normalized current for various limiting values of y and a.Analyst, June 2996, Vol. I 2 2 723 Ul) = (YY correspondence between eqns. (34) and (28) is presented in Fig. 12. Alternatively, when (Y >> 1, eqn. (34) reduces to 1 - (37) This expression differs from eqn. (30) (the latter serves to connect cases I11 and IV), but for small y we note that eqn. (37) reduces to y = y, which is case 111.Eqn. (30) also reduces to the same result under the same conditions. We finally compare eqn. (34) with the expression presented in eqn. (26) which connects cases I1 and IV. This is done in Fig. 13 for y = 50. It is clear that the agreement between the two expressions is very poor. This is to be expected, as the ‘magic’ approximation breaks down when substrate depletion within the film becomes appreciable, which will happen when y is large. Inclusion of Concentration Polarization of Substrate in Solution We now extend the analysis of the steady-state current response and consider the effect of substrate diffusion in solution and in the polymer film. In this case the boundary conditions are given by Ds x = L x, = -(SOD - SL) x = L DF(z) The first boundary condition is the same as that presented previously.The second differs from that adopted in our previous discussion since we have replaced sm, the bulk concentration of substrate, with sL, the concentration of substrate at the film/ solution interface. We also need to introduce a third boundary condition, that of flux balance at the film/solution interface. In the last boundary condition expressed in eqn. (38) we denote DS and DF as the diffusion coefficients of the substrate S in the solution and film, respectively, and XD represents the diffusion layer thickness*. We can introduce a diffusional rate constant kD as follows: kD = Ds/XD. The latter parameter quantifies the rate of substrate transport in the solution. It is now useful to introduce the Biot number Y as follows: ‘magic’ approximation 1 y 0.05 y = 0.1 1 This dimensionless parameter compares the rate of substrate diffusion in the solution with the rate of diffusion of substrate within the film.The boundary conditions can now be expressed in dimensionless terms as follows: du dX x = o - = o x = 1 ($r)l = v(1 - u1) X = l u = u 1 We now solve the steady-state reaction diffusion problem defined by eqns. (8) and (40). We use the ‘magic’ approxima- tion introduced in eqn. (31). As outlined in Appendix 111, the approximate solution for the concentration profile is given by u(X) = u l c o s h ( z ) s e c h ( s ) + a[ c o s h ( s ) s e c h ( z ) - I ] (41) where u1 is given by f i a v- Y - - t a n h ( 2 ) l + a l + a u1 = Y + - fi tanh (2) l + a Hence we note that the u1 factor contains the effect of external mass transfer.The steady-state current response y is given by y = av(1 - v - - v y a t a n h ( 2 ) v + - fi t a n h ( 2 ) l + a l+cU 0.00 I I I I I I 0 10 20 30 40 50 60 a Fig. 12 Correspondence between the current response presented in eqn. (34), obtained using the ‘magic’ approximation introduced in eqn. (31), and the simpler Michaelis-Menten expression for the current response pre- sented in eqn. (28). The agreement is excellent when y is small (i.e., when substrate diffusion does not markedly affect the catalytic kinetics). * Since we differentiate between DF and Ds, then the normalized steady-state current response y and the parameter y are redefined as y = iL/nFAKMDF and y = kL2/DF. 30 Y I I I 1 I J 0 5 10 15 20 25 30 a Fig.13 Illustration of the breakdown of the ‘magic’ approximation when y is appreciable. The expression for the current response using the latter approximation is clearly at variance with that predicted using eqn. (26).724 Analyst, June 1996, Vol. 121 which simplifies to a* tanh (*) l + a v = J 1 +- 6 t a n h ( 2 ) v(l + a) (44) This function is illustrated in Fig. 14, where y is plotted as a function of a for a given value of y ( y = 0.1) and for various values of the Biot number We see that when v is small, the effect of external diffusion is most marked, but when Y is large (> 50, say) the observed normalized current response is indistinguishible from the Michaelis-Menten expression [eqn. (28)] or from the Albery equation [eqn. (29)] derived earlier.Hence when v is large, concentration polarization in the solution may be neglected. This may be noted also from examination of eqn. (44). When Y is large then v--l-+ 0 and eqn. (44) reduces immediately to eqn. (34). We invert eqn. (44) to obtain the following result: 1 1 1 - - - - c o t h ( z ) + a(1 + a)v Y a 6 (45) We see that a clear separation is effected between substrate diffusion/reaction in the polymer film and substrate diffusion in the Nernst diffusion layer adjacent to the polymer film. In many respects eqn. (45) is reminiscent of two resistors in series consisting of an internal resistance (reaction/diffusion) and an external resistance (substrate transport in solution). Note also that eqn. (44) will be valid for all a values provided that y is not too large.An upper limit of y is approximately 2. When y is small and y1/2 << a and a << 1, then eqn. (45) reduces to a case I scenario: 1 1 1 Y a Y -+- - - Transforming into dimensioned quantities, we obtain 1 (47) In the latter expression we have assumed that mass transport in solution is controlled via the use of a rotating disc electrode (RDE), and defined the mass transport rate constant k D in terms nFA KM 1 KM +-=-+- i kcCxLKSw k ~ s , k c C x ~ K S w F L S ~ O ' / ~ --____ - 0.1 5 0.10 Y 0.05 0.00 0 10 20 30 40 50 60 01 Fig. 14 Plot of the normalized current response y as a function of the saturation parameter a for various values of the Biot number Y (which quantifies substrate diffusion in the Nemst diffusion layer adjacent to the polymer film).The curves were calculated using eqn. (44). When Y is small the effect of external diffusion is most marked. For large Y the shape of the y = y ( a ) profile is invariant with Biot number. of this electrode configuration. (We recall that for an RDE configuration kD = 1.55 D?I3 v-l/6 o 1 / 2 . The rotation speed 03 is expressed in hertz. Eqn. (47) is a simple Koutecky-Levich-type expression, where we have introduced the Levich factor FL as FL = 1.55DS2/3v--l/6, where v denotes the kinematic viscosity of the solution. On the other hand, when y is large then eqn. (45) reduces to 1 1 1 - y &fi+G This corresponds to a case I1 situation. In terms of dimensioned parameters, we have 1 1 kcCXDF KSm kDSm k,cXDF K S ~ FLsm w 112 (49) Again, linear Koutecky-Levich behaviour (i- 1 linear with o-ll2) is predicted. It is interesting that as y becomes very large (still assuming that a -=c l), then y-1/2+ 0 in eqn.(48) and the observed current response becomes totally dominated by external diffusion. This will become manifest in a Koutecky-Levich plot that exhibits a zero intercept. When a >> 1, and for small y (case III), we note that eqn. (45) reduces to 1 1 1 - - - - + - Y Y a2v In terms of dimensioned parameters, we obtain (51) i k , q L K S ~ ~ F ~ C O ~ / ~ In this case the variation of i-l with o- ll2 is linear, but the slope of the latter plot varies as a--2 rather than a-1 as would be predicted from a simple Koutecky-Levich expression. Our analysis has not considered the case IV situation corresponding to large y and a > 1.As noted previously, the 'magic' approximation does not work for this range of y and a values. We therefore defer discussion of this case. nFA 1 K M - + Potentiometric Sensors Exhibiting Michaelis-Menten Kinetic Behaviour We finish our discussion with a brief examination of the steady- state potentiometric response expected for a system exhibiting Michaelis-Menten kinetics. The general time-dependent prob- lem has been tackled previously by Carr35 using Fourier analysis resolution of the reaction/diffusion equation, and the steady-state problem has been examined by Brady and Carr36 via digital simulation using orthogonal collocation methods. Tran-Minh and Broun37 have also examined both the steady- state and transient potentiometric response using digital simula- tion methods.For the steady-state situation, we note that (assuming that the diffusion coefficients of reactant and product within the film are equal for simplicity) yu - 0 d2u dX2 1 + au d2w yu -+-=o dX2 1 + au where w denotes the normalized product concentration given by w = P / K S ~ and p represents the concentration of product in the film. Now, for a potentiometric sensor, the boundary conditions are given byAnalyst, June 1996, Vol. 121 725 limiting expressions for the product concentration at X = 0 are in excellent agreement with those previously reported by carr.35 Verification of the Theoretical Analysis: Electro-oxidation of Catecholamines at Polypyrrole-modified Electrodes In this section, we present some experimental results pertaining to the electro-oxidation of the neurotransmitters dopamine, adrenaline and noradrenaline at surfactant doped polypyrrole (PPy)-modified electrodes.A complete account of this work will appear in a subsequent paper38 and so we shall concentrate our discussion on the presentation of batch amperometric current-concentration data. The PPy/DBS- (DBS- = dodecylbenzenesulfonate) poly- mer was formed via potential step electropolymerization (deposition potential, 800 mV versus Ag/AgCl) on to a glassy carbon disc electrode (Metrohm, geometric area = 0.071 cm2) immersed in a solution containing 50 mmol dm-3 pyrrole and 0.1 mol dm-3 C12H25C6H4S03Na. All solutions were prepared using ultrapure Milli-Q water and AnalaR-grade reagents. A conventional three-electrode electrochemical cell (Metrohm) was used.Potentials were measured and are quoted with respect to an Ag/AgCl (saturated KC1) reference electrode. A large Adding the two expressions in eqn. (52), we obtain d2u d2w dX2 dX2 +- = 0 - (54) This expression may be readily integrated twice, using the boundary conditions presented in eqn. (53), to obtain the following relationship between u and w: w(X) = 1 - u(X) ( 5 5 ) Hence, the concentration profile of product through the film may be readily evaluated. In particular, using the ‘magic’ approximation we can show that w(X) = 1 - [ (1 - a ) c o s h ( z ) s e c h ( z ) - a] (56) = (1 + a)[ 1 - c o s h ( ~ ) s e c h ( z ) ] HO In particular, the sensor device measures the product concen- tration wo at X = 0 and so we note that wo = (1 +a)[ 1 - sech(2-l (57) OH This is a very simple result.Eqn. (57) is presented in Fig. 15 for various values of a. When a -ZK 1, eqn. (57) reduces to wo = 1 - sech(fi) (58) whereas when a >> 1, we obtain Noradrenaline HO wo = a[1 - rcch()] (59) Now, when a >> 1 we note that sech(*/a) = 1 - (y/2a2) and so wo =: y/2a. Transforming back into dimensioned parameters, we note that the product concentration po at the sensor surface (at x = 0) is given by po = kccxL2/2D~. The OH Adrenal inc 0.4 0.3 wo o.2 0.1 0.0 i-i 0 1.2 a cc1 ( b ) lo-’ loo 10’ lo2 103 Do pa mi nc 0 10 20 30 40 50 60 a log Y Fig. 15 (a) Application of the ‘magic’ approximation to obtain the normalized product concentration wo as a function of saturation parameter 01 for a sensor operating in the potentiometric mode.The curve was calculated using eqn. (57). The effect of substrate diffusion in the film (via the y parameter) on the product concentration detected at the electrode surface at X = 0 is outlined. Also the limiting behaviour expected at low and high (Y values is presented. (b) Variation of wo with log y for the situation where 01 < 1 as predicted by eqn. (58). The product concentration detected at the electrode surface increases rapidly with increasing y. HO 0 Scheme 1726 t ._ 0 ' Analyst, June 1996, VoE. 121 0 - surface area graphite cylinder served as counter electrode. Solutions were degassed with oxygen-free nitrogen gas prior to electrochemical measurements. Cyclic voltammetric and batch amperometric measurements were performed using an EG&G Model 273 potentiostat/galvanostat and a Linseis Model 17 100 X-Y recorder.It is well established that catecholamines undergo a 2e-, 2H+ redox transformation as outlined in Scheme 1. Typical voltam- mograms obtained at a sweep rate of 2 mV s-1 for cate- cholamine oxidation at PPy/DBS- electrodes are outlined in Fig. 16. All catecholamine concentrations are 250 pmol dm-3 and the supporting electrolyte used was McIlvane buffer (pH 3). The corresponding RDE voltammograms (again recorded at 2 mV s- and at a rotation rate of 3000 rpm) are presented in Fig. 17. In Fig. 18 we outline the Tafel plots extracted from the RDE voltammetric data presented in Fig. 17. The general features of the voltammograms are similar. A well defined reduction peak is observed during the reverse potential sweep for all three substrates.Hence the redox reaction presented in Scheme 1 is chemically reversible. Peak separations are larger than that expected for a rapid, reversible, two-electron transfer process. Consequently, in an electro- chemical sense, the redox chemistry exhibits quasi-reversible electron transfer kinetics. We note that the redox reaction is diffusion controlled if a potential more positive than 650 mV is applied. It is interesting that dopamine undergoes oxidation at less positive potentials than either adrenaline or noradrenaline, but the polymer electrode is not selective electrochemically with respect to adrenaline and noradrenaline. The analytical aspects of the amperometric detection will be discussed in a forthcoming ~aper.3~ Typical steady-state current-voltage curves for the oxidation of dopamine, adrenaline and noradrenaline are presented in Fig.17. A similar behaviour is observed for all three substrates. As noted previously, the experiments were performed at a polymer- coated RDE rotating at 50 Hz. Such a high rotation speed was chosen in order to minimize complications due to solution- phase diffusional transport of substrate to the polymer surface. The latter curves may be analysed using the Tafel equation to obtain mechanistic information about the nature of the rate- determining step in the oxidation process. The results of such a Tafel analysis is presented in Fig. 18. These plots are all linear at low potentials. Curve A corresponding to dopamine oxidation has a Tafel slope of 124 mV per tenfold change in current; curve B for noradrenaline oxidation has a Tafel slope of 118 mV per decade current change, whereas curve C for adrenaline oxidation has a slope of 130 mV per decade.These slope values imply that the first electron transfer step in the overall oxidation sequence is rate determining. A deviation from Tafel linearity was observed at higher potentials. This could possibly be due to a number of factors. First, the current response becomes increasingly affected by diffusional transport effects in solution as the applied potential becomes more positive. A slight drop in current is expected under such conditions. Second, it is also possible that the deviation from Tafel linearity observed could be due to the fact that the rate-determining step changes at high potentials.The first suggestion appears to be the most satisfactory. Table 2 Typical kinetic parameters obtained via NLLS fitting to eqn. (29) using Sigmaplot analysis KM/ 106 Dj Substrate pmoldm-3 kJs-1 crn*s-' y LIXK Dopamine 68.85 0.42 1.59 13.8 3.7 Noradrenaline 124.80 0.38 2.47 4.4 2.1 Adrenaline 167.10 0.37 3.01 2.65 1.63 Batch amperometric experiments were conducted at a polymer-coated electrode rotating at 3000 rpm. The applied potential was 500 mV. The protocol consisted of monitoring the steady-state current response to increasing additions of sub- strate. These raw data were then curve fitted to either the simple Michaelis-Menten equation [eqn. (28)] or to the more general expression in eqn.(29). The results are presented in Figs. 19 and 20. The NLLS (non-linear least-squares) curve-fitting software used was Sigmaplot (Jandel Scientific, Corte Madera, CA, USA). The Sigmaplot curve fitter uses the Marquardt- Levenberg algorithm, which utilizes a least-squares procedure to minimize the sum of the squares of the differences between the target equation values and the experimental raw data values. The results of the NLLS fitting to eqn. (28) are outlined in Fig. 19. The fit to the simple Michaelis-Menten expression is 0.5 PA I r T 0.5 PA 0.6 EN Fig. 16 Typical voltammetric responses recorded at a PPyDBS-- modified electrode for (a) adrenaline, (b) noradrenaline and (c) dopamine. All concentrations are 250 pmol dm-3 in McIlvane buffer (pH 3).Sweep rate, 2 mV s- I .Analyst, June 1996, Vol. 121 727 good when the substrate concentration is low, but is less reasonable for high substrate concentrations. This observation is valid for all three substrates examined. Hence substrate diffusion within the film and also the Michaelis-Menten kinetics at the polymer sites affect the observed current response and the more complex expression for the current response presented in eqn. (29) must be used. Recasting eqn. (29) in dimensional form, we obtain where we have set the partition coefficient K = 1. This expression may be written in the following form: i swb I where a, b and c are given by nFAKMD, k,cyL* a = , b = ~ and c = KM (62) L KMDs and are to be regarded as fitting parameters. The results of the NLLS fitting are outlined in Fig.20 for each substrate. It is clear that good agreement between the batch amperometric data (represented as discrete points) and eqn. (61) is obtained. Some deviation between theory and experiment is observed in regions where the substrate concentration is high (typically for soo > 550 pmol dm-3). This can be attributed to the fact that the sensor exhibits surface fouling at elevated t - 0 ' substrate concentrations which results in the dimunition of the current response. The NLLS fitted values of the parameters a, b and c are presented in Table 1. From the latter values and using eqn. (62), we may derive least-squares estimates for the fundamental kinetic parameters KM and k, and for the substrate diffusion coefficient for substrate transport through the polymeric matrix, the latter parameters by noting that the surface coverage* r = 2.4 x 10-8 mol cm-2, the layer thickness L = 1.5 pm = 1.5 X 10-4 cm and so the active site concentration cx = T/L = 1.6 X 10-4 mol cm-3.Values for KM, k, and Ds for each of the substrates studied are presented in Table 2, which also gives the corresponding y and L/XK values. A number of features may be noted from the results in Table 2. First, the K M value increases significantly as the substrate changes from dopamine to noradrenaline to adrenaline. The K , value may be taken as a measure of the affinity of the polymer sites with respect to substrate binding. A low KM value signifies a high substrate- site affinity. Hence dopamine has the highest affinity for the polypyrrole sites and adrenaline has the lowest.The k, values are all fairly similar and are typically 0.4 s-1. This type of value indicates that the polymer-substrate complex dissociates rap- idly into product, and that a high turnover is to be expected. The values obtained for the substrate diffusion coefficient are high, being typically in the range 1.5 X 106-3 X 10-6 cm2 s-1. These values are higher than that usually observed for the diffusion of substrates through polymeric matrices and may well indicate that the polymer material exhibits a more open morphology when in the conducting state (as it is at the potential of 500 mV used in the batch amperometric experiments). Finally, the normalized parameter y varies significantly with substrate type. The maximum y value is obtained for dopamine (13.8).Lower y values are obtained for noradrenaline (4.4) and adrenaline (2.6). We recall that y = kL*/D, = k/kD, where k denotes the pseudo-first-order rate constant for chemical reaction at the polymer site and k~ denotes the diffusional rate constant for substrate transport through the polymer matrix. The simple Michaelis-Menten expression [eqn. (28)] is valid only for small y values where substrate depletion in the polymer matrix may be neglected and one only must consider the non-linear reaction kinetics between the polymer sites and the substrate. The fact that y is significant for all substrates examined indicates that substrate concentration polarization in the film cannot be neglected and will affect the observed steady-state current ~~~~ ~ * The surface coverage is obtained by integrating the voltammetric response recorded at low sweep rate for the PPy-DBS layer in supporting electrolyte i i I 0 I 0.7 0 0.7 E N 1 I I 0 0.7 Fig.17 (b) adrenaline and (c) noradrenaline. Sweep rate, 2 mV SKI. The substrate concentration in each case was 250 kmol dm-3 in McIlvane buffer (pH 3). Typical RDE voltammograms (o = 3000 rpm) recorded at a PPy/DBS--coated glassy carbon disc electrode for the oxidation of (a) dopamine,728 Analyst, June 1996, Vol. 121 response. This observation explains why there is such a poor agreement between simple Michaelis-Menten theory and experiment, as presented in Fig. 19. Also, we observe that the balance between the rate of substrate reaction and substrate diffusion depends on substrate type.The reaction kinetics for Conclusions The steady-state amperometric response for a polymer-modi- fied electrode system which exhibits Michaelis-Menten kinet- detail. Approximate analytical has been discussed in dopamine oxidation are considkrably faster than those for dopamine diffusion, but this difference is less marked for the noradrenaline and adrenaline substrates. Finally, we can also estimate the characteristic ratio L/XK for each substrate. The latter quantity is related to the y value via fl = L/XK, where L denotes the layer thickness and X K represents the distance travelled by the substrate into the layer before it interacts and reacts with the immobilized catalytic sites. We note from Table 2 that for dopamine L = 4XK whereas for noradrenaline and adrenaline L = 2 X,.Hence a smaller proportion of the polymer layer is engaged in electrocatalysis when dopamine is used as a substrate as compared with either noradrenaline or adrenaline. This observation correlates with the greater chemical reactivity observed for the dopamine-polymer site reaction and with the fact that dopamine exhibits the lowest K M value (and hence the greatest site-substrate affinity) of the three substrates exam- ined. These observations indicate that the theoretical model 510 575 660 presented in this paper is in good agreement with experimental EN observation kinetic and catalysis of 4 =a and that the an&’sis can be used to o b h n useful mechanistic infOrmation pertaining to the electro- the substrate reaction at the modified electrode.Fig. 18 Tafel plot analysis for catecholamine oxidation at the polymer coated RDE. A, Dopamine; B, noradrenaline; and C, adrenaline. Conditions as in Fig. 17. 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 [DA]/pmol ~ i r n - ~ [NA]/pmol dm3 [AD]/pmol dm-3 Fig. 19 Batch amperometry at a polymer-coated RDE (o = 3000 rpm, E = 500 mV). The discrete data points represent the batch amperometric results and the full line represents the NLLS fit curve obtained from simple Michaelis-Menten theory [eqn. (28)]. (a) Dopamine; (b) noradrenaline; and (c) adrenaline. 140 120 100 ao 4 =a 60 40 20 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 [DA]/pmol dm” [NA]/pmol dm-3 [AD]/pmol dm-3 Fig. 20 Batch amperometry at a polymer-coated RDE (o = 3000 rpm, E = 500 mV).The discrete data points represent the batch amperometric results and the full line represents the NLLS fit curve obtained from theory [eqn. (29)], which takes substrate diffusion in the polymer matrix into account. (a) Dopamine; (b) noradrenaline; and (c) adrenaline.Analyst, June 1996, Vol. 121 729 solutions to the non-linear reaction diffusion equation have been presented and the limits of validity of each solution evaluated. In particular, a novel approximation has been developed that can be used to integrate the reaction/diffusion equation. Concentration polarization of substrate both within the polymer matrix and in the external bathing solution has been explicitly considered. Finally, the application of our analysis to potentio- metric sensors has been briefly presented, and the results obtained are in excellent agreement with those published previously.The theoretical model presented for the steady-state ampero- metric response has been used to quantify the steady-state current-substrate response profiles, recorded under batch amperometric conditions, for dopamine, adrenaline and nora- drenaline electro-oxidation at thin surface-deposited surfactant- doped polypyrrole films. Good agreement was obtained be- tween the experimental batch amperometric data and the theoretical current response given by eqn. (29). Non-linear least-squares fitting of eqn. (29) to the experimental data yielded numerical values for the fundamental kinetic parame- ters defining the Michaelis-Menten mechanism.A number of more general considerations may be mentioned at this point. First, the electrocatalytic effect may well be dependent on the identity of the conducting polymer. In a forthcoming paper28 we describe the electro-oxidation of ascorbate at doped polypyrrole- and polyaniline-modified electrodes. Now the over-all substrate oxidation kinetics at the polymer film may be quantified by a modified electrode rate constant We have shown28 via analysis of the rotating disc voltammogram that k’ME may be measured at polypyrrole electrodes but the latter parameter is too large to measure accurately when ascorbate is oxidized at polyaniline-modified electrodes. In this situation, the net current is determined by the diffusional transport of ascorbate in solution.Hence polyaniline appears to be a very efficient electrocatalyst for ascorbate oxidation, and indeed is more efficient than polypyrrole. It is also clear that the catalytic efficiency of the polymer film will depend on the nature of the counter ion employed in the polymer electrosynthesis. It is well established that the dopant counter ion can affect the redox switching behaviour of the conducting polymer and can directly effect the electronic conductivity of the polymer by causing enhanced chain orientation.39 The counter ion can also affect the morphology of the polymer film. We have shown that polypyrrole films doped with C1- ions exhibit a more open morphology than polypyrrole films doped with DBS- ion. As yet we have not experimentally investigated whether the polymer morphology directly affects the mechanism of elec- trocatalysis.However, the polymer morphology will have a distinct role to play in determining the magnitude of the substrate diffusion coefficient for transport in the polymer phase. The latter parameter plays a direct role in determining the shape of the steady state current response [recall eqn. (29)]. This is where morphology will make its mark. It will effect the y parameter discussed in the theory. We recall that y = k/kD, where k denotes the pseudo-first-order rate constant for chemical reaction at the polymer site and kD is the diffusional rate constant for substrate transport through the polymer matrix. Now k~ = &/L2. Hence, if Ds is made smaller by making the polymer morphology more compact, then k~ will decrease and substrate concentration polarization in the film will become more important and y will increase.The form of the kinetic law will not change, however. The substrate-polymer site inter- action will still be described by the Michaelis-Menten kinetic expression. Finally, we note that a Michaelis-Menten mecha- nism is normally used for enzyme kinetics where there is a very specific lock-and-key mechanism in operation. In our case, the catalysis is less specific. Further experimental work is required in order to establish the chemical structure of the substrate binding mode to the polymer catalytic site. These studies will shortly be initiated. The studies reported in this paper are of a theoretical nature. They lay the foundation for the rational development of a new class of amperometric chemical sensor. The work and the theoretical expressions derived therein may be used to predict the steady-state sensor response on experimental variables such as the polymer layer thickness, the catalyst site loading and the substrate concentration. The theoretical curves presented may also be used to predict the threshold value of substrate concentration for which the amperometric response is linear. In the following paper of this series, the more complex time- dependent reaction/diffusion problem, as presented in eqn.(4), will be examined. This will enable the response time of the sensor to be quantified. This work was supported by the Polymer Research Unit, Trinity College, Dublin (Materials Ireland), BioResearch Ireland and the British Council.Appendix I In this Appendix we examine the validity of the ‘magic’ approximation presented in eqn. (31). We consider the reaction term in eqn. (8) and define the following functions: We are interested in the conditions under which the approxi- mation G(u,a,y) = F(u,a,y) pertains. Now 0 S u d 1. We evaluate each of the functions presented in eqn. (I. 1) for various values of a and y. In Fig. 1.1 we outline a plot of the reaction term (exact or approximate) vei-sus u for various values of the saturation parameter a and the kinetic parameter y. For all values of a, we note that the approximate expression G will differ markedly from the full expression F when u ex 1 (signifying the operation of considerable reactant depletion within the polymer film) and when y is large.Appendix I1 We now derive eqn. (33) outlined in the paper. We begin with the approximate form of the reaction/diffusion equation: d2u y(a + u) dX2 (1 + a)2 - 0 (11.1) This equation admits the following solution: (11.2) -- ’(a + ’) - Acosh(z) + Bsirih(*) (1 + a)* l + a l + a730 Analyst, June 1996, Vol. 121 0.5 0.4 0.3 0.2 0.1 g 0.0 al c c .- c 0.3 [r 0.2 0.1 0.0 , 0.0 0.2 0.4 0.6 0.8 1 .o U 2.0 1.5 1 .o 0.5 0.0 0.05 1 1 I I 0.04 0.03 0.02 0.01 0.0 0.2 0.4 0.6 0.8 1 .o U Fig. 1.1 Evaluation of the validity of the ‘magic’ approximation. F(u,a,y) represents the accurate reaction term and G(u,a,y) denotes the approximate form of the chemical reaction term. The curves shown compare the functions F and G over the range 0 < u < 1 and for various values of a and y.In all instances the approximate term G will differ from the accurate term F when u differs appreciably from unity, i.e., when significant diffusional depletion of substrate in the film occurs. whereA and B are constants to be determined from the boundary conditions. Solving for u, we obtain u = ~ (1 + a)2 = [ A c o s h ( z ) + B s i n h ( s ) ] - a (11.3) Y Also, * - l+ac [Asinh(3) + Bcosh(%)] l + a (11.4) dX z/t l + a We now recall the boundary conditions: X = 0, du/dX = 0, X = 1 and u = 1. From eqn. (11.4), we immediately obtain B = 0 and from eqn. (11.3) we obtain A = L s e c h ( s ) 1 +a Hence the expression for the concentration profile, eqn. (11.3), reduces to u = (1 + a)cosh ( - l y a ) s e c h ( z ) - a (11.5) which is eqn.(33). Substitution of the expression for A into eqn. (11.4), setting B = 0 and setting X = 1 immediately results in eqn. (34) for the normalized current response. Appendix 111 In this Appendix we show how eqns. (41) and (42) may be derived. We begin with eqns. (11.3) and (11.4) derived in Appendix 11. We use the following boundary conditions: X = 0, du/dX = 0, X = 1 u = ul, X = 1 and du/dX = Y (1 - ul). Again, using the first boundary condition in eqn. (11.3), we note that B = 0. Using the second boundary condition we can derive an expression for the constant A by noting that eqn. (11.3) reduces to u1 = - (1 + a)2 A c o s h ( 2 ) - Y and so we note that a (111.1)Analyst, June 1996, Vol. 121 73 1 Using the third boundary condition and eqn.(11.4), we obtain (111.2) Substituting eqn. (111.1) into eqn. (111.2), we can, after some algebraic manipulation, obtain the following expression for U]: v + * tanh (2) l + a which is eqn. (42). Now, substitution of eqns. into eqn. (11.3), and noting that B = 0, results (111.3) (111.1) and (111.3) in the concentra- tion profile outlined in kqn. (41). The steady-state current response y presented in eqn. (43) can be readily obtained by noting that y = av (1 - ul) and using eqn. (111.3) directly. References 1 2 3 4 5 6 7 8 9 10 1 1 12 13 Hillman, A. R., in Electrochemical Science and Technology of Polymers, ed. Linford, R. G., Elsevier, Amsterdam, 1987, pp. Lyons, M. E. G., Annu. Rep. C., R . SOC. Chem., 1990,87, 119. Lyons, M. E. G., in Electroactive Polymer Electrochemistry: Part I , Fundamentals, ed.Lyons, M. E. G., Plenum Press, New York, 1994, Lyons, M. E. G., Analyst, 1994, 119, 805. Evans, G. P., in Advances in Electrochemical Science and Engineer- ing, ed. Gerisher, H., and Tobias, C. W., VCH, Weinheim, 1990, vol. Wring, S. A., and Hart, J. P., Analyst, 1992, 117, 1215. Molecular Design of Electrode Surfaces, ed. Murray, R. W., Techniques of Chemistry Series, vol. XXII, Wiley-Interscience, New York, 1992. Biosensors and Chemical Sensors, Optimizing Performance Through Polymeric Materials, ed. Eldeman, P. G., and Wang, J., ACS Symposium Series, No. 487, American Chemical Society, Wash- ington, DC, 1992. Andrieux, C. P., and Saveant, J. M., in Molecular Design of Electrode Surfaces, ed. Murray, R. W., Techniques of Chemistry, vol. XXII, Wiley-Interscience, New York, 1992, pp. 207-270. Beck, F., and Schulz, H., Electrochim. Acta, 1984, 29, 1569. Gorton, L., Torstenssen, A., Jaegfeldt, H., and Johansson, G., J. Electroanal. Chem., 1984, 161, 103. Gorton, L., Johansson, G., and Torstensson, A., J. Electroanal. Chem., 1985,196, 81. Gorton, L., J. Chem. SOC., Faraday Trans. I , 1986,86, 1245. 103-29 1. pp. 237-374. 1, pp. 1-74. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Andrieux, C. P., and Saveant, J. M., J . Electroanal. Chem., 1984,171, 65. Burke, L. D., and O’Leary, W. A., J. Electrochem. SOC., 1988,135, 1965. Lyons, M. E. G., Lyons, C. H., McCormack, D. E., McCabe, T., Breen, W., and Cassidy, J. F., Anal. Proc., 1991, 28, 104. Lyons, M. E. G., Lyons, C. H., Michas, A., and Bartlett, P. N., J . Electroanal. Chem., 1993, 351, 245. Lyons, M. E. G., Lyons, C. H., Michas, A., and Bartlett, P. N., Analyst, 1992, 117, 1271. Lyons, M. E. G., McCormack, D. E., Michas, A., Lyons, C. H., and Bartlett, P. N., Key Eng. Muter., 1992, 72-74, 477. O’Sullivan, E. J. M., and White, J. R., J . Electrochem. SOC., 1989, 136, 2576. Shieh, D. T., and Hwang, B. J., J . Electrochem. SOC., 1995, 142, 816. Laidler, K. J., Chemical Kinetics, Harper and Row, New York, 3rd edn., 1987, pp. 400-404. Bartlett, P. N., and Whitaker, R. G., J. Electroanal. Chem., 1987,224, 27; 37. Bartlett, P. N., Tebbutt, P., and Tyrrell, C. H., Anal. Chem., 1992,64, 138. Bartlett, P. N., Tebbutt, P., and Whitaker, R. G., Prog. React. Kinet., 1991, 16, 55. Albery, W. J., Bartlett, P. N., Driscoll, B. J., and Lennox, R. B., J. Electroanal. Chem., 1992, 323, 77. Lyons, M. E. G., Fitzgerald, C. A., and Smyth, M. R., Analyst, 1994, 119, 855. Lyons, M. E. G., Fitzgerald, C. A., and Bannon, T., unpublished work. Britz, D., Digital Simulation in Electrochemistry, Lecture Notes in Chemistry Series, vol. 23, Springer, Berlin, 1981. Bard, A. J., and Faulkner, L. R., Electrochemical Methods: Fundamentals and Applications, Wiley, New York, 1980, pp. 675-697. Aris, R., The Mathematical Theory of Reaction and Diffusion in Permeable Catalysts, Clarendon Press, Oxford, 1975. Albery, W. J., Cass, A. E. G., and Shu, Z. X., Biosens Bioelectron., 1990, 5, 367. Engasser, J. M., and Horvath, C., in Applied Biochemistry and Bioengineering: Immobilized Enzyme Principles, ed. Wingrad, L., Katchalski-Katzir, E., and Goldstein, L., Academic Press, New York, Engasser, J. M., and Horvath, C., Biotechnol. Bioeng., 1974, 16, 909. Carr, P. W., Anal. Chem., 1977, 49, 799. Brady, J. E., and Carr, P. W., Anal. Chem., 1980, 52, 977. Tran-Minh, C., and Broun, G., Anal. Chem., 1975,47, 1359. Lyons, M. E. G., Fitzgerald, C. A., and Smyth, M. R., Analyst, to be submitted. Lyons, M. E. G., Adv. Chem. Phys., 1996,94,297. 1976, V O ~ . 1, pp. 127-221. Paper 510 7855 E Received December 4 , 1995 Accepted February 9,1996

ISSN:0003-2654    

DOI:10.1039/AN9962100715

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (733-741) 733 Kinetic Separation of Amperometric Sensor Responses* Robert J. Forster School of Chemical Sciences, Dublin City University, Dublin 9, Ireland The electrochemical behaviour of adriamycin and quinizarin monolayers, which are adsorbed on mercury microelectrodes and are in contact with aqueous electrolyte solutions, were studied by means of cyclic voltammetry and high-speed chronoamperometry. When the solution pH is below 6, reduction of the quinone moieties is a rapid, electrochemically reversible, process that is consistent with a nearly ideal two-electron, two-proton redox reaction involving a surface-confined redox couple. The potential dependence of the redox composition follows the Nernst equation with the expected theoretical slope.The adsorption thermodynamics follow the Langmuir isotherm over the concentration range 2 X 10-8 to 2 x 10-5 moll-'. Limiting surface coverages, rs of (1.1 f 0.1) X 10-10 and (1.3 f 0.1) X 10-10 mol cm-2 and energy parameters, p, of (4.5 f 0.3) x lo5 and (6.1 f 0.5) X 105 1 mol-1 were observed for adriamycin and quinizarin monolayers, respectively. Microsecond time-scale chronoamperometry was used to probe both the rate of heterogeneous electron transfer to the adsorbed anthraquinone moieties and their surface coverages. Standard heterogeneous electron transfer rate constants, k", as measured at a solution pH of 3.5, are (3.1 f 0.2) X 104 and (1.0 f 0.1) X 103 s-1 for adriamycin and quinizarin, respectively. The formal potentials of adriamycin and quinizarin are almost identical.Therefore, binary monolayers, formed by simultaneous adsorption of both anthraquinones exhibit only a single voltammetric peak. In these circumstances, traditional electroanalytical techniques cannot be used to determine the surface coverages of the individual species. However in potential step experiments, three single exponential current decays are separated on a microsecond time-scale. These decays correspond to dou ble-layer charging and heterogeneous electron transfer to the adriamycin and quinizarin redox centres, respectively. This kinetic separation of the Faradaic responses allows the surface coverages of the individual components within the monolayer to be determined. Despite their identical formal potentials, the concentrations of the two anthraquinones in solution were determined by combining information about heterogeneous kinetics and adsorption thermodynamics.Keywords: Amperometric sensor responses; adsorption; microelectrode; quinone; kinetic resolution Introduction Voltammetry is a highly sensitive technique that can be applied to a wide range of inorganic and organic systems.' In recent years, numerous modifications to the basic polarographic method have been developed to extend the number of * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. waveforms available and to broaden the range of media in which electroanalysis can be successfully performed. In particular, the development of microelectrodes, whose critical dimension is in the micrometre range, has opened up tremen- dous opportunities to perform electrochemical measurements in small domains using poorly conducting samples and to do so at high speed.24 The properties of microelectrodes have been exploited extensively in ac impedance and pulse voltammetry to measure rate constants for distinct steps occurring within electro- chemical processes on the basis of their different time constants.4 However, when attempting to determine the concen- tration of a particular redox-active species in a complex mixture, one typically separates the response of the target analyte from redox-active interferences on the basis of different formal potentials.1,5-7 In fact, there have been relatively few reports on using differences in electrochemical reactivity, i.e., electrode kinetics, to determine the concentration of a target analyte by separating its voltammetric response from that of an interferent on the basis of different time constants for the two reactions.8.9 Since the width of the electrochemical response for any species is a sizeable fraction of the potential scale, relying on the potential axis alone to generate a selective response provides only a very limited ability to resolve an analyte's response from that of an interfering species.If one can use the time axis in addition to the potential axis, separating the analyte's response from that of an interfering species becomes considerably more likely. This time-resolved approach ought to benefit significantly from the dramatic expansion in the range of time-scales that can be resolved and exploited in electro- chemistry with the advent of high speed instrumentation and microelectrodes.In this paper, we demonstrate the feasibility of using kinetic, rather than thermodynamic, information to determine success- fully the concentration of electroactive species. The test system chosen challenges traditional electroanalytical approaches as the formal potentials of both redox couples in the mixture are identical, and the potential axis cannot be used to generate a selective response. However, the concept explored here can be employed in any situation where electrochemical responses overlap on the potential axis. In general, since the kinetics of the overlapping analyte and interference responses are potential dependent, it should be possible to find a potential that will allow a kinetic resolution.The ability to control instrumentally the driving force, and hence the rate, of a reaction represents a distinct advantage of electrochemistry over other analytical approaches, e.g., spec- troscopy. In hardly any other use of time-resolved analysis does one have the power to vary the kinetics in a simple, instrumentally based manner so as to optimize the separation achieved on the time axis. In this work, we have used binary monolayers containing electroactive centres that have identical formal potentials, but different heterogeneous electron transfer rate constants k, as model systems. Monolayers offer a more controlled environ- ment and avoid the difficulties of diffusion-limited mass734 Analyst, June 1996, Vol.121 transfer, which complicate the investigation of high-speed kinetics for solution species. ' G 2 0 Single-component and binary monolayers have been formed by the spontaneous adsorption or co-adsorption of adriamycin and quinizarin on the surface of mercury microelectrodes. Not only are the measurements reported important for developing new approaches to electro- analytical chemistry, but also the spontaneous adsorption approach used gives rise to quinonoid layers whose composition varies significantly. Therefore, these supramolecular assem- blies are useful models for understanding photochemical and biological systems where the electron-transfer reactions of quinone and hydroquinone compounds play important roles.21-23 With electroactive monolayers that are immobilized on electrode surfaces, the integrated charge under the voltammetric wave is usually taken as a measure of the surface coverage.l2-20 However, in order for this approach to be applicable to multicomponent systems, the formal potentials of the different constituents must be separated by several tens of millivolts. This requirement is not satisfied for the binary monolayers con- sidered here since the formal potential with the quinone- hydroquinone redox reaction is the same with both adriamycin and quinizarin. Therefore, a different strategy is proposed that separates the Faradaic responses based on differences in the kinetic properties of the two redox centres. The possibility of extending this kinetic approach to determine bulk concentra- tions in solution by combining information about adsorption thermodynamics and surface coverage is also considered. Experimental Procedures Apparatus Electrochemical cells were of conventional design and were thennostated within k0.2 "C using a Julabo (Seelbach, Germany) F 1 0-HC refrigerated circulating bath.All potentials are quoted with respect to a BAS (West Lafayette, IN, USA) Ag/AgCI gel-filled reference electrode, the potential of which was 35 mV more positive than that of the saturated calomel electrode (SCE). In order to prevent chloride contamination from the reference electrode, the electrolytic solution was separated from the reference electrode by a salt bridge filled with saturated KN03. Cyclic voltammetry was performed using a computer-controlled EG&G Princeton Applied Research (Princeton, NJ, USA) Model 273 potentiostat/galvanostat and a conventional three-electrode cell.All solutions were degassed using nitrogen, and a blanket of nitrogen was maintained over the solution during all experiments. As described previously,24 a custom-built function genera- tor-potentiostat, which had a rise time of less than 10 ns, was used to apply potential steps of variable pulse width and amplitude directly to a two-electrode cell. The function generator of this potentiostat allows potential steps to be applied to the cell at frequencies up to 200 MHz. There are two channels available that operate off the same internal clock, each with 7680 memory points. The voltage output range is approximately k5 V with ten-bit resolution.The output also has a variable attenuator and voltage offset so that one can maintain ten-bit resolution when voltage ranges less than the maximum are used. A Pt foil and an Ag/AgCl reference electrode were combined to form a counter electrode. The foil lowered the resistance and provided a high-frequency path. No shift in the formal potential of [Os(bpy)2C12] was observed when the Ag/AgCl reference electrode was combined with the Pt foil. Microelectrodes were fabricated from platinum microwires (Goodfellow Metals, Cambridge, UK) with radii between 5 and 25 pm by sealing them in soft glass using a procedure described previ0usly.~5~~6 Microdisc electrodes were exposed by remov- ing excess glass using 600 grit emery paper followed by successive polishing with 12.5, 5, 1,0.3 and 0.05 pm alumina.The polishing material was removed between changes of particle size by sonicating the electrodes in de-ionized water for at least 5 min. The polished electrodes were electrochemically cleaned by cycling in 0.1 moll-' HC104 between potential limits chosen first to oxidize and then to reduce the surface of the platinum electrode. Finally, the electrode was cycled between -0.300 and 0.700 V in 0.1 moll-' NaC104 until hydrogen desorption was complete. Mercury hemispherical microelectrodes, that were used exclusively in the experiments reported here, were formed by electrodeposition of mercury on platinum electrodes that were prepared as described above. The deposition solution contained 6 mmol 1-l mercury(1) nitrate, 0.5% HN03 and 1.0 mol I-' KN03.Sufficient charge was passed at 0.000 V to form a mercury droplet on the platinum surface whose radius was at least twice as large as that of the platinum microwire.27 The area of these mercury microelectrodes was determined from cyclic voltammetry performed at a sufficiently high scan rate so as to obtain a response dominated by semi-infinite linear diffusion, rather than radial diffusion, using [Os(2,2-bipyridyl)2Cl2]2+ as a solution-phase electrochemical probe. RC cell time constants measured in blank electrolyte solution were between 50 and 500ns, depending on the electrode radius and the sup- porting electrolyte concentration. The interfacial kinetics were measured only at times greater than about 5-10 RC.Materials and Procedures Adriamycin hydrochloride was obtained from Sigma (St. Louis, MO, USA) and was used as received. Quinizarin (1,4-di- hydroxyanthraquinone) was obtained from Aldrich (Milwaukee, WI, USA) and was purified by recrystallization from methanol. The recrystallization and subsequent storage were carried out in the dark to avoid photochemical decompo- sition. Other chemicals were of analytical-reagent grade. Water was purified using a Milli-Q filtration system (Millipore, Milford, MA, USA). Monolayers were formed by immersing the mercury microelectrodes in the supporting electrolyte solution containing the anthraquinone of interest at the desired concentration. All subsequent measurements on the monolayers were performed with this concentration of anthra- quinone present in solution.The highest solution concentration was in the micromolar range, which means that the maximum contribution from diffusion to the over-all current observed in chronoamperometry or cyclic voltammetry is typically less than 5%. The time evolution of the surface coverage was monitored using cyclic voltammetry until the equilibrium surface coverage was attained. Binary monolayers were formed by immersing the micro- electrode in an electrolytic solution containing both anthraqui- nones. The ratio of adriamycin to quinizarin within the monolayers was controlled by altering the concentrations of the two anthraquinones in solution. The solution concentration ratio of the competing adsorbates was varied from 0.9 to 0.1 to produce monolayers with a range of compositions. Results and Discussion General Electrochemical Properties Fig.1 shows the scan-rate dependence of cyclic voltammo- grams observed for mercury microelectrodes of radius 30 pm immersed in 5 pmol 1- 1 solutions of adriamycin and quinizarin, respectively, with 1.0 mol 1-1 HC104 as the supporting electrolyte. Despite the relatively high scan rates employed (5-50 V s-I), the voltammograms observed are consistent with those expected for an electrochemically reversible reaction involving a surface-confined ~pecies.289~9 For example, the peak height scales linearly with the scan rate Y, unlike the v1/* dependence expected for a freely diffusing species.5-7 There-Analyst, June 1996, Vol. 121 735 fore, it appears that adriamycin and quinizarin adsorb on the surface of a mercury microelectrode to give an electroactive film.Where there are no lateral interactions between adsorbates, and a rapid equilibrium is established with the electrode, a zero peak-to-peak splitting, AE,, and a full width at half maximum, FWHM, of 45.3 mV are expected for a reaction in which two electrons are transferred.28.29 This behaviour is observed for both adriamycin and quinizarin films, at least at scan rates -0 P h I -300 I ' -500 Fig. 1 Cyclic voltammogram for 30 pm radius mercury microelectrodes immersed in (A) a 5 pmol l-1 solution of adriamycin and (B) a 5 pmol I-' solution of quinizarin. Scan rates from top to bottom: 50,20,10 and 5 V s- 1 . Supporting electrolyte: 1.0 mol 1-1 HC104. Cathodic currents are up and anodic currents are down.Initial potential: -0.700 V. below 5 V s-1. Therefore, in agreement with previous reports,30-37 Fig. 1 suggests that the over-all redox reaction involves transfer of two electrons to surface immobilized anthraquinone moieties (Scheme 1). The cyclic voltammetric data for adriamycin suggest that heterogeneous electron transfer across the mercury-film interface is a relatively fast process because, even for scan rates up to 50 V s-l, the integrated charges of the anodic and cathodic peaks are identical and AE, is less than 10 mV.30-39 However, for quinizarin films, AEp is not negligible for scan rates above 5 V s-1 and the magnitude of the separation increases with increasing scan rate. This observation suggests that heterogeneous electron transfer is a relatively slower process for quinizarin films.For low scan rates, where the voltammetric response is not complicated by the interfacial kinetics, the FWHM is within 5 mV of that predicted by theory, indicating that the formal potentials of the individual adsorbates are essentially identical and that the local microenvironments are highly uniform. Fig. 1 shows that the formal potentials of adriamycin and quinizarin are essentially identical. This is an important observation since E O' is highly sensitive to the local dielectric constant within the film.4w5 That the formal potentials of both anthraquinones are similar suggests that the presence of a sugar moiety in adriamycin does not significantly influence the structure of the adsorbed film, perhaps indicating that the membrane is highly solvated.The importance of proton availability in dictating the formal potential in these systems has been investigated by probing the pH dependence of the formal potential. Over the pH range 1-6, the formal potentials for both anthraquinone films shift by -57 f 3 mV per pH unit. This behaviour is consistent with reduction of the adsorbed anthraquinone moieties proceeding by a two-electron, two-proton reduction me~hanism.~~50 The formal potentials for both anthraquinones do not become discernibly different from one another over the pH range 1-6. The electrochemical behaviour of the adsorbed species can be further probed by determining the redox composition as a function of the applied p ~ t e n t i a l . ~ ~ For an ideal system involving transfer of two electrons, the ratio of oxidized to reduced species should change by approximately an order of magnitude for each 29.5 mV change in the applied potential about the formal potential E In the experiments reported here, the potential was stepped in a positive direction from a potential 100 mV more negative than E"' using a pulse amplitude Eamp.Initially Earn, was 10 mV, and it was increased by 10 mV between successive experiments until the potential was stepped to a value 100 mV more positive than E O ' . By OH OH 0 Scheme 1736 Analyst, June 1996, Vol. I21 1.2E-10 N E - 8.OE-11 .. L measuring the charge passed in each of these steps, and determining the charge passed in an experiment in which the adsorbed film is fully oxidized, the functional relationship between the ratio of the oxidized to reduced forms of the anthraquinone and the applied potential, could be probed. Fig.2 illustrates representative data for single-component films of both adriamycin and quinizarin formed in 5 vmol l-1 solutions. The slopes of these plots, 33 & 4 mV per decade, are indistinguishable from those predicted for a two-electron transfer reaction by the Nernst confirming that the electrochemical response of these films is nearly ideal under the experimental conditions employed. - - Adsorption Isotherms After correcting for the contribution from double-layer charg- ing, the total charge withdrawn or injected to oxidize or reduce the layer can be determined from the area under the voltam- metric peaks. From the charge under the wave and the geometric electrode area, the surface coverage of the anthraquinone molecules can be established.To define the adsorption isotherm, the surface coverages of adriamycin or quinizarin at equilibrium were determined as the solution concentration was systematically varied. Fig. 3 shows the change in surface coverage as the solution concentrations of the anthraquinones are individually varied from 2 X 10-8 to 15 X 10-6 mol I-'. I f' I Fig. 2 Redox composition of 0 adriamycin and (0) quinizarin mono- layers as a function of the applied potential. Supporting electrolyte: 1 .O moll-' HC104. b 1.6E-10 Y 4.OE-11 ' 5 10 15 20 Concentration/pmol I-' Fig. 3 cyclic voltammetry of adriamycin and concentrations. Supporting electrolyte: 1 .O mol 1- I HC104.Relationship between the surface coverage as determined using quinizarin and their bulk The Langmuir isotherm5-7,52,53 describes equilibrium ad- sorption where there are no lateral interactions between the adsorbed molecules, and the limiting surface coverage is dictated simply by the size of the adsorbate. That the voltammetric responses shown in Fig. 1 are essentially ideal suggests that the Langmuir isotherm may be an appropriate description of adsorption in these systems. This isotherm is described by the following expression:5-7.52~~3 (1) where rl is the surface excess of species i at equilibrium, TS is the surface excess of species i at saturation, is the energy parameter and ci is the concentration of species i in solution. In the following discussion, activity effects are incorporated into the energy parameter.The lines shown in Fig. 3 are the best fits provided by the Langmuir isotherm to the experimental data, and this figure shows that satisfactory agreement between experiment and theory is obtained. The Langmuir isotherm predicts that a plot of crKl versus c, should be linear, and that the saturation surface coverage and the energy parameter can be obtained from the slope and intercept, respectively. In all instances, plots of this type were linear (correlation coefficients > 0.995) for both adriamycin and quinizarin films. Modelling the experimental data using a Frumkin i~otherm,5-~,~~,5~ which considers adsorbate-adsor- bate interactions, gave interaction parameters that were close to zero.This observation suggests that any interactions between the adsorbates do not depend on the surface coverage, and hence exert little influence over the thermodynamics of adsorption. It is possible that incomplete monolayer coverages contain dense islands of adsorbate and regions of unmodified mercury, making the lateral interactions independent of the surface coverage. The values of rs provided by the Langmuir isotherm are (1.1 k 0.1) X and (1.3 f 0.1) X 10-lO mol cm-2 for adriamycin and quinizarin monolayers, respectively. The aver- age limiting surface coverage [( 1.2 & 0.1) X 10-10 mol cm-2)] corresponds to an average area of occupation per molecule of approximately 140 A2. This value is consistent with the value of I30 A2 found by Soriaga and Hubbard31 for a flat orientation of anthraquinone derivatives on platinum.It appears that both adriamycin and quinizarin adsorb on the electrode surface with their anthraquinone moiety oriented parallel to the electrode surface. However, it should be noted that this area occupied per molecule is significantly smaller than that found for other anthraquinone derivatives adsorbed on mercury, where areas of the order of 180-200 A2 have been reported.30-37 The energy parameters for these two systems are also similar, and have values of (4.5 f 0.3) X lo5 and (6.1 k 0.5) X lo5 1 mol- 1 for adriamycin and quinizarin, respectively. That the formal potentials, peak widths and strengths of adsorption are essentially identical for these two anthraquinones indicates that the thermodynamics of adsorption and lateral interactions are similar in both circumstances.We probed the formation kinetics of adriamycin monolayers by recording repetitive high-speed cyclic voltammograms following immersion of a clean mercury microelectrode in solutions containing adriamycin at various concentrations. Fig. 4 illustrates the dependence of the cyclic voltammetric response on the accumulation time at an adriamycin concentra- tion of 5 pmol I-*. These data show a gradual increase in the peak current corresponding to an increasing surface coverage of adriamycin molecules with increasing accumulation time. Fig. 4 demonstrates that monolayers assemble rapidly. In fact, at this anthraquinone concentration, the equilibrium surface coverage is attained within 500 rns.This time constant for adsorption is significantly smaller than that predicted by the linearized adsorption isotherm describing linear diffusion conditions.'-7 This model suggests that equilibrium coverage would only be obtained for times greater than approximately 1000 s. It appears r,/(rs - rl) = P A737 Analyst, June 1996, Vol. 121 that the small size of the electrodes used in this study enhances the rate of adriamycin diffusion to the electrode surface, causing rapid monolayer formation. The monolayer surface coverage depends on the square root of the accumulation time, and increases with an increasing bulk concentration of adriamycin. These two observations suggest that analyte diffusion, rather than the kinetics of adsorption, dictate the rate of monolayer formation in these systems.In the following section, we report on the use of high-speed chronoamperometry to measure the rate of heterogeneous electron transfer from the underlying mercury electrode to the adsorbed anthraquinone redox sites. The ultimate objective is to compare the rate constants obtained for single-component systems with those obtained for mixed structures. Moreover, the ability of chronoamperometry to determine surface coverages within single-component monolayers is considered before dealing with more complex systems. Chronoamperometry For an ideal electrochemical reaction involving a surface-bound species, the Faradaic current following a potential step that changes the redox composition of the monolayer exhibits a single exponential decay with time according to5-10,14 iF(t) = kQ exp( -kt) ( 2 ) where k is the apparent rate constant for the over-all reaction and Q is the total charge passed in the redox transformation.Fig. 5 shows the current response for a dense adriamycin monolayer following a potential step from -0.700 V to a potential E of -0.350 V, where the pH of the supporting electrolyte is 4.5. This potential step corresponds to an overpotential (q=E - E") of +I00 mV. Fig. 5 shows that two current decays, corresponding to double-layer charging and Faradaic current flow, respectively, are separated on a 20 ps time-scale. These two processes are time resolved because the time constant for double-layer charging is much smaller than that of the Faradaic reaction. In this study, measurement of the electron-transfer rates was restricted to those circumstances where the time constant of double-layer charging is at least five times smaller than that of the Faradaic reaction.Sufficiently rapid charging of the modified interface to satisfy this condition was achieved by carefully selecting the electrode radius. The cell time constant, RC, where R is total cell resistance and C is the double-layer capacitance, measured from the data presented in Fig. 5 , is approximately 410 ns. The measured resistance is approximately 3500 Q, which is in reasonable agreement with the theoretical value of 2950 Q calculated using a value of 8.5 S2-1 m-1 for the specific conductivity of the supporting electrolyte solution. The linearity of the semi-log plot shown in the inset of Fig.5 indicates that heterogeneous electron transfer associated with oxidation of the adriamycin monolayer is a first-order process. The unusual linearity also indicates that a single rate constant predominates over the time required to collect more than 95% of the total Faradaic charge. This observation is significant, and contrasts with other spontaneously adsorbed or self-assembled monolayers, notably pure ferrocene alkanethiol mono- layers.40-45 In these ferrocene-containing systems, a hetero- geneous distribution of rate constants is observed and diluent electroinactive spacer molecules must be added to obtain monodisperse behaviour. That a single rate constant predom- inates for these anthraquinone monolayers suggests that the films are highly ordered and that there are either very weak lateral interactions between the adsorbates, or that there is very rapid interconversion between dissimilar sites.Factors other than disorder can cause non-linear semi-logarithmic plots to be observed. If heterogeneous electron transfer were limited by the motion of protons rather than by electron transfer, then negative deviations from linearity would be expected at short times. A similar negative deviation would arise if ohmic effects were significant.5-7 For the situations reported here, the electrode radius was selected so that the iR drop did not exceed 5 mV even in dilute electrolyte solutions. That ohmic effects are largely absent in these systems is confirmed by the ideal cyclic voltammetry and chronoamperometry, in addition to the observation that the standard heterogeneous electron-transfer rate constants (see below) for both adriamycin and quinizarin monolayers remain constant at (3.1 f 0.2) x 104 and (1 .O Lt 0.1) X lo3 s-l, respectively, as the concentration of the supporting electrolyte is systematically varied from 0.1 to 1 .O moll-' while the solution pH is kept constant at 3.5.The presence of only weak lateral interactions and extensive solvation of the adsorbates are probably the primary reasons why nearly ideal electrochemical responses are observed. However, it is perhaps useful to consider how differences in the monolayer charge density between ferrocene alkanethiol mono- layers and these anthraquinone systems might influence the electrochemical responses.That electron transfer is coupled to I , 600 A 1400 200 0 -0.50 -0.60 t -400 Fig. 4 Dependence of the voltammetric response of a 30 pm radius mercury microelectrode on the accumulation time following immersion in a 5 pmol 1-I adriamycin solution. Scan rate: 50 V s-I. Accumulation times, from top to bottom: 300, 200, 100 and 50 ms. Supporting electrolyte: 1 .O moll- I HC104. I -12.3 t I 4 I T -13.2 I 0 5 10 15 20 I I I 0 6 12 18 Time/ps Fig. 5 Current response for a 10 pm radius mercury microelectrode immersed in a 5 pmol l-1 solution of adriamycin following a potential step from -0.700 to -0.350 V. Supporting electrolyte: 1.0 mol 1-I perchlorate at pH 4.5. The inset shows the semi-logarithmic plot for data between the marks on the current-time transient.The time axis on the inset is referenced to the leading edge of the potential step.738 Analyst, June 1996, Vol. 121 proton transfer in the structures considered here means that both the initial and final states, before and after reduction of the monolayer, are uncharged. This situation contrasts with other redox-active monolayers, notably those based on transition metal complexes, where a layer of charge compensating counter-ions must be assembled in solution. In those circum- stances, the charge density of the monolayer changes con- tinuously as heterogeneous electron transfer proceeds. This variation may cause the reaction free energy to change throughout the reaction, leading to a multi-exponential decay of the Faradaic current.54,55 The data presented here do not allow us to reach any conclusion as to whether electron and proton transfer in these systems proceeds by a concerted or stepwise mechanism.However, we note that if electron and proton transfer are concerted in these anthraquinone monolayers, then the monolayer is uncharged throughout the redox transforma- tion, perhaps contributing to the unusually linear response shown in Fig. 5. As indicated by eqn. (2), the absolute slope of the linear regression line for the semi-logarithmic plot in Fig. 5 represents the heterogeneous electron-transfer rate constant k. The value obtained, 4.2 X lo4 s-l, confirms the rapid nature of electron and proton transfer within these supramolecular assemblies. Table 1 shows the variation of the heterogeneous rate constant measured at q = 50 mV in an electrolyte solution of pH 3.5 as the surface coverage is systematically varied.These data show that whereas the interfacial kinetics for the adriamycin mono- layers are approximately independent of the surface coverage, k increases by a factor of 1.7 as the surface coverage of quinizarin is changed from approximately 1 X lo-" to 1 X 10-10 mol cm-2. As the heterogeneous electron-transfer rate depends exponentially on the separation of the electronic manifolds on the two sides of the interface,5"58 the insensitivity of k to changes in the adriamycin surface coverage suggests that the average electron-transfer distance does not depend on the surface coverage of adriamycin, i.e., the molecular orientation does not depend on the packing density.That the rate of heterogeneous electron transfer increases with increasing quinizarin coverage may suggest that the growth mechanisms for these two monolayers may be different. For example, it is possible that partial adriamycin monolayers contain dense islands of adsorbates separated by regions of modified mercury- solution interface. This distribution of adsorbates would lead to a heterogeneous electron-transfer rate that was independent of the surface coverage. In contrast, if partial monolayers of quinizarin involve the adsorbates being randomly distributed over the entire mercury surface, then the lateral interactions would change on going from partial to dense monolayers. This change in the nature and extent of lateral interactions could lead to electron-transfer rate constants that depend on the surface coverage.Table 1 Dependence of the heterogeneous electron-transfer rate constant, k, as measured at an overpotential of 50 mV on the surface coverage of adriamycin or quinizarin. Surface coverages determined from the area under the cyclic voltammetric peak. All measurements were made at 25 "C; electrolyte pH = 3.5* rAdrinrnycin/ mol cm-2 1.1 x 10-11 2.0 x lo-" 4.3 x 10-11 5.9 x lo-" 8.2 x 1.1 x 10-10 lop5 kq = SO rnv/ S-1 0.8 (0.03) 0.8 (0.04) 0.9 (0.05) 1.0 (0.03) 1 .O (0.03) 1.0 (0.04) rquinizarin/ lop3 kq = 50 mV/ mol cm-2 S-1 1.3 x lo-" 1.4 (0.06) 2.4 X 1O-Il 1.5 (0.06) 4.8 X 10-11 1.8 (0.08) 6.3 X 1O-lI 2.2 (0.12) 8.8 X 2.3 (0.10) 1.3 x 10-10 2.4 (0.09) * Numbers in parentheses are the standard deviations for measurements on at least three individual monolayers. Table 1 indicates that the heterogeneous electron-transfer rate for adriamycin monolayers is about 40 times smaller than that found for quinizarin systems. This large difference in electron- transfer dynamics may be due to the presence of the sugar moiety in adriamycin.It is likely that differences in the extents of intermolecular hydrogen bonding play an important role in dictating the magnitude of the heterogeneous electron-transfer rates. Our previous investigations into adsorbed anthraquinone- disulfonic acid films show that relatively minor changes in the substitution pattern of the anthraquinone, e.g., sulfonic acid groups in the 1,5- as opposed to the 2,6-positions, can decrease the heterogeneous electron-transfer rate by a factor of 50.9 The ultimate objective of this work is to probe the possibility that the large difference in heterogeneous rate constants can be exploited to determine the surface coverages of both adriamycin and quinizarin when they are co-adsorbed on an electrode. However, before dealing with multicomponent assemblies, the ability of high-speed chronoamperometry to determine surface coverages within single-component monolayers is demon- strated.The absolute intercept of the semi-logarithmic plot in Fig. 5 represents the product ln(kQ), allowing the charge passed in the redox transformation to be detem~ined.~?g As demonstrated by Fig. 2, an overpotential of -100 mV decreases the number of reduced species within the monolayer to less than 1% of the total.Therefore, this potential step effectively causes complete oxidation of the film, and the full surface coverage r can be calculated from the intercept of the semi-logarithmic inset Fig. 5 using the relationship28.29 r = QInFA (3) where n is the number of electrons transferred, F is the Faraday constant, and A is the electrode area. The charge obtained from the intercept in the semi-logarithmic plot in Fig. 5 is 1.1 X C, which corresponds to a surface coverage of 8.0 X lo-" mol cm-2, given that the electrode area is 7.1 X 10-6 cm2. This value is within 10% of that found by cyclic voltammetry. Table 2 contains surface coverages determined by both cyclic voltammetry, T(CV), and chronoamperometry, T(CA), for single-component monolayers containing adriamycin or quini- zarin alone.That the surface coverages determined by cyclic voltammetry and chronoamperometry agree with one another to within 15% over this range confirms that high-speed chronoamperometry can be successfully used to determine surface coverages of redox species within single component Table 2 Cyclic voltammetric, T(CV), and high-speed chronoamperometric, T(CA), surface coverages for single-component adriamycin and quinizarin monolayers. All measurements were made at 25 "C" 0.93 (0.06) 2.11 (0.11) 2.98 (0.15) 4.12 (0.20) 5.23 (0.23) 5.93 (0.29) 7.43 (0.37) 8.55 (0.37) 1 1.20 (0.5 1) 0.98 (0.06) 2.05 (0.14) 2.77 (0.17) 4.44 (0.28) 5.64 (0.32) 5.41 (0.48) 7.60 (0.5 1) 8.82 (0.62) 10.52 (0.74) 0.81 (0.04) 2.13 (0.10) 3.23 (0.16) 4.18 (0.18) 5.29 (0.24) 5.93 (0.30) 7.1 1 (0.31) 8.59 (0.36) 13.02 (0.48) 0.88 (0.06) 2.13 (0.15) 3.44 (0.24) 4.02 (0.27) 5.45 (0.39) 5.53 (0.51) 7.21 (0.52) 8.43 (0.60) 12.11 (0.82) * Numbers in parentheses represent the standard deviations for meas- urements on at least three individual monolayers.+ Surface coverages determined from the area under the cyclic voltammetric peak. * Surface coverage determined using high-speed chronoamperometry. See text for details.Analyst, June 1996, Vol. 121 RC= 450 ns \ c y = 4.076 x - 10.68 739 monolayers. This agreement also indicates that all of the surface-confined molecules are redox active on a microsecond time-scale, i.e., relatively few, if any, sites are kinetically isolated. Table 2 also shows that the reproducibilities in the surface coverage measured by cyclic voltammetry and chrono- amperometry are similar, demonstrating the ability of micro- electrodes to provide rapidly high-quality analytical informa- tion.2-4 Mixed Monolayers Cyclic voltammetry represents a useful technique for measuring the surface coverage of redox-active material within mono- layers containing a single type of anthraquinone.However, the situation regarding mixed monolayers containing both adriamy- cin and quinizarin is distinctly different. Fig. 6 shows that a cyclic voltammogram of a monolayer deposited from a solution containing equimolar concentrations of both adriamycin and quinizarin exhibits only a single peak associated with the quinone-hydroquinone redox reaction. It is unlikely that only one of the anthraquinones adsorbs, given the similarity of the adsorption energy parameters for the two systems.Chrono- amperometry (see below) strongly suggests that these mono- layers do in fact contain both species. The voltammetric behaviour shown in Fig. 6 is expected as the formal potentials of the two types of redox centre are essentially identical (see above). However, as shown in Table 1, the heterogeneous electron transfer-rate constants are not identical for the two types of redox centre, at least not in single-component monolayers. Therefore, the possibility exists that differences in the heterogeneous electron-transfer rates to the two anthra- quinone derivatives could be exploited to determine their respective surface c0verages.~~9 Fig. 7 shows a semi-logarithmic plot for this binary monolayer following a potential step from -0.700 V to -0.340 V.This potential step oxidizes both the adriamycin and quinizarin centres within the monolayer. The most striking feature of Fig. 7 is that three single exponential decays are separated on a 90 ps time-scale. These three processes correspond to double-layer charging and oxidation of the two types of anthraquinone molecules within the supramolecular assembly.5-9 When an identical overpotential is applied, the rate constants obtained from the second and third decays in Fig. 7 are within 10% of those found for single-component mono- layers of adriamycin and quinizarin, respectively. That the same heterogeneous electron-transfer rate constants are observed for binary and pure monolayers suggests that lateral electron transfer between adjacent adriamycin and quinizarin molecules does not occur to any great extent, and that heterogeneous -- -30 Fig.6 Cyclic voltammogram of a 10 pm radius mercury microelectrode immersed in a 1.0 mol 1-1 HC104 solution containing 10 pmol 1-1 adriamycin and 10 pmol 1-I quinizarin. Scan rate: 20 V s-'. Cathodic currents are up and anodic currents are down. Initial potential: -0.700 V. electron transfer is the primary mechanism by which the redox composition of these mixed monolayers is changed. The surface coverages obtained from the intercepts of the semi-logarithmic plots using eqn. (3) are (4.9 f 0.2) X and (5.5 & 0.3) X 10-1' mol cm-2 for the adriamycin and quinizarin redox centres, respectively. Given that the concen- trations of the two anthraquinones in solution are identical, and that the energy parameters of adsorption are very similar, one would expect this binary monolayer to contain equal coverages of adriamycin and quinizarin.It is important to note that the total surface coverage of anthraquinone, (1.0 f 0.5) X mol cm-2, is indistinguishable from that found for dense monolayers containing only adriamycin or quinizarin. These observations strongly suggest that high-speed chronoamper- ometry can be used to determine surface coverages within binary monolayers even though the formal potentials of the two components are identical. The only requirement of this approach is that the interfacial kinetics of the individual components be sufficiently different that two single exponential decays are observed.Competitive Adsorption The Langmuir is0thenn-7~s*~~~ can be used to describe competitive adsorption of species i and j according to the following relationships: (4) ( 5 ) where Tr,s and r,,s represent the saturation coverages of i andj, respectively. Eqns. (4) and (5) can provide estimates of the surface coverages of both adriamycin and quinizarin within binary monolayers as the composition of the deposition solution is systematically varied. This synthetic approach of controlling not only the total anthraquinone concentration but also the ratio of the two derivatives gives rise to modified surfaces with a wide range of compositions that may be useful for sensor applications. Fig. 8 shows the relationship between the values of the surface coverages predicted by eqns.(4) and (5) and the values determined experimentally using high-speed chrono- amperometry. It can be seen that the predicted and experimental values are highly correlated, that a slope of nearly unity is observed and that the experimental data are not biased. These r/ = rl,sB/cI/(1 + B J I + (?l(j) r, = r.,,sB,c,/(l + P/Cr + B,/Lj) -9 -1 1 7 . h ..r v .- Y C 1 -1 3 -15 1 I I I I -5 20 45 70 95 Time/ps Fig. 7 Log (current) versus time response for a 30 pm radius mercury microelectrode immersed in a solution containing 10 pmol 1- adriamycin and 10 pmol I-' quinizarin following a potential step where the overpotential was 0.050 V. The supporting electrolyte is 1 .O moll- I HC104. The time axis is referenced with respect to the leading edge of the potential step.740 Analyst, June 1996, Vol.121 observations strongly suggest that chronoamperometry can be used to determine not only the total surface coverage of anthraquinone but also the relative amounts of adriamycin and quinizarin within the superstructures. A strategy is now presented that extends this kinetic approach to the quantification of species in solution. What is required is an expression for the concentration of species i in solution in terms of the saturation coverage, the coverages of the individual species and the energy parameter. This objective can be attained by substituting into eqn. (5) the expression for pici obtained from eqn. (4): (6) Therefore, by experimentally determining the partial coverages of adriamycin and quinizarin within binary monolayers using high-speed chronoamperometry, it should in theory be possible to use eqn.(6) to determine their unknown concentrations in solution. It is important to note that conventional electro- chemical approaches, * which depend on the individual compo- nents having different formal potentials for separation of their voltammetric responses, are completely unable to attain this objective. Binary monolayers have been deposited from solutions in which the concentrations of adriamycin and quinizarin have been systematically varied. The high-speed chronoampero- metric data obtained for these systems are similar to those shown in Fig. 7 allowing the surface coverages of the two components within the monolayer to be determined on the basis of different kinetic rather than thermodynamic properties.These data were then used in conjunction with eqn. (6) to calculate the bulk concentrations of the anthraquinones in solution. Fig. 9 shows the relationship between the actual anthraquinone concentrations in solution and those calculated from the anthraquinone surface coverages using the above kinetic approach. Fig. 9 shows that these data are highly correlated with a slope of nearly unity, indicating that this kinetic approach can be successfully applied to determine the concentrations of surface-active species in solution. Significantly, the linear regression line that models the experimental data shown in Fig. 9 has an intercept that is close to zero, demonstrating that the experimental data are not significantly biased.That this high-speed approach yields a strong unbiased correlation with rirj,s B i ( r j , s r i , s - rirj,s - rjri,s) c. = I .2E-10 / 1.OE-10 t '6 8.OE-11 1 4.0E-11 t 2.OE-11 1 / 4.OE-11 8.OE-I 1 1.2E-10 r/mol cm-2 Fig. 8 Relationship between the surface coverages, I', predicted by eqns.(4) and ( 5 ) and those determined experimentally using chrono- amperometry, T(CA). B, Total surface coverage of both anthraquinones; 0, surface coverage of adriamycin; and A, surface coverage of quinizarin. The solid line indicates the behaviour expected for a direct, unbiased correlation. The ratios of adriamycin to quinizarin within the mixed monolayers varies from approximately 0.15 to 0.85. the actual solution concentrations is especially significant when one considers the low concentrations of analyte involved. The solution concentrations are typically in the micromolar range, emphasizing another advantage of this method, namely that the analyte of interest is preconcentrated on the electrode surface.However, in spite of these potential advantages, it is important to note that the actual implementation of the proposed approach to the analysis of real samples containing several electroactive and electroinactive components, is likely to be problematic. Apart from the obvious requirement that the time constants for electron transfer to the target analyte and interferences be sufficiently different so as to achieve a kinetic separation, complications from co-adsorption of electro- inactive species such as surfactants and proteins need to be considered.Conclusions Binary monolayers containing both anthraquinones have been formed by simultaneous co-adsorption of the two molecules. The surface coverages of the two components cannot be determined using cyclic voltammetry because the formal potentials for the two molecules are experimentally indistin- guishable. However, in a potential step experiment, the Faradaic current flow arising from oxidation of the two types of redox centre can be separated in time because the two rates of interfacial electron transfer are different. This time-resolved electroanalysis allows the surface coverages of the two anthraquinones to be determined even though their formal potentials are essentially identical. This approach to electroanalysis has parallels with the technique of time-resolved fluorescence in photochemistry.59 It is probable that in the future, new electroanalytical techniques will be developed that exploit the ability of microelectrodes to provide high-quality kinetic information on very short time- scales.High-speed electrochemical measurements are also advantageous in terms of discriminating against interfering background Faradaic processes. For example, because many interfering redox reactions occur on a time-scale of milliseconds or even seconds, they can be effectively eliminated by making the analytical measurement on a microsecond time-scale. The financial support of Forbairt, the Irish Science and Technology Agency, under the Strategic Research Grant ST/95/305 is gratefully acknowledged. 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Linford, R. G., Elsevier, Amsterdam, 1987, vol. 1, ch. 5 , p. 103. Finklea, H. O., and Hanshew, D. D., J. Am Chem. SOC., 1992, 114, 3173. Chen, X., Zhuang, J., and He, P., J. Electroanal. Chem., 1989, 271, 257. Chidsey, C. E. D., Science (Washington, D. C., 1883-), 1991, 251, 919. Chidsey, C. E. D., Bertozzi, C. R., Putvinski, T. M., and Mujsce, A. M., J. Am. Chem. Soc., 1990,112,4301. De Long, H. C., Donohue, J. J., and Buttry, D. A., Langmuir, 1991, 7, 2196. Donohue, J. J., and Buttry, D. A., Langmuir, 1988, 4, 671. Weber, K., and Creager, S. E., Anal. Chem., 1994,66, 3164. Tender, L., Carter, M. T., and Murray, R. W., Anal. Chem., 1994,66, 3173. Rao, G. M., Lown, J. W., and Plambeck, J. A., J. Electrochem. Soc., 1978,125,534. Molinier-Jumel, C., Malfoy, B., Reynaud, J. A., and Aubel-Sadron, G., Biochem. Biophys. Res. Commun., 1978, 84, 441. Baldwin, R. P., Packeff, D., and Woodcock, T. M., Anal. Chem., 1981, 53, 540. Chaney, E. N., and Baldwin, R. P., Anal. Chem., 1982, 54, 2556. Kano, K., Konse, T., Nishimura, N., and Kubota, T., Bull. Chem. Soc., Jpn., 1984, 57, 2383. Forster, R. J., and Vos, J. G., Langmuir, 1994, 10, 4330. Laviron, E., Electroanal. Chem., 1982, 12, 53. Trassatti, S., J. Electroanal. Chem., 1974, 53, 335. Smith, C. P., and White, H. S., Anal. Chem., 1992, 64, 2398. Creager, S. E., and Weber, K., Langmuir, 1993, 9, 844. Li, T. T.-T., and Weaver, M. J., J. Am. Chem. Soc., 1984, 106, 6107. Miller, C., Cuendet, P., and Gratzel, M., J. Phys. Chem., 1991, 95, 877. Miller, C., and Gratzel, M., J. Phys. Chem., 1991, 95, 5225. Wolfbeis, 0. S., Fluorescence Spectroscopy: New Methods and Applications, Springer, New York, 1993. Paper 5107650A Received November 23,1995 Accepted January 15,1996

ISSN:0003-2654    

DOI:10.1039/AN9962100733

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (743-748) 743 Interpreting Signals From an Array of Non-specif ic Piezoelectric Chemical Sensors* Patricia McAlernon, Jonathan M. Slatert, Philip Lowthian and Mark Appleton Centre for Analytical Science, Birkbeck College, University of London, Gordon House, 29 Gordon Square, London, UK WCIH OPP Pattern recognition methods were tested using a gas sensor array consisting of eight interchangeable quartz crystals coated with different sorbent layers. The system is designed in such a way as to allow headspace sampling from a jar or vial. Using a two-sample t-test the two sampling methods were found to be significantly different at the 95% confidence level. It was found that the application of principal component analysis, multivariate analysis of variance and discriminant function analysis to the gradient of the initial responses and over-all response magnitudes allowed hexane, o-xylene, toluene, dodecane and tetradecane to be distinguished.Keywords: Array sensor; piezoelectric sensor; olfaction; pattern recognition Introduction The array sensor concept was first proposed around 10 years ago as an approach for overcoming some of the limitations of existing discrete sensors.' The principle of using a number of sensors simultaneously and analysing the combined responses has some analogies with human olfaction and hence the term 'electronic nose' has become popular in certain quarters. Often the 'nose' analogy can be misleading because most systems generate and match physico-chemical fingerprints and do not attempt to map the human olfactory percept.Nevertheless, the promise of such systems is enormous because they offer the potential for generating unambiguous standards for measure- ments previously described only in subjective terms. There are many methods of implementing the array concept, the most significant variations being the choice of transduction platform and the pattern recognition regime. However, the physical embodiment of the method, e.g., the sampling system, is often critical in determining the quality of information available and hence the outcome of any measurement. The objective of this paper is to demonstrate how the sampling regime and initial signal processing methods effect the success of the technique for a particular transducer platform.A range of sensor technologies have been employed, including bulk wave and surface acoustic wave quartz crystal resonators,2-5 metal oxide gas sensors,6 electrochemical cells7 and conducting polymer chemiresistors.8 Recent research on multilayer conducting polymer gas sensors arrays has also proved promi~ing.~ All of these transducers have different operating characteristics and the physical mode of interaction with the gas phase is understood to a greater or lesser extent. In this study, we chose bulk wave quartz crystal microbalance sensors because the amount of information available about their mode of interaction is c ~ n s i d e r a b l e ' ~ ~ ~ and this may be utilized in optimizing the array method. Experimental Reagents and Materials The sample test panel comprised two aromatics (toluene and o- xylene) and three aliphatics (hexane, dodecane and tetrade- cane).All were AnalaR-grade reagents from Merck (Poole, Dorset, UK). The sample containers were either 1.5 ml polypropylene vials (Sarstedt, Leicester, UK) or modified 60 ml glass jars (Fisons, Loughborough, UK). Instrumentation The sensor response data were collected on a ScanMaster I1 sensor array system (Array Tec Chemical Sensors, Chesham, Buckinghamshire, UK). A block diagram of the system is shown in Fig. 1. The sensor chamber contained eight piezo- electric quartz crystals with different sorbent layers ACS2 to ACS9 (Array Tec Chemical Sensors) designed for organic vapours. The system generated comma-separated variable (CSV) data files containing the response of each individual sensor over the time course of an experiment.These data files were exported to Excel (Microsoft UK) and MINITAB (Minitab, PA, USA) for data analysis. Sampling Methods Two sampling regimes were examined: (A) 10 ml of sample were placed in a 60 ml glass jar and left to equilibrate for 10 min at 25 "C; and (B) 0.5 ml of sample was placed in a 1.5 ml polypropylene vial at 25 "C. In both cases the instrument was configured to sample the headspace for 60 s in a recirculating mode and then switched to a purge cycle for 120 s. Results and Discussion Array sensor devices contain a number of individual sensors which respond to stimuli. The extent to which each sensor responds will depend on its affinity for a given analyte. Thus the pattern generated is essentially a chemical 'fingerprint'. The fingerprint may equally well be in response to a complex mixture as to a single compound.In the case of a mixture, the 'fingerprint' will change as the composition changes. * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. + To whom correspondence should be addressed. Fig. 1 Block diagram of the ScanMaster 11 sensor array system.744 Analyst, June 1996, Vol. 121 The quartz crystal microbalance array is essentially a set of sorption sensors where the response is determined by the diffusion of gases and vapours into absorbent ‘solvent’ be employed. polymers. A particular advantage of the sorption sensor platform is that for a wide range of samples, the sensor response is linearly additive,16 so linear pattern recognition routines can Statistical Analysis of Data Prior to application of pattern recognition methods, the quality of the data was assessed.Individual sensor responses taken at Fig. 2 Response (at 60 s) of the quartz crystal gas sensor array to aliphatic and aromatic compounds contained in 1.5 ml polypropylene vials: (a) relative frequency (%); and (b) fractional frequency change (96). Fig. 3 Response (at 60 s) of the quartz crystal gas sensor array to aliphatic and aromatic compounds contained in 60 ml glass jars: (a) relative frequency (%); and (b) fractional frequency change (%). Table 1 Correlation matrices of standardized % fractional frequency responses for jar sampling regime at different times Method Sensor 3 s 1 2 3 4 5 6 7 8 60 s 1 2 3 4 5 6 7 8 1 2 3 4 5 1.00000 0.87868 0.85956 0.87414 0.87495 1.00000 0.98743 0.98396 0.94641 1 .00000 0.98975 0.93556 1 .00000 0.93682 1 .ooooo 1 .OOOOO 0.97477 0.99094 0.42201 0.97871 1 .OO000 0.99414 0.5 1302 0.99829 1 .OOOOO 0.44827 0.99493 1 .OOOOO 0.49884 1 .ooooo 6 0.88 150 0.9 1482 0.93 149 0.942 17 0.957 19 1 .00000 0.99 195 0.97342 0.98723 0.45020 0.97728 1 .ooooo 7 0.86724 0.87438 0 39901 0 30810 9.9 1779 ~1.98874 1 .00000 0.99175 0.97334 0.98657 0.46647 0.97626 0.99917 1 .00000 8 0.5 1649 0.39737 0.31591 0.40425 0.49 160 0.42438 0.37480 1 .ooooo 0.53 1 18 0.6092 8 0.56355 0.7 103 1 0.60587 0.58125 0.58435 1 .oooooAnalyst, June 1996, Vol.121 -2 - 745 0 A 60 s were found to have relative standard deviations (RSD) of between 2 and 5% (n = 5).The RSD was correlated with the magnitude of the response, which suggests that there may be useful residual information available in the noise generated by different sensor coatings. Using a two-sample t-test, the two sampling methods were found to be significantly different at the 95% confidence level. Sensor responses for jar headspace measurements were found to be up to 50% higher than those for corresponding vial headspace measurements. The headspace of the jar is 50 cm3 whereas the headspace of the vial is 1 cm3. The total value of the recirculating flow system is around 15 cm3, corresponding to a 30% dilution of the jar headspace sample and a 1500% dilution of the vial headspace sample. A consequence of the measuring regime is that the concentration of the vapour- phase sample will increase throughout the measurement cycle, although at a greater rate for the vial than the jar. On exposure to a vapour, the frequency of the sensor (termed the gradient of the initial response) decreases rapidly, then reaches an equilib- rium frequency (termed the over-all magnitude).The RSD for sensor responses taken at 3 s (essentially the gradient of the initial response) varied from 30 to 50% for both jar and vial measuring regimes. Signal Pre-processing Previous studies suggest that the pre-processing algorithm employed is important in determining the performance of the pattern recognition method. Various pre-processing parameters have been used in the field of gas sensing, for example, difference models, relative models, fractional difference models and normalization procedures. *7-l9 However, for the bulk acoustic wave quartz crystal microbalance, data extraction from sensor response curves has not yet been fully exploited.The over-all response magnitude is the most commonly used descriptor of sensor response, but the gradient of the initial response or the sensor recovery may also be a useful variable in pattern recognition regimes. Nanto et a1.20 used parameters which characterize the transient responses of quartz crystals to discriminate aromas. Edmonds et a1.21 also used initial rate measurements of quartz crystals and compared them with equilibrium shift values. Saunders et ~ 1 . 2 2 examined the time- dependent frequency responses (termed kinetic signatures) of sensors.Using the initial sensor response has two significant advantages: (1) it will shorten the analysis time and (2) it may increase the lifetime of the sensor. Table 2 Eigenanalysis of the correlation matrix for the various sampling methods Vial method Jar method Eigenvalues 3 s 60 s 3 s 60 s 6.0678 (75.8%) 1.1520 (1 4.4%) 0.4986 (6.2%) 0.225 1 (2.8%) 0.0301 (0.4%) 0.0 134 (0.2%) 0.0094 (0.1 %) 0.0037 (0% ) 7.2559 (90.7%) 0.5802 (7.3%) 0.1361 (1.7%) 0.0 192 (0.2%) 0.0044 (0.1 %) 0.0030 (0%) 0.001 1 (0%) 0.0000 (0%) 6.7433 (84.3 9%) 0.8337 (10.4%) 0.2019 (2.5%) 0.1512 ( I .9%) 0.0562 (0.7%) 0.008 1 (0.1 %) 0.0036 (0%) 0.0022 (0%) 6.6059 (82.6%) 1.0543 (1 3.2%) 0.2794 (3.5%) 0.0509 (0.6%) 0.0068 (0.1%) 0.0020 (0%) 0.0005 (0%) 0.0003 (0%) Since the efficiency of pattern recognition methods relies on samples having different response patterns, it is crucial to maximize these differences using the most appropriate pre- processing algorithm.In this work three pre-processing methods were investigated using responses obtained at 60 s only: (i) % relative frequency = [AF(t)/AFo(t)] X 100 (ii) % fractional frequency change = [AF(t) - AFo(t))/ (iii) normalized fractional frequency change = [AF(t) - AFo(t)J X 100 AFo(t)/AFo(t)] X loo/(, 2 [AF(t) - A F O ( ~ > ] ~ / A F O ~ ( ~ ) } ‘” I = 1-8 where AF(t) is the frequency after exposure ( H z ) at time t s (t = 60) and AFo(t) is the initial frequency (Hz) at time t s ( t = 0). The % relative frequency and the % fractional frequency response patterns obtained from the sensor array system at 60 s using the vial and jar regimes are shown in Figs.2 and 3, respectively. The latter pre-processing method maximized the differences in response patterns taken at 60 s and was employed for subsequent pre-processing of responses at t = 3 s. Choosing this pre-processing method for sensor responses at 3 s will essentially give the gradients of initial sensor responses. Pattern normalization and subsequent autoscaling to a mean of zero and standard deviation of unity were not found to extract any extra information or enhance the response of any of the pattern recognition methods employed. Pattern recognition is some- times employed to remove the effects of concentration and the sensitivity of one vapour relative to another. Autoscaling has the disadvantage that sensors which have weak responses to an individual compound are often subject to greater relative experimental error but have an equal influence on the analysis, leading to noise propagation within the response pattern.23 ‘1 0 0.0 0.5 :: “15, I I c X c O.0 - 1.0 1 I I 1 I 1 1 - 4 -3 - 2 - 1 0 1 2 3 FC1 Fig. 4 Principal component analysis of data for (a) 3 s and (b) 60 s response of the quartz crystal gas sensor array to (0) hexane, ( X ) o-xylene, ( A ) toluene, (0) dodecane and (0) tetradecane contained in 1.5 ml vials.746 Analyst, June 1996, Vol. 121 4 - 3 - 3--r ( b ) A 2 - 4a 2 - (v 1 - 0 - 1 - -1 QD -1 - A i? 0 - -2 - 3 - -1 - r - 2 - 1 Pattern Recognition Methods Pattern recognition can be divided into unsupervised (classifi- cation) and supervised (discrimination) approaches.24.25 These methods differ in that a training set is required for supervised learning in order to establish a response model.Unsupervised learning is commonly used for preliminary investigations to determine the natural groupings of data in two-dimensional space. The unsupervised method employed for this work was principal component analysis (PCA). Principal component analysis has been successfully applied to analyse the response of piezoelectric devices2C28 and more recently to classify odours. The object of principal component analysis is to take p variables XI, X2, ..., X p and find combinations of these to produce indices Z,,Z,, ..., 2, that are uncorrelated and measure different ‘dimensions’ in the data. The indices are also ordered so that Z1 displays the greatest variation, 2 2 displays the second largest and Zp the least. Since the best results are obtained when the original variables are very highly correlated, this multivariate technique is ideally suited to gas sensor arrays which have partially overlapping sensitivities to different sample components. Our implementation of PCA involved four steps.First, the variables were coded to have zero means and unit variance. Second, the correlation matrix was calculated for the different sampling regimes. The correlation matrices for the jar sampling regime are shown in Table 1. Some sensors are more highly correlated at the 3 s response than the 60 s response, which may suggest that different interaction mechanisms predominate at different times in the sensor response profile.The third step involved calculating the eigenvalues and corresponding eigenvectors. The eigenvalues and corresponding percent variance for the different sampling regimes are shown in Table 2. The first principal component is by far the most important of the eight components for X ( b ) X x x x A * a d .4 1 1 1 1 1 1 I I 1 AA I I I I I I 1 1 Ic I - 6 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 A r .C, I I I I I I I I I -5 - 4 -3 - 2 - 1 0 1 2 3 FC1 representing the variation in the measurements of the five samples. The eigenvectors, more commonly called the loadings, provide the coefficients of the principal components. The loadings for the sampling methods were found to be similar for all variables, suggesting that all have an impact on classifica- tion.The corresponding scores for each sample were plotted in two-dimensional space to complete the PCA and these are shown in Figs. 4 and 5. The PCA plot obtained when gradient and steady-state signals were mixed, shown in Fig. 6, reveals less within-group scatter for the jar measurements. Comparison with Figs. 4 and 5 suggests that measurements at 3 s have an important contribution when gradient and steady-stage signals were mixed. This may in turn suggest that an over-all ‘kinetic signature’ may yield valuable information overlooked when gradient and steady-state measurements are considered sepa- rately. In Figs. 4-6 hexane, dodecane and tetradecane cluster in the same area of the plots, which suggests similarities in response patterns.The location of these clusters may indicate similarities in vapour-coating interaction mechanisms for the three ali- phatic compounds. Toluene and o-xylene were found to take up a different location, which may suggest different vapour- coating interaction mechanisms. Comparison of PCA plots for different sampling methods show greater scatter in the 3 vial responses, which is probably related to the increased im- portance of exact timing for initial data collection. Multivariate analysis of variance (MANOVA) has been used to determine several linear combinations (canonical dis- criminant functions) for separating groups.27 The first dis- criminant function gives the maximum possible F ratio (mean 3-l 1 2 -I” 0 a 0 -7 -2 A , I A 1 I I I -6 -1 4 PC1between groups divided by the mean within groups) on a one- way analysis of variance for the variation within and between groups.The second function gives the maximum possible F ratio for a one-way analysis of variance provided that there is no correlation between the first function and the second function within groups. The relationship between groups can be visualized by plotting these two functions for each individual in a similar manner to PCA when the principal components are plotted. Application of MANOVA to the % fractional frequency responses for the eight sensors revealed distinct groupings, as shown in Figs. 7 and 8. The jar headspace responses at 3 s revealed distinct groupings. Results of eigenanalysis for class and corresponding percentage variance are shown in Table 3.The first two canonical variates account for a high percentage of variation in the data. The coefficients of the canonical variates are shown in Table 4. The values suggest that the variables are not highly correlated. The five groups (hexane, o-xylene, 1.0 0.5 0.0 2 Q - 3 -8 Analyst, June 1996, Vol. 121 747 0 1 2 21 f I I I - 20 - 10 0 zl Fig. 7 Application of MANOVA to responses obtained at (a) 3 s and (b) 60 s for (e) hexane, (X) o-xylene, (A) toluene, (0) dodecane and (0) tetradecane contained in 1.5 ml vials. toluene, dodecane and tetradecane) were found to be sig- nificantly different using Wilks' lambda and Bartlett's chi- squared approximation: Wilks' lambda = I Wo 1/1 Bo + WO I where Wo is the sum-of-squares and products within groups and Bo is the sum-of-squares and products between groups.One approach to supervised (discrimination) learning is hard modelling through linear discriminant analysis (LDFA). The disadvantage of this method is that there is no allowance for overlapping of class models. The data do not need to be standardized prior to analysis as is the case with PCA. The first step involves setting up a model for all classes using a training set. The efficiency of the model is then determined using a test set to identify unknowns. Unknowns are identified by calculat- ing the Mahalanobis distance to group centroids and allocating it to the group to which it is closest. LDFA was carried out on the four sets of measurements. Since the data sets were small, 4 3 Q 2 1 0 r- ~ - 1 I I I I -5 -4 -3 -2 - 1 0 '1 f 1 I 0 10 zl 20 Fig.8 Application of MANOVA to responses obtained at (a) 3 s and (b) 60 s for (*) hexane, (X) o-xylene, (A) toluene, (0) dodecane and (0) tetradecane contained in 60 ml glass jars. Table 3 Results of eigenanalysis for class (MANOVA) of the various sampling methods Vial method Jar method Factor 3 s 60 s 3 s 60 s 18.5 136 3.6128 2.4549 0.1984 0.00 0.00 0.00 0.00 (74.71%) (14.58%) (9.91%) (0.8%) (0%) (0%) (0%) (0%) 2303.46 235.77 27.64 12.31 0.00 0.00 0.00 0.00 53.9269 3 1.3439 1.9825 0.1625 0.00 0.00 0.00 0.00 (61.69%) 1532.88 (35.86%) 133.02 (2.27%) 27.37 (0.19%) 1.73 (0%) 0.00 (0%) 0.00 (0%) 0.00 (0%) 0.00748 Analyst, June 1996, Vol. 121 cross-validation was employed to determine how well groups could be separated using the available variables (i.e., sensors).The results of this procedure (also known as leave-one-out) are shown in Table 5. The success rate for the vial and jar methods when 60 s responses were used was 100%. For the 3 s responses a 96% success rate was found. In a further comparison, the data set was halved, which resulted in training with only ten samples and testing with the remaining ten. This caused problems with LDFA owing to highly correlated variables. It was necessary to remove at least five variables which were considered to be too highly correlated. This high correlation was confirmed using multiple linear regression (MLR), which is useful to isolate redundancy of sensors in the array. When highly correlated variables were removed and LDFA was implemented for the 3 s vial and jar responses, six and eight out of ten samples were correctly predicted, respectively.For 60 s vial and jar responses, ten and nine out of ten samples were correct. The results are shown in Table 5. Conclusions The over-all response magnitude results suggest that 1.5 ml polypropylene vials provide the optimum sampling regime on the basis of higher success rates obtained from linear dis- criminant function and less within-group scatter observed in MANOVA plots. These findings may be due to the correlation between the RSD and the magnitude of the response. Sensor responses for jar headspace measurements were much greater Table 4 Coefficients of the canonical variates for the various sampling methods Vial method Jar method Canonical variate Variable 1 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 2 3 s 6 0 s -0.191 -0.912 0.016 0.411 0.003 0.740 0.162 -0.566 -0.108 -1.738 2.314 -6.359 -2.962 -0.172 1.646 0.303 0.045 1.025 0.030 1.301 -0.141 -1.571 0.425 -4.539 -2.993 1.329 2.824 1.774 -0.599 3.004 - 1.923 - 1.998 3 s 60 s 0.413 -0.919 -0.356 0.743 0.270 -0.154 -0.041 0.048 0.301 -1.113 -4.957 8.547 3.139 -2.798 0.450 -0.122 0.420 -1.495 0.309 0.620 0.270 0.034 -0.416 -0.433 -0.052 1.050 -0.899 -0.804 0.550 -1.528 0.529 0.123 Table 5 Results of linear discriminant analysis for the various sampling methods Success rate (%) Using whole data set Method with cross-validation Using part of data set 60 100 80 90 than those for vial headspace measurements, resulting in higher RSDs and consequently more scatter in two-dimensional plots.This sampling regime gave 100% success rates for discriminat- ing between hexane, o-xylene, toluene, dodecane and tetra- decane when correlated variables were removed and LDFA was implemented. For the ‘gradient of response’ approach, the results suggest that sampling from the headspace of a 60 ml glass jar was the best. This sampling regime gave 96% success rates for discrimination using cross-validation. PCA plots obtained by mixing gradient and steady-state measurements suggested that the former has an important contribution ( i e . , the gradient of the response is simply not ignored but is providing additional information). This may in turn suggest that over-all ‘kinetic signatures’ may yield valuable information overlooked when gradient and steady-state measurements are considered separately.The authors thank Array Tec Chemical Sensors for supplying the instrumentation and for their valuable support and advice. References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Stetter, J. R., Jurs, P. C., and Rose, S . L., Anal. Chem., 1986, 58, 860. Zaromb, S., and Stetter, J. R., Sens. Actuators, 1984, 6, 225. Gardner, J . W., Sens. Actuators B , 1991, 4, 109. Hidehito, N., Kawai, T., Sokooshi, H., and Usuda, T., Sens. Actuators B , 1993, 13-14,718. Nakamoto, T., Fukuda, A., and Moriizumi, T., Sens. Actuators B , 1993, 10, 85. Shurmer, H. V., Gardner, J. W., and Chan, H. T. Sens. Actuators, 1989, 18, 361. Stetter, J. R., Jurs, P. C., and Rose, S . L., Anal. Chem., 1986, 58, 860. Pelosi, P., and Persaud, K., in Sensors and Sensory Systems for Advanced Robots, ed. Dario, P., NATO AS1 Series, vol. F42, Springer, Berlin, 1988, pp. 361-381. Slater, J. M., Paynter, J., and Watt, E . J., Analyst, 1993, 118, 379. King, W., Anal. Chem., 1964,36, 1735. Alder, J. F., and McCallum, J. J., Analyst, 1983, 108, 1169. Guilbault, G., Zon-Sel. Electrode Rev., 1980, 2, 3. McCallum, J. J., Analyst, 1989, 114, 1173. Lai, C. S. I., Moody, G. J., and Thomas, J. D. R., Analyst, 1986, 111, 511. Slater, J. M., and Paynter, J . , Analyst, 1994, 119, 191. Janghorbani, M., and Freund, H., Anal. Chem., 1973,45, 325. Heiland, G., Sens. Actuators, 1982, 2, 343. Yannopoulos, L. N., Sens. Actuators, 1987, 12,287. Homer, G., and Hierold, C., Sens. Actuators B , 1990, 2, 173. Nanto, H., Kawai, T., Sokooshi, H., and Usuda, T., Sens. Actuators B , 1993,13-14,718. Edmonds, T. E., Hepher, M. J., and West, T. S . , Anal. Chim. Acta, 1986,187, 293. Saunders, B. W., Thiel, D. V., and Mackay-Sim, A., Analyst, 1995, 120, 1013. Gardner, J. W., and Bartlett, P. N., in Techniques and Mechanisms in Gas Sensing, ed. Moseley, P. T., Norris, J., and Williams, D. E., Adam Hilger, Bristol, 1991, p. 335. Manly, B. F. J., Multivariate Statistical Methods: a Primer, Chapman and Hall, London, 2nd edn., 1994, p. 76. Brereton, R. G., Analyst, 1987, 112, 1635. Carey, W. P., Beebe, K. R., Kowalski, B. R., Illman, D. L., and Hirschfeld, T., Anal. Chem., 1986, 58, 149. Ide, J., Nakamoto, T., and Moriizumi, T . , Sens. Actuators B , 1993, 13-14, 35 1 . Gardner, J. W., Shurmer, H. V., and Tan, T. T., Sens. Actuators B , 1992, 6 , 71. Paper 5108341 I Received December 22, I995 Accepted March I I , 1996

ISSN:0003-2654    

DOI:10.1039/AN9962100743

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (749-754) 749 Chemometric Techniques in Multivariate Statistical Modelling of Process Plant* M. Hartnett, G. Lightbody and G. W. Irwin? The Control Engineering Research Group, Department of Electrical and Electronic Engineering, The Queen's University of Belfast, Belfast, UK BT9 SAH The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.Keywords: Statistical process control; principal component analysis; partial least squares Introduction Advances in material science and improvements in sensor design and fabrication1 have led to their increased use in a wide variety of applications from clinical2 to environmental3 mon- itoring. Sensors have also seen flourishing application in providing supervisory information concerning the state and performance of a range of mechanical and automated systems from engines4 to aircraft.5 Hence it is not surprising that sensors have obtained a key role in monitoring the behaviour of industrial pr0cesses.6.~ The increased use of sensors has led to a concomitant growth in the amount of data that can be acquired from the system of interest.Frequently, however, all the measured variables are not statistically independent and represent different manifestations of a smaller number of underlying variables that can be used to characterize the system. The main purpose of sensors in an industrial monitoring context is to provide information concerning the process being studied and the quality of the final product. Data acquired while monitoring the process itself may include thermodynamic properties such as pressure or temperature or they might also include process settings such as the positions of valves. Measurements of the final product may involve descriptions of physical or chemical properties used for determining its quality, an example of which might be the use of the melting-point of a solid product to obtain a purity estimate.Data from on-line sensors monitoring the plant (process variables) can be obtained frequently. By contrast, variables describing the quality of the final product (product quality variables) are measured less frequently, and indeed are often determined off-line in a laboratory. Further, these measurements may require the use of complex analytical techniques or expensive instrumentation. It is therefore desirable to develop a method for inferring such product quality variables from measurements acquired while monitoring the operation of the process. * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995.+ To whom correspondence should be addressed. Traditional univariate methods of process monitoring (e.g., Shewharts and CUSUM9 techniques) involve the use of control charts on which a small number of key measurements (which in some way relate to the quality of the final product) are plotted and monitored. There are two main problems with this approach. The first arises from the use of a few key variables for monitoring. With the increased amount of measurements available, it may be difficult to identify which variables to use for this purpose. The second problem arises from the univariate nature of these approaches. Many of these techniques examine the deviation from the mean of each variable independently of the rest, rather than considering how the variables are behaving with respect to each other, and hence encounter problems when the variables are correlated. While multivariate statistical techniques, such as principal component analysis (PCA) and partial least squares or projec- tion to latent structures (PLS), have been used within the chemometrics community for some time as a method of dealing with data containing correlated variables,l&l* it is only recently that they have been employed for process monitoring and control applications. 13-16 The aim of this contribution is to discuss the application of PCA and PLS in this context and to describe how extensions and tests based on these techniques can be used in statistical process control (SPC).Two case studies are examined in order to investigate the use of these techniques in this type of problem.The first involves the application of principal component regression (PCR) to model data obtained from a simulation of a continuous stirred tank reactor (CSTR). The second study involves the application of PLS to model data obtained from the paper by Skagerberg et al.,'7 acquired from a simulation of a low-density polyethylene (LDPE) reactor. The PLS technique is implemented in the same manner as described by Skagerberg et a/. The first case study demonstrates the ability of the PCR modelling technique to form steady-state models, which in this instance are obtained by pseudo-random binary sequence (PRBS)18.19 excitation of the CSTR system. The second study also demonstrates the ability of PLS to form steady-state models, but it additionally describes an extension to the algorithm that allows the development of control charts to detect disturbances to the system.This example (reproduced from the work by Skagerberg et a1.17) and the first study involving the CSTR are used as tutorial examples, to portray the potential of these powerful new techniques and to expose current limitations as areas of future research. Principal Component Analysis PCA20-25 is a technique which can be used to reduce the dimension of a data matrix whose variables are highly correlated. Given a matrix X , of m measurements on n variables, PCA decomposes X into a sum of the products of the n column vectors ti and n row vectors pT and a matrix E: (1) X = tgr+tzpT+.-+t@kT+E = T P T + E750 Analyst, June 1996, Vol.121 The ti are known as scores and describe how different measurement samples relate to each other. The pi vectors are known as loadings and are eigenvectors of the covariance matrix of X . The loadings describe how the process variables relate to each other. The eigenvalue hi associated with the eigenvector pi describes the variance contained in pi. The matrix E contains the residuals, or the part of the matrix X not contained in the PCA model. The pi and ti vectors are extracted from X in order of decreasing values of hi. Thus, { p l , t l } describes more of the variance of X than (p2,tz 1, and so on. If the variables in X are highly correlated, most of the variance in X will be described by k principal component {p&} vector pairs where k I min(m,n) and X can be represented by a matrix of equal or smaller dimension, given by the retained (pi,ti} vector pairs.In this way, it is assumed that most of the systematic variation in X is contained within the k retained principal components and that the remaining [min(m,n) - k] principal components describe the noise in X . There are a range of different tests available for determining k, these include tests on the residual standard deviation, the root mean squared (rms) error and cross- validation tests. As the tests are looking at different aspects of the problem of determining how many principal components to retain, they tend to yield slightly different results and hence it is advisable to employ a number of different tests to obtain a better overview of the situation.Prior to the development of a PCA or PLS model, the data are frequently mean-centred and variance scaled. In mean-centring the average value of a variable is calculated and subtracted from the elements of its corresponding vector, and is used to remove the candidate model parameter corresponding to the mean. Variance scaling is used when the variables in a matrix are measured in different units. As PCA is essentially a least- squares technique, its models can be dominated by variables which are orders of magnitude greater than the others, consequently reducing the sensitivity of the model to these variables. However, variance scaling should be used with some care; in situations where the variables do not change much, it is possible to scale up the noise within a system rather than the underlying variance within the variables.20 Having developed a PCA model, based on a reference data set obtained from the process under normal operating conditions, it is possible to develop control charts from this model for future process measurements.Control charts based on PCA models use two main statistics, namely the T’ and the Q statistics.26 Geometrically, the T2 statistic is a measure of how far a PCA estimate is from the multivariate mean of the measurement data. It is a measure of the variation accounted for by the PCA model and is given by the sum of the normalized squared scores from the model: where tiG) is the score of a sample j from the ith principal component, PO) is the T‘ statistic for the sample and hi is the eigenvalue of the principal component.A confidence interval can be established for the T’G) statistic as k(m - 1 ) m - k T&k.m,or = Fk,rn,l--cu (3) where m is the number of samples and a is the confidence level (often 95%) of the F distribution. This statistic, however, will only detect unusual variability in the subspace defined by the PCA model. If a new event occurs that was not contained within the reference data set, the PCA estimate of the measured event will shift outside the normal operating space of the model. This occurrence can be detected by the Q statistic (also known as the squared prediction error of X). Geometrically, the Q statistic represents the squared perpen- dicular distance of a new multivariate observation from the hyperplane defined by the retained principal components.Following PCA, a given measurement vector xi can have k scores, from the k retained principal components. These scores can be represented by the vector tik. If the matrix Pk represents the matrix of the k retained principal component vectors, then from eqn. (1) it is possible to estimate an observation vector xF from the matrix Pk and the vector tik. If the principal components completely represent the data, then (4) xi = tikp: Hence tik = xJ’k and, if the estimate of X i is given by $i, ( 5 ) The residuals of a new multivariate observation are given by e j = xi - rii (6) and Q is given by Ileill. Approximate confidence intervals for Q can be established if all the eigenvalues of the data matrix are known:27 where n (8) ei = hj i = 1,2,3, ...is the sum of the eigenvalues for the eigenvectors not used in the model raised to the power of i, j = k + l (9) c, is the normal deviate cutting off an area a under the upper tail of the distribution if ho is positive and under the lower tail if ho is negative. Partial Least Squares (PLS)2&32 If product quality information is available, a regression method can form a relationship between the product quality variables, Y , and the process variables X . Multilinear regression (MLR) forms a relationship between X and Y without considering the internal structure (covariance) in these matrices. As a result, correlation in the X or Y matrix has the effect of increasing the variance of the estimates of the regression parameters. A regression technique based on PCA, known as principal components regression (PCR), uses PCA to form an initial model of X whose principal components are then progressively correlated with Y.However, the PCA model is constructed on the basis of variability in X without consideration of Y. Thus, while the first few principal components may describe most of the variation in X , they may bear no relation to Y . Hence a large number of principal components may be needed to form a good model of Y.28 The term PLS refers to a group of techniques that attempt to form a compromise between PCR and MLR by finding latent vectors that fulfil the dual requirements of capturing variance in X and achieving correlation with Y . The PLS model can be considered as consisting of outer relations which describe the X and Y matrices separately and inner relations which describe the relationship between them.The outer relations describe a decomposition of the X and Y matrices in a form similar to that encountered in PCA [see eqn. (1)], where TPL$ is the matrix of the scores for the X matrix obtained from PLS, UPLS is theAnalyst, June 1996, Vol. 121 75 1 matrix of the scores for the Y matrix, PpLsT is the matrix of loadings for the X matrix obtained from PLS and QpLsT is the matrix of loadings for the Y matrix obtained from PLS: The inner relation is obtained by exchanging the scores between the X and Y matrices. If it is required that the tpLs vectors be orthogonal, then the loadings p p ~ s are replaced by weights wpLs. For further details of these techniques, Martens and Naes28 provide an in-depth analysis.In addition to modelling the product quality variables from the process variables, PLS can also be used in a similar way to PCA with T2 and Q statistical control charts. Case Studies In order to illustrate some of the techniques discussed above, two case studies are examined. The first case study involves the use of PCR to form a model of some input variables of a continuous stirred-tank reactor, to permit the determination of an output variable, namely product concentration. The second study involves the use of PLS to form a model of a low-density polyethylene reactor using 22 input variables to predict six output variables. A multivariate monitoring chart was devel- oped using this model to allow the supervision of the future operation of the process.Both studies were performed in MATLAB (version 4.2) using the Chemometrics toolbox33 and using Wise's PLS toolbox34 operating on a Sun workstation. Principal Components Regression Modelling of the Continuous Stirred Tank Reactor The continuous stirred-tank reactor (CSTR) plant35 is described by the following non-linear differential equations: V 4 P(i) = -[To - T(t)] + klC,(t). exp Within the CSTR, two chemicals are mixed and react to produce a product compound A at a concentration Ca(t), with the temperature of the mixture being T(t). The reaction is exothermic, producing heat which acts to slow it down. By introducing a coolant flow-rate qc(t), the temperature can be varied and hence the product concentration controlled.Ca0 is the inlet feed concentration, 4 the process flow-rate and TO and TCo the inlet feed and coolant temperatures, respectively, all of which are assumed constant at nominal values. Likewise, ko, EIR, v, k l , k2 and k3 are thermodynamic and chemical constants relating to this particular problem. Numerical values for the parameters of this model are given in Table 1. Ordinarily, for a process flow rate 4 of 100 1 min- and a feed temperature To of 350 K, an equilibrium product concentration C, of 0.1 mol 1-1 can be obtained if the steady-state reactor temperature T is 438.54 K and the coolant flow rate qc is 103.41 1 min-1. In this study, it was desired to develop a PCR model that could form a relationship between the process variables X , given by TCo, Cao, q, qc, TO supplemented by the reactor temperature T and the product quality variable Y of the product concentration C,. To create a realistic simulation, Gaussian noise was added to Tco and C,o, at f2.5% of their dc levels of 350 K and 1.0 mol 1-l, respectively, in order to simulate measurement noise.The q, qc and To variables were perturbed by a PRBS inducing random amplitude steps in the ranges q = 100 f 2.25 1 min-1, qc = 103.41 k 2.33 1 min-1 and To = 350 f 4.4 K. Gaussian noise at 0.25% of their steady-state values was also added to these signals and to the resulting temperature T and product concentration C, variables. As the six process variables X used for building the PCR model are effectively independent of each other (apart from the reactor temperature variable 7') in eqns.(1 1) and (12), there would be no dimensionality reduction effect with the PCA technique (i.e., it might be expected for the model to contain six parameters). To demonstrate suitably the potential of PCA, the PRBS signals on the q and qc variables were artificially correlated (in order to enable the PCA technique to form a model using five parameters). As PCA is a steady-state technique, it would not be suitable for modelling dynamic data. Hence the data sets used for the development and testing of the PCR model were composed of data obtained after the system had settled around a given step change in the concentration. Following boxcar averaging and moving average smoothing, the data obtained from the simulation were split into two sets.The set used for developing the PCR model was composed of data obtained from 27 PRBS steps with five samples at each step. The data set used for testing the model was composed of data obtained from ten PRBS steps with five samples at each step. Following mean-centring and variance scaling, PCR was performed on the training data set, and following tests such as cross-validation, five principal components were used to develop the PCR model, as was expected. Fig. 1 shows the prediction of the concentration in the test set using the PCR model developed on the training set plotted with the actual averaged and smoothed concentration in the test set. While there is good agreement between the two sets of data, there is still some noise around a given step. Further smoothing may alleviate the problem, as might a larger data set on which to base the original PCR model.PLS Modelling and Statistical Process Control of a Low-density Polyethylene (LDPE) Reactor LDPE is normally produced in a tubular reactor at high pressure. The quality of the final product can be described by a number of parameters such as the average molecular mass of the polymer. However, many of these parameters are difficult and expensive Table 1 CSTR simulation parameters Parameter* Description Nominal value c,, c p c EIR Inlet feed concentration Equilibrium product concentration Specific heats Activation energy Reaction rate constant Constants Process flow rate Coolant flow rate Inlet coolant temperature Feed temperature Temperature Reactor volume Heat of reaction Liquid densities Heat transfer coefficient 1.0 k 0.025 moll - I cal g-1 K-I 7.2 X 1010 min-1 See footnote 100 * 2.25 1 min-l 103.41 k 350 f 9 K 350 k 4.4 K 1 x 1 0 4 ~ 2.33 1 min-1 100 1 -2 x lo5 cal mol-1 1 x 103g1-1 7 x 105 cal min-1 K-1 * k l = -AHko/pCp; k2 = pcCpc/pCpv; k3 = hJpCCp.For an equilibrium product concentration Ca = 0.1 mol I - I , the following steady- state reactor temperature, T , and coolant flow rate, qc, are required: T = 438.54 K; qc = 103.41 1 min-I.752 Analyst, June 1996, Vol. 121 to measure frequently, involving techniques such as mass spectrometry. In this example, a statistical model is developed which enables the product quality variables to be inferred from parameters describing the operating conditions of the reactor. The data for this example were taken from a paper by Skagerberg et a1.I7 in which a set of product quality variables were obtained by simulation of a theoretical model, devised by Kiparissides et al.,36 under different operating conditions. In an attempt to learn more about the implementation of PLS in such applications, it was decided to reproduce the work performed by Skagerberg et a1.17 The properties used to describe the quality of the final product include (i) the number-average molecular weight M,, (ii) weight-average molecular weight M,, (iii) the frequency of long-chain branching, LCB, (iv) the frequency of short-chain branching, SCB, ( v ) the content of vinyl groups, VNL, and (vi) the content of vinylidene groups, VND, in the polymer chain.The operating conditions used for the development of the model are a set of 20 different temperatures measured along the length of the reactor, supplemented by the temperature of the wall of the reactor and the feed rate of the solvent.A set of 56 measurements of the process variables and the product quality variables was obtained, of which the first 32 were used for the development of the model and the remaining 24 for testing the model. Following mean-centring and variance scaling, PLS was performed on the training data set and a range of tests were implemented in order to determine how many latent vectors to retain. Based on these tests, it was decided that five latent vectors should be retained. Clearly, there is a large dimension reduction achieved from a potential 22 input parameter model to a five-parameter model.The model was examined by considering its ability to model its training set. Figs. 2 and 3 plot predicted values of SCB and LCB (based on the PLS model on the training data set) against the actual values of these variables obtained from the paper.17 There is good agreement in both instances. Having apparently obtained a good model, it was then used for the development of a multivariate control chart for the data in the test set. Such a multivariate control chart based on the PLS model (see Fig. 4) was used to examine the data in the test set. The chart was designed as described by Kresta et al.,37 in which the first two axes correspond to the scores of the observations in the test set on the first two latent vectors [tl and tz from the matrix T in eqn.(12)]. The third axis corresponds to the squared prediction error (SPE,,) of the product quality variables. Thus, 0.125 ".W< 0 5 10 15 20 25 30 35 40 45 50 Observations Fig. 1 Plot of the concentration C,(t) obtained from the simulation of the CSTR excited by a PRBS (solid line) and the prediction of this concentration from the PCR model (dashed line). n SPE,,j = c ( y j j - pQ)2 (13) where is the predicted value of a product quality variable and i andj denote a measurement and a variable index, respectively. A number of features can be observed in Fig. 4. A coolant overheating problem at points 34-36 results in a significant j = 1 0333- 5: l 3 2 - 4 O 3 l - 30 - 29 - 0 0 28 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3 6 ObseNed sc0 Fig.2 Plot of the short-chain branching frequency (SCB) predicted by the PLS method against the SCB values in the paper by Skagerberg et af.17 (given by the open circles; a potential exact agreement between the prediction and the published values is given by the solid diagonal line). 0.24 - 0.23 0.22 - - 9 4 0.2- - a 2 0 1 9 - 0.18- 017- 0.16 - Fig. 3 Plot of the long-chain branching frequency (LCB) predicted by the PLS method against LCB values in the paper by Skagerberg et af.I7 (given by the open circles; a potential exact agreement between the prediction and the published values is given by the solid diagonal line). x 1013 147 Fig. 4 Mutivariate control chart for the LDPE reactor based on the scores from the first two latent vectors (tl and t 2 ) and the squared prediction error of the product quality variables Y.Analyst, June 1996, Vol.121 753 increase in the SPE. A gradual change in the impurities reaching the reactor at points 54-56 results in a shift in the tl-tz plane, as does a gradual fouling of the reactor wall at points 38-40 with a small increase in the SPE. Multi-way PCA and PLS PCA as described does not consider the order within a measured data set (the order arising from the sequential nature of the measurements). Thus, if the samples were re-ordered, identical results would be obtained from the PCA analysis. Techniques that explicitly consider the ordering in data are known as multi- way methods and are particularly useful for the analysis of batch process data. Process data from s runs of a batch process can be organized into a three-dimensional array X* in which N = 1,2, ....n variables are measured at M 1,2, .... rn intervals throughout a batch. Fig. 5 depicts this three-dimensional array. Multi-way PCA (MPCA) is equivalent to unfolding the three- dimensional array X* slice by slice and rearranging the slices into a large two-dimensional array X*. This involves putting each of the vertical slices side by side, starting with the slice corresponding to the first time interval. Fig. 6 depicts this unfolding process. Each column of the two-dimensional array is mean-centred, which is equivalent to subtracting the mean trajectories of each variable through all the time intervals, thus removing the main non-linear and dynamic components in the data.PCA performed on these mean-centred data is thus a study of the variation in the trajectories of all the variables in all the batches about their mean trajectories. PCA decomposes the data matrix X* into a summation of k products of score vectors t and loading vectors p plus a residuals matrix E*. Each element of a t vector corresponds to a single batch and depicts the over-all variability of this batch with respect to the other batches in the data set throughout the whole batch duration. The elements of a p vector are the weights applied to each variable at each time interval within a batch and summarize the time variation of the measurement variables around their average trajectories. Choosing the number of principal components to retain can be performed via cross- validation as with conventional PCA.MPCA in batch processes only makes use of the process variable trajectory measurements taken throughout the duration of the batch: multi-way PLS (MPLS) uses both the process data X* and the product quality data Y . In this case, the data used for = Mcasurenieiits Batchel/ v/ n / Variables ’ Fig. 5 Three-dimensional array of batch process data X* (s X n X m). ................. Variatioii of a Trajectory o f a l l the single variable variables through ~n at a single time tinic intervals in a X’ interval amongst single batch all the halchea x’ Fig. 6 Unfolding the three-dimensional batch process array into a two- dimensional matrix for MPCA. developing the MPLS model is composed of the three- dimensional matrix of the process variables and a two- dimensional matrix Y of the product quality variables. The process variable matrix is unfolded in an identical fashion to MPCA, and hence MPLS explains the variation of a process variable about its average trajectory at each point of time, which is most closely related to the end quality of the product.Nomikos and MacGregor used MPCA38 and MPLS39,40 to model the behaviour of the semi-batch emulsion polymerization reactor of styrene-butadiene for the production of latex rubber. The techniques were able to distinguish between ‘good’ batches or batches with an acceptable product and ‘bad’ batches in which the product quality lay slightly outside the acceptable and control limits were established to permit on-line monitoring of the process. MPCA only uses information about the process behaviour and it will flag any abnormality in the process measurements even though it may not be relevant to the quality of the product.MPLS captures the variation in the process measurements which is most correlated with the final quality measurements. Hence MPLS will create fewer alarms than MPCA, but it might be desirable to detect all process deteriorations and correct them before they lead to more serious malfunctions. Discussion and Conclusions PCA and PLS have been briefly introduced and discussed in a process monitoring context. The former has been described in the context of reducing the dimension of large data matrices while PLS can be used for forming linear, steady-state models between the process variables and the product quality variables.In addition to giving the advantages of these techniques, it is worth considering their limitations. Both PCA and PLS form linear models from the measured data. While this can work well with a linear plant and in a region local to a particular operating point in a non-linear plant, the modelling accuracy will decrease as the range over which the model extends increases. Another limitation with these techniques arises from their steady-state nature. In calibration applications this is not problematic, but in control applications it is clearly essential to be able to model the plant dynamics. Both of these techniques are areas of current research interest and a number of extensions to basic PCA and PLS have been proposed. Spline41 and quadratic42 inner relations have been developed to allow PLS to model non-linear systems, while Budman et al.43 have proposed augmenting a PLS model with a dynamic filter to enable it to model from dynamic systems.It can be seen, in conclusion, that the PCA and PLS techniques constitute potentially very powerful methods of dealing with highly correlated data frequently obtained while monitoring a process. The authors acknowledge the financial support of the Engin- eering and Physical Sciences Research Council under grant number GRK37 16 1. Appendix Glossary of Terms Scalars coi h0 See eqn. (9) k rn n Number of process variables The normal deviate cutting off an area of a Number of retained principal components Number of measurements of process variables754 Q r Po'> S a hi ei Vectors Xi i i Yi tik Matrices E E R m X n P E Rmxn F E R r n X r Analyst, June 1996, Vol.121 Q statistic or squared prediction error Number of product quality variables Number of batches The P statistic = sum of the normalized squared scores from the PCA model [eqn. (311 Confidence level for the P statistic [eqn. (3)] Eigenvalue of the loading vector pi in PCA Sum of the eigenvalues for the eigenvectors not used in the PCA model raised to the power of i [eqn. (S)] The ith column vector of X (i.e., a measurement vector) The PCA estimate of xi The ith column of vector of Y The score vector for a given measurement vector xi using k retained principal components Residuals matrix from PCA operation on X Residuals matrix for the product quality variables Y Loadings matrix for the process variable matrix X (following dimensionality reduction E Rnxk) Loadings matrix for the unfolded batch process variable matrix X* (following dimensionality reduction E Rmnxk) component vectors variables matrix Y X (following dimensionality reduction Matrix of the k retained principal Loadings matrix for product quality Scores matrix for the process variable matrix 1 E R m X k Scores matrix for the unfolded batch process variable matrix X* (following dimensionality reduction E R s x k ) matrix Y Scores matrix for product quality variables Process variables Three way array of process variables acquired from batch processes Unfolded matrix from X* Product quality variables References Gopel, W., Sens.Actuators B, 1994, 18-19, 1.Gumbrecht, W., Peters, D., Schelter, W., Erhardt, W., Henke, J., Steil, J., and Sykora, U., Sens. 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Wold, S., Kettaneh-Wold, N., and Skagerberg, B., Chemom. Intell. Lab. Syst., 1989, 7, 53. Budman, H. M., Webb, C., Holcomb, T. R., and Morari, M., Ind. Eng. Chem. Res., 1992, 31, 1665. Paper 51083086 Received December 21, I995 Accepted February 19,1996

ISSN:0003-2654    

DOI:10.1039/AN9962100749

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (755-759) 755 Determination of 2-Furaldehyde in Transformer Oil Using Flow Injection With Pulsed Amperometric Detection* John W. Dilleen, Chris M. Lawrence and Jonathan M. Slater Centre for Analytical Science, Birkbeck College, University of London. Gordon House, 29 Gordon Square, London, UK WCIH OPP An on-line flow injection (FI) system suitable for the on-site determination of 2-furaldehyde in transformer oil has been developed. This paper examines the feasibility of combining FI with pulsed amperometric detection (PAD) to produce a method for the rapid on-line analysis of 2-furaldehyde. The determination of 2-furaldehyde by FI with PAD is direct and simple. PAD was applied to the determination of 2-furaldehyde at a platinum electrode in alkaline media.Plots of peak height versus concentration gave linear regions for concentrations in the range 5-40 ppm at a pump rate of 0.4 ml min-1 and detection potential (El) of -750 mV (versus SCE), and in the range 1-8 ppm at a pump rate of 0.8 ml min-l and El of -800 mV. Detection limits of 1 ppm (S/N = 3) were found for El = -750 mV (pump rate = 0.4 ml min-1; injection volume = 67 pI) and 0.5 ppm for El = -800 mV (pump rate = 0.8 ml min-1; injection volume = 100 pl), respectively. 2-Furaldehyde was extracted from transformer oil through a partitioning membrane in a flow through macrodialyser cell. Under optimized conditions the extraction efficiency for 2-furaldehyde from transformer oil, using differential pulse polarography experiments, was found to be 15% at oilheagent flow rates of 0.3 ml min-1.Keywords: Pulsed amperometric detection; platinum electrode; flow injection; 2-furaldehyde (furfural); power transformer; insulating oil; oil condition monitoring; cellulose degradation; partition membrane Introduction Power transformer conductor windings are insulated with paper impregnated with insulating oil. A typical high power trans- former on a national electricity grid contains 10-12 t (1 t = 103 kg) of cellulose based paper, 30-120 mm thick, and 45 t of oil. Due to heat, water and oxygen effects the cellulose degrades over a period of time. This reduces the polymer molecular chain length which in turn reduces its mechanical strength. The life of the insulation usually determines the ultimate life of the transformer, although other factors may cause it to fail early.Analysis of the degradation products of paper insulators in transformers can be used to monitor their degree of depolymer- ization (DP).' 2-Furaldehyde is one such product, so too is 2-acetylfuran and 5-methyl-2-furaldehyde. Other degradation products which may provide useful markers are phenol, m- cresol and xylene which are degradation products of phenol- formaldehyde insulating resins present in the transformer windings. Emsley and Stevens' concluded that 2-furaldehyde and related compounds were suitable markers of transformer * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. paper insulation condition. In situ monitoring of these products may provide a simple method of predicting transformer failure.Methods currently employed in the UK for transformer condition monitoring include oil acidity, oil moisture content, dissolved gas in oil analysis and 2-furaldehyde in oil analy- sis.l-4 Burton et al.5 demonstrated methods of analysis based on HPLC of extracts from the oil, and demonstrated an empirical, inverse relationship between log (DP of cellulose) and 2-fur- aldehyde concentration. They found 2-furaldehyde levels between 1-10 pprn in poorly cooled transformers. Paper degradation, and hence 2-furaldehyde levels, increase in transformers prone to overheating. The aldehyde group of 2-furaldehyde should be directly detectable at a solid noble metal electrode by pulsed ampero- metric detection (PAD).6 The method is based on multi-step potential waveforms, which incorporates amperometric detec- tion with successive anodic and cathodic polarizations in a constantly replenished analyte.7-17 The application of alternate anodic and cathodic polarizations cleans and reactivates the electrode surface.9 PAD in conjunction with flow injection (FI) has been applied successfully to the determination of numerous aliphatic compounds including carbohydrates, alcohols, alde- hydes, amines and many organic sulfur species.11-16 FI allows a small volume (typically 100 p1) of analyte to be injected into a carrier stream flowing through a detector.6,10-'6317 This enables manual wet chemistry techniques to be automated in the interests of high speed analysis and drastically reduced cost per sample.In this paper various PAD-FI regimes are considered for determining 2-furaldehyde in aqueous carrier systems.The best regime can be incorporated in a system to determine 2-furaldehyde extracted from transformer oil. Differential pulse polarography (DPP) of 2-furaldehyde and related compounds is well established,l8,19 and was used to determine the effective- ness of the extraction of the target analyte from transformer oil. Experimental Reagents and Solutions All solutions were prepared with deionized water from a Whatman R050 reverse osmosis-deionizer system (Whatman Labsales, Maidstone, UK). Reagents used were sodium per- chlorate (AnalaR grade, Merck, Poole, UK), sodium hydroxide (analytical-reagent grade, Rh6ne-Poulenc, Manchester, UK), 2-furaldehyde (298% purity, Fisons, Loughborough, UK) and 2-acetylfuran (Aldrich, Dorset, UK).Transformer oil standards containing 2-furaldehyde and 2-acetyl furan were supplied by National Power (Surrey, UK) . Solution preparation The carrier solution was 0.1 mol 1-l NaOH-O.1 mol 1-I NaC104.756 Analyst, June 1996, Vol. 121 Equipment and Methods Current-potential curves (14) were obtained by linear scan cyclic voltammetry at a platinum electrode. The detector assembly was a Metrohm 656 electrochemical detector (Met- rohm AG, Herisau, Switzerland) with a thin-layer flow cell. The flow cell comprised a Metrohm platinum working electrode (area 5.3 mm2), glassy carbon auxiliary electrode, and an SCE. The platinum electrode was polished with Metrohm coarse (0363 19) and fine (0363 18) polishing compounds, rinsed with deionized water, sonicated in a water bath for 5 min, and dried before use.The glassy carbon and reference electrodes were used as supplied. The detector was used in conjunction with an FIAstar 5020 Flow Injection Analyser pumping module with flow-through injector (Tecator AB, Hoganas, Sweden). The FI system included two 4-channel peristaltic pumps controlled by a microprocessor. The pumps can be operated independently, e.g., for stopped-flow or intermittent pumping. A macrodialyser cell, SpectraPore MacroDialyser Cell, (Spectrum Medical Industries, Houston, Texas, USA), was used to partition the analyte from the oil to the supporting electrolyte carrier for detection. Samples of 2-furaldehyde in virgin transformer oil were pumped through one chamber of the cell, whilst a supporting electrolyte stream (0.1 mol 1-1 NaOH-O.1 mol 1-l NaC104), was pumped through the adjacent chamber (pump rates = 0.3 ml min-1). The two chambers were separated by a Durapore HVLP04700 (0.45 pm pore diameter) membrane [Millipore (UK), Watford, UK]. The membrane was specially manufactured from polyvinylidene difluoride to facilitate partition between aqueous and non-aqueous solutions. The supporting electrolyte stream could then be introduced into the sample loop for detection using PAD-FI (Fig. 1). Experiments in quiescent conditions were performed using the same working and counter electrodes in conjunction with an Ag/AgCl (saturated KC1) reference electrode. The electrolytic cell was a 25 cm3 beaker. The sample filling was performed by aspiration, via the injection valve tube which was connected to a pump tube.When the valve was activated the sample loop was interposed in the carrier stream. It is necessary to maintain the valve in the inject position for a certain period of time so that the sample loop can be completely emptied; a 10 s period was allowed between samples. In order to establish a stable baseline for the carrier it was necessary to earth the pump tubing using in-line copper tubing. A pulse suppressor was introduced between the pump and the flow through detector. These measures reduced baseline noise but increased injection noise which interfered with the signal. The injection noise was removed from the signal by sufficiently lengthening the tubing from the damper to the detector, and by keeping the injection time to a minimum.An injection time of 10 s was found to be satisfactory. However, for flow rates below 0.6 ml min-1 the sample loop (100 p1) was not fully emptied. Transformer oil Reagent Macrodialysex cell - Injection valve Analogue Fig. 1 Schematic of detection system for transformer oil contaminants. The potential waveform was generated by a Dionex Pulsed Electrochemical Detector (Dionex, Sunnyvale, CA, USA). The I-E curves and PAD peaks were recorded on a Philips PM 827 1 x-y/t chart recorder (Philips, Eindhoven, The Netherlands). For differential pulse polarography (DPP) an EG & G Princeton Applied Research (Princeton, NJ, USA) polaro- graphic analyser and stripping voltammeter, Model 264, was used in conjunction with a Model 303, hanging/dropping mercury electrode stand.The standard platinum auxiliary and dropping mercury electrodes were used but in order to reduce interference due to contamination, a special Ag/AgCl reference electrode, similar to that described by Torrance and Gatford,20 was used. This electrode utilizes a cracked borosilicate glass junction design which is easier to clean and remove con- taminants from than the Vycor frit Ag/AgCl reference electrode that is supplied with the Model 303 electrode stand. All of the analysed samples were first purged with white-spot nitrogen to remove dissolved oxygen for 4 min before each analysis. The sample volume during polarographic analysis was always kept constant (10 ml) so as to remove any variance that may arise from accumulation of the analyte on the mercury drop surface.21 The polarographic glass cup was washed in dilute hydrochloric acid, then rinsed in acetone and deionized water between experiments.All experiments were performed at an ambient temperature of 20 "C. Results and Discussion Voltammetry Quiescent conditions Cyclic voltammetry experiments carried out in quiescent conditions showed that 2-furaldehyde progressively fouled the electrode surface on repeated cycling. Fig. 2 shows cyclic voltammograms recorded at a clean platinum electrode in: (a) background supporting electrolyte solution (0.1 moll-' NaOH- 0.1 moll-' NaC104), (b) 100 ppm 2-furaldehyde in supporting electrolyte, and (c) at a 'fouled' Pt electrode (in supporting electrolyte) which had undergone 10 repeated cycles in 100 ppm 2-furaldehyde in supporting electrolyte and gentle rinsing in de-ionized water.Scan (c) showed a decrease in electrode response, at the detection potentials (around -0.8 V versus Ag/AgCl), and also 0.7 0.2 4.3 4.8 -1.3 E l V versus AglAgC1 Fig. 2 Current versus potential curves obtained by cyclic voltammetry at a platinum electrode under quiescent conditions in: (a, 0) background supporting electrolyte solution (0.1 moll-' NaOH-O.1 mol NaC104), (b, A) 100 ppm 2-furaldehyde in supporting electrolyte, and (c, 0) at a 'fouled' Pt electrode (in supporting electrolyte) which had undergone 10 repeated cycles in 100 ppm 2-furaldehyde in supporting electrolyte and gentle rinsing in deionised water. Scan rate; 100 mV s-l.Analyst, June 1996, Vol.121 757 the appearance of small reduction and oxidation peaks at about 0.45 and 0.15 V versus Ag/AgCl, respectively. Hydrodynamic conditions Cyclic voltammograms of the supporting electrolyte carrier (0.1 moll-1 NaOH-0.1 moll-' NaC104), and 100,200 and 400 ppm concentrations of 2-furaldehyde, respectively, using the FI detector during fluid flow (0.4 ml min-1) and cycling between +1200 and -1200 mV at 100 mV s-1, are shown in Fig. 3. Several well-defined surface reactions of the platinum electrode can be seen. The upper cathodic I-E curve (negative potential scan) shows the reduction peak of surface oxides with a maximum at about -400 mV. The adsorption peak of hydrogen22-24 occurs between -650 and -900 mV. The lower anodic I-E curve (positive potential scan) shows the oxidation peaks for the dissolution of adsorbed hydrogen22-24 in the voltage range between -850 and -500 mV.Between about -300 and +800 mV platinum forms surface oxides, and oxygen evolution starts at about +800 mV.22-24 It was noted that 2-furaldehyde suppresses the cathodic current of the reduction peak of surface oxides and the adsorption peak of hydrogen, and lowers the overpotential for hydrogen evolution during the negative potential scan, between about - 1000 and - 1 100 mV. During the positive potential scan 2-furaldehyde suppressed the anodic current of the oxidation peak of adsorbed hydrogen and enhanced the anodic current of formation of surface oxides on the platinum electrode between about -300 and +800 mV.2-Furaldehyde suppressed the anodic current of oxygen evolution between about +750 and +900 mV. During the cyclic voltammetry experiments the following processes 0ccur:22-2~ (1) Adsorbed 2-furaldehyde is catalytic- ally oxidized during the positive scan at about -300 mV by the formation of PtOH, resulting in an enhanced anodic response. (2) The anodic response for 2-furaldehyde is reduced due to the formation bf the catalytically inactive PtO, giving rise to a plateau betiyeen about - 100 and +600 mV. (3) 2-furaldehyde blocks complete coverage of the electrode with PtO, hence the reduc~on in the cathodic response for the reduction of surface oxides at about -400 mV during the negative potential scan. (4) In the region between about -400 and -900 mV 2-furaldehyde is adsorbed strongly at the oxide free platinum surface, resulting in a decrease in the number of surface sites available for the adsorption of atomic hydrogen.-ve 4 +ve +1200 +600 0 -600 -1200 E f mV versus SCE Fig. 3 Current versus potential curves obtained by cyclic voltammetry at a platinum electrode under fluid flow in 2-furaldehyde in 0.1 rnol 1-I NaOH-O. 1 moll-' NaC104 reference solutions: 0 ppm, H 100 ppm, A 200 ppm, V 400 ppm. Flow rate; 0.4 ml min-1. Scan rate, 100 mV s-1. Extraction of 2-Furaldehyde and 2-Acetylfuran From Transformer Oils 2-Furaldehyde and 2-acetylfuran (a related compound also formed by the decomposition of cellulose) may be determined by DPP since the carbonyl and acetyl groups undergo reduction at a mercury electrode between approximately -1000 and - I800 mV versus Ag/AgCl reference electrode, depending on pH.18719 DPP was performed on 2-furaldehyde in 0.1 mol 1-I NaOH-0.1 moll-' NaC104, aqueous standards and on extracts from 2-furaldehyde-transformer oil samples in the supporting electrolyte stream (0.1 moll-1 NaOH-O.l mol 1-1 NaC104), for comparison.DPP was also performed on similar oil extracts containing both 2-furaldehyde and 2-acetylfuran. Extractant efficiency depends on the relative flows of the transformer oil and reagent through the two compartments of the macrodialyser cell. For these trials a compromise was made between extraction efficiency and sample throughput. The extractant efficiency was examined for the contaminant range in and above that of analytical interest (1-1000 ppm) at flow rates of 0.3 ml min-1.A typical polarogram of the extract from a 1000 ppm 2-furaldehyde oil sample showed the expected DPP profile for 2-furaldehyde in 0.1 mol 1-1 NaOH-O.1 mol 1-l NaC104, with a reduction peak around - 1400 mV. The DPP profile (Fig. 4) for the extract from a 1000 pprn 2-furaldehyde- 2-acetylfuran oil sample was similar but with a small secondary peak at -1570 mV corresponding to the co-extracted acetyl- furan, which was identified from standard additions. DPP was performed on standard solutions of 2-furaldehyde and 2-acet- ylfuran (100 ppm) in 0.1 mol 1 - 1 NaOH-O.l mol 1-1 NaC104 (Fig. 5). It was noted that the reduction of 2-acetylfuran gave much larger current values than the reduction of 2-furaldehyde, the opposite effect to that observed in the extract from the 1000 ppm 2-furaldehyde-2-acetylfuran oil sample.This suggests that 2-furaldehyde is much more readily partitioned from the oil into the aqueous extraction solution. The extraction efficiency for 2-furaldehyde was determined from comparisons between reduction peaks obtained for DPP in 2-furaldehyde standard solutions and oil extract solutions, and was found to be 15% at 0.3 ml min-1. Other workers have reported that the levels of 2-acetylfuran and related compounds are much lower than 2-furaldehyde in problem transformers (by a factor greater than 100 : l).1,25,26 Moreover, the poor extrac- tion efficiency for 2-acetylfuran compared to 2-furaldehyde would further decrease the interference in practical measure- ments, hence 2-furaldehyde alone was tested with PAD-FI.18.0 13.5 5. @ 9.0 5 0 4.5 W 0 -0.8 -1.0 -1.2 -1.4 -1.6 -1.8 I I L EN versus Ag/AgCI Fig. 4 Differential pulse polarogram of sample extracted from transformer oil, containing 1000 ppm of both 2-furaldehyde (A) and 2-acetyl furan (B), into 0.1 mol 1-1 NaOH-O.1 rnol 1-1 NaC104. Scan rate, 5 mV s-1. Pulse height, 50 mV.758 Analyst, June 1996, Vol. 121 Pulsed Amperometric Detection Flow Injection The extent to which 2-furaldehyde suppressed the cathodic current of the adsorption peak of hydrogen was found to be influenced by 2-furaldehyde concentration. PAD was carried out in this region for the indirect determination of 2-furaldehyde at an 'oxide free' platinum electrode. The potential waveform- current detection programmes used are described in Table l(a) and (b), based on the appearance of the cathodic signals for hydrogen.Optimum detection potentials, El, for PAD were between -750 and -800 mV. Values of potentials E2 and E3 for reduction of the electrode surface and oxidative cleaning were - 1200 and +800 V, respectively. The lengthening of the tubing from the damper to the detector increased sample plug diffusion which in turn increased the peak dispersion. The increased peak dispersion produced smaller peak heights but did not change peak areas. In FI dispersion is defined as the ratio of concentrations before and after the dispersion process has taken place in the element of fluid that yields the analytical signal. In a straight tube it is the result of radial and axial mixing, due to convection and diffusion.The rate of equilibration and baseline drift were determined. It was found that severe baseline drift in PAD continued for longer than 20 min. We attribute this to the reported gradual increase in true electrode area which occurs with surface reconstruction under the repeated conditions of alternate anodic and cathodic polarizations in the PAD waveform.14 During the detection process surface reconstruction takes place which is caused by repeated cycles of oxide formation and dissolution. 24 18 5 P 12 B 6 A -1.0 -13 -1.4 -1.6 -1.8 EIV versus Ag/AgCI Fig. 5 Differential pulse polarogram of 100 ppm each of 2-furaldehyde (A) and 2-acetyl furan (B) in 0.1 mol I-' NaOH-O. 1 moll- NaC104. Scan rate, 5 mV s-l. Pulse height, 50 mV.Table 1 Waveform specification for pulsed amperometric detection Potential/mV versus SCE Time/s Function (a) El -800 0 (b) El -750 0.50 Detection 0.59 Reduction of electrode 0.70 Oxidative cleaning EZ -1200 0.51 E3 +800 0.60 Integration period 0.30 to 0.50 s The carrier baseline was run for 2 hours to allow the platinum electrode to attain an equilibrium number of active surface sites, which reduced the baseline drift dramatically. It is important also that the ranges of the potential changes are chosen to span the majority of the voltammetric region for the surface oxide formation; however the potential should not exceed values for onset of significant evolution of oxygen. PAD was carried out on 2-furaldehyde samples of concen- trations between 1 and 80 ppm in carrier, respectively. The results are shown in Fig.6(a) and 6(b); the response was taken to be the peak height. For 2-furaldehyde concentrations in the range 1 to 16 ppm the waveform described in Table l(a) was used, where El = -800 mV. For a higher concentration range, 5-80 ppm, it was necessary to modify the waveform for one with El = -750 mV [Table l(b)]. Plots of 2-furaldehyde concentration against response gave linear regions for concen- trations in the range 1-8 ppm (regression equation; y = 0.861 + 0.293x, Y = 0.994, n = 5) at a pump rate of 0.8 ml min-1, El of -800 mV, and injection volume of 100 p1 [Fig. 7(a)], and in the range 5 4 0 ppm (regression equation; y = 0.642 + O.O67x, Y = 0.998, n = 5) at a pump rate of 0.4 ml min-l, El of -750 mV, and injection volume of 67 p1 [Fig. 7(b)].The limit of detection for 2-furaldehyde was determined by PAD-FI to be 1 ppm for E l = -750 mV (pump rate = 0.4 ml min-1; injection volume = 67 pl) and 0.5 ppm for E l = -800 mV (pump rate = 0.8 ml min-1; injection volume = 100 pl) respectively, using three times the standard deviation of the baseline for each system. The rate at which an analyte accumulates at the electrode surface is transport limited, hence the response which is proportional to surface coverage is often a linear function of concentration. The adsorption of 2-furaldehyde is also affected by changes in El. 2-Furaldehyde appeared to adsorb more strongly at the more negative potential. It appears that other electrode surface reactions occur for 2-furaldehyde concentra- tions above about 50 pprn at El = -750 mV and above about 10 ppm at El = -800 mV, hence the deviation from linearity of the concentration against response plots.This suggests that T 10 min H I I Fig. 6 PAD-FI detection peaks for 2-furaldehyde in 0.1 mol I-' NaOH- 0.1 moll-' NaC104. (a) El = -800 mV, E2 = - 1200 mV, E3 = +800 mV. Sample volume, 100 pl; pump rate, 0.8 ml min-I. (b) El = -750 mV, E2 = -1200 mV, E3 = +800 mV. Sample volume, 67 pl: pump rate, 0.4 rnl min-1.Analyst, June 1996, Vol. 121 759 the response is not entirely controlled by the adsorption isotherm which limits the surface coverage by adsorbed analyte. Conclusion This study shows that the combination of flow through PAD and FI can be used to produce a system suitable for the determina- tion of 2-furaldehyde in alkaline, aqueous media.The work also demonstrates the feasibility of on-line detection of transformer oil contaminants using selective partitioning membranes with a continuous flow extraction system for in-situ monitoring. Over a period of four weeks no noticeable deterioration in extraction efficiency was observed; however over extended periods of time it is likely that if extraction efficiency changes then recalibration of the system would be required. 0 2 4 6 8 10 12 14 16 2-furaidehyde concentration / ppm 0 10 20 30 40 50 60 70 80 2-furaldehyde concentration I ppm Fig. 7 Mean k s response against 2-furaldehyde concentration (a) El = -800 mV. Sample volume, 100 vl; pump rate, 0.8 ml min-1. (b) E l = -750 mV. Sample volume, 67 1.11; pump rate, 0.4 ml min-1.This research was supported by a grant from EA Technology Ltd., Chester, England. References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Emsley, A. M., and Stevens, G. C., IEE Proc.-Sci. Meas. Technol., 1994, 141, 324, and references cited therein. Szuta, J., Energopomiar (Poland), 1969, 15, 21. Domun, M., Cornfield, G., and Hadfield, A., CIGRE Symposium, Vienna, 1987, 1020-80, 1. Rybakov, L. M., Rybakova, G. A., Belyakov, I. G., and Akhmetshin, R. S., Prom. Energ. (USSR), 1986, 10,42. Burton, P. J., Carballiera, M., Duval, M., Fuller, C. W., Graham, J., de Pablo, A., Samat, J., and Spicar, E., Proceedings of the CIGRE Conference (Paris), 1988, Paper 15-08. Neuburger, G. G., and Johnson, D. C., Anal. Chem., 1988, 60, Andrew, K.N., Blundell, N. J., Price, D., and Worsfold, P. J., Anal. Chem., 1994, 66, 917A. Gunther, A., and Bilitewski, U., Anal. Chim. Acra., 1995, 300, 117. Johnson, D. C., Polta, J. A., Polta, T. Z., Neuberger, G. G., Johnson, J. Tang, A. P. C., Yeo, I. H., and Baur, J., J . Chem. SOC., Faraday Trans. I , 1986, 82, 1081. Polta, J. A. and Johnson, D. C., Anal. Chem., 1985, 57, 1373. Ngoviwatchai, A., and Johnson, D. C., Anal. Chim. Ada., 1988,215, 1. Lacourse, W. R., Jackson, W. A., and Johnson, D. C., Anal. Chem., 1989,61, 2466. Lacourse, W. R., Johnson, D. C., Rey, M. A,, and Slingsby, R. W., Anal. Chem., 1991, 63, 134. LaCourse, W. R., Mead, D. A. Jr., and Johnson, D. C., Anal. Chem., 1990, 62, 220. Larew, L. A., and Johnson, D. C., Anal. Chem., 1988, 60, 1867. Larew, L. A., Mead, D. A., and Johnson, D. C., Anal. Chim. Acra., 1988, 204, 43. Tait, R. J., Bury, P. C., Finnin, B. C., Reed, B. L., and Bond, A. M., Anal. Chem., 1993, 65, 3252. Zuman, P., Organic Polarographic Analysis, Pergamon Press, Oxford, 1964, and references cited therein. Meites, L., Polurographic Techniques, Interscience, New York, 1955, and references cited therein. Torrance, K., and Gatford, C., Talanra, 1985, 32, 273. Kalvoda, R., Anal. Chim. Acta., 1982, 138, 11. Hughes, S., and Johnson, D. C., Anal. Chim. A m . , 1981, 132, 11. Hughes, S., Meschi, P. L., and Johnson, D. C., Anal. Chim. Acta., 1981, 132, 1. Koryta, J., Dvorak, J., and Kavan, L., Principles of Electrochemistry, Wiley, New York, 2nd edn., 1993, pp. 352-368, and references cited therein. Jakob, F., and Haupert, T., presented at the 16th Technical Conference of the International Electrical Testing Association (NETA), Atlanta, GA, USA, March 1994. Kan, H., Miyamoto, T., Makino, Y., Narnba, S., and Hara, T., presented at the IEEE International Symposium on Electrical Insulation, Pittsburgh, PA USA, June 5-8, 1994. 2288. Paper 51083426 Received December 22, 1995 Accepted March 26, 1996

ISSN:0003-2654    

DOI:10.1039/AN9962100755

出版商:RSC    

年代:1996

数据来源: RSC

摘要:

Analyst, June 1996, Vol. 121 (761-766) 76 1 Characterization of Carbon Paste Electrodes ln Vitm for Simultaneous Amperometric Measurement of Changes in Oxygen and Ascorbic Acid Concentrations ln Vivo* John P. Lowrya, Martyn G. Boutelleb, Robert D. O’Neillc and Marianne Fillenza a University Laboratory of Physiology, Parks Road, Oxford, UK OX1 3PT. E-mail: jlowry@sable.ox.ac.uk Department of Chemistry, Kings College London, Strand, London, UK WC2R 2LS L‘ Department of Chemistry, University College Dublin, Belfield, Dublin 4 , Ireland Differential-pulse amperometry, an established technique for the in vivo monitoring of dopamine in brain extracellular fluid (ECF), was extended to the simultaneous electrochemical detection of molecular oxygen (02) and ascorbic acid (AA). Lipid-treated carbon paste electrodes (LCPEs) were characterized in vitro using this technique and found to be ideally suited for the detection of both compounds. For 0 2 reduction, two equally sized cathodic pulses were applied, the first from a resting potential at -150 to -350 mV, which corresponds to the foot of the reduction wave for O2 at LCPEs, and the second from -350 to -550 mV, which corresponds to the peak of the reduction wave.Following the same criterion, equally sized anodic pulses were then applied from -150 to +50 mV and from +50 to +250 mV for AA oxidation. The complete sequence of pulses for O2 and AA detection lasts 1 s. Interference by O2 with AA currents and vice versa was not a problem. Also, several compounds present in brain ECF were tested and shown not to interfere appreciably with the amperometric signal for either compound. The technique was tested in vivo, and results from behavioural stimulation, achieved by the application of tail pinch, support the conclusion of simultaneous independent detection of changes in O2 and AA at LCPEs.Keywords: Diferential-pulse amperometry; lipid-modified carbon paste electrodes; oxygen; ascorbic acid; voltammetry in vivo Introduction The first report of voltammetry in brain tissue was by Clark et al.1 in 1958, when changes in 0 2 were reported at a noble metal electrode. This was followed in 1965 by a paper from the same group using ‘brain polarography’ to measure 0 2 cathod- ically with a glassy carbon electrode.2 Changes in an anodic signal were also observed in separate experiments and attributed to fluctuations in brain ascorbic acid (AA) levels. Almost a decade later, Kissinger et al.3 used a carbon-Nujol paste electrode implanted in rat striatum to study brain neu- rochemistry.Using cyclic voltammetry at 100 mV s-’, a single peak was observed that was attributed mainly to the oxidation of AA. Since these pioneering experiments, extensive research * Presented at Sensors and Signals 111, Malahide, Co. Dublin, Ireland, October 26-27, 1995. using in vivo electrochemical methods has been undertaken in many laboratories.495 This research has led to a parallel development of voltammetric techniques and sensors which are selective, sensitive and stable enough to provide electro- chemical signals related to neurotransmitters.68 However, most in vivo voltammetric experiments still involve carbon electrodes either as carbon paste electrodes (CPEs) or carbon fibre electrodes (CFEs).The latter are the most commonly used electrodes, principally because of their small diameter (5-50 pm), easily varied length (0-500 pm) and sensitivity. The main advantage of CPEs is their stability over several months of recording in vivo.9 Initially, the application of in vivo voltammetry was mainly limited to the detection of electroactive species. However, in recent years the range of analytes has been broadened by the use of enzyme-based sensors.1G12 A number of groups have been involved in the development and characterization of sensors based on the immobilization of oxidoreductase enzymes in poly(o-phenylenediamine) (PPD) films on Pt electrodes.l3-18 These electrodes possess a variety of properties in vitro, including a fast response time, freedom from protein and lipid fouling, effective elimination of interference by endogenous electroactive species and ease of miniaturization, all indicating potential suitability for analysis in biological systems. Of these properties, interference by endogenous species is probably the most critical with respect to their use in such a complex environment as the living brain. The principal interferents for enzyme-modified Pt/PPD electrodes are AA, which is present in high and varying concentrations in brain extracellular fluid (ECF),l9,20 and 02, since these sensors are ‘first generation’ and thus dependent on 0 2 for the formation of the signal generating H202.Preliminary results from the simultaneous monitoring of AA with a CPE and of glucose with a glucose oxidase (G0x)-modified Pt/PPD electrode in rat striatum suggest that interference from AA is not a problem. l4 To test for O2 interference in vivo, it is desirable to be able to monitor both 0 2 and glucose levels simultaneously in a manner similar to that achieved for glucose and AA. Thus, in order to monitor 0 2 in brain tissue, we have developed a double potential pulse technique with CPEs, similar to differential- pulse amperometry (DPA), applied to dopamine detection in vivo.8 This technique was also applied to the simultaneous detection of AA at the CPE. In this paper, we describe the development and in vitro characterization of the technique.The results suggest that both 0 2 and AA can be measured simultaneously and with minimum interference from endogenous species, indicating suitability for in vivo applications.762 Analyst, June 1996, VoE. 121 Experimental Reagents and Solutions L-Ascorbic acid and Triton-X 100 were obtained from Sigma (St. Louis, MO, USA). Compounds used in the interference study were dehydroascorbic acid (DHAA) (Aldrich, Mil- waukee, WI, USA), glutathione (Aldrich, oxidized form), L- cysteine (Sigma), uric acid (Sigma, potassium salt), L-tyrosine (Aldrich), L-tryp top han ( Aldric h), 5 - h y drox y indoleacetic acid (5-HIAA) (Sigma), dopamine (Sigma, hydrochloride), 3,4-di- hydroxyphenylacetic acid (DOPAC) (Sigma), 5-hydroxytrypta- mine (5-HT) (Sigma) and homovanillic acid (HVA) (Sigma). All chemicals were used as supplied.Carbon paste was prepared by thoroughly mixing 2.83 g of carbon powder (UCP-1-M, Ultra Carbon, Bay City, MI, USA) and 1.0 ml of silicone oil (Aldrich).21 A stock standard solution of 0.2% Triton-X 100 was prepared by dilution and stored at room temperature. Stock standard solutions (1 00 mmoll-l) of all other compounds were prepared at the beginning of each experiment. Experiments in vitro were carried out in a phosphate-buffered saline (PBS) solution (pH 7.4) consisting of NaCl (BDH, Poole, UK, AnalaR grade, 0.15 mol l-l), NaH2P04 (BDH, AnalaR grade, 0.04 mol 1-1) and NaOH (BDH, AnalaR grade, 0.04 moll-1). All solutions were prepared using deoxygenated, doubly distilled water.lnstrumentation and Software All experiments were microcomputer controlled with data acquisition accomplished using a National Instruments (NI, Austin, TX, USA) AT-MIO-16 data acquisition board and a specially designed low-noise, low-damping potentiostat (Bio- stat 11, Electrochemical and Medical Systems, Newbury, UK). Further noise reduction was achieved by using a data sampling rate of 10000 Hz and averaging to give 1 data point s-l. In- house software was written in the NI Labwindows (version 2.1) QuickBASIC environment to perform all electrochemical experiments and to collect, plot and analyse the data. The differential pulse amperometric program was set up to sample four channels per second, resulting in 250 points per 100 ms (the time duration for pulses; see Differential-pulse Amperometry for the Detection of 0 2 and Ascorbic Acid below).The current transients for the various pulses were examined using both the designed software and a digital storage oscilloscope (Hameg, Frankfurt, Germany, HM 205-3, 20 MHz). For maximum sensitivity 100 (02) and 150 (AA) points close to the end of each pulse were averaged to give 1 point per pulse s-1. A resting interval (i.e., the time between each pulse sequence) of 2 s was used in all pulse experiments. Working Electrode Preparation Carbon paste electrodes were made from 5T (125 pm id, 160 pm od) Teflon-coated silver wire (Clark Electromedical Instruments, Reading, UK, and Advent Research Materials, Halesworth, Suffolk, UK). The Teflon insulation was slid along the wire to create an approximately 1 mm deep cavity, which was packed with carbon paste using a bare silver wire as plunger.A small gold electrical contact was attached to the end of the wire. When not in use, electrodes were stored in PBS at 4 "C. Voltammetric Experiments In Vitro All experiments in vitro were performed in a standard three- electrode glass electrochemical cell containing 20 ml of PBS at room temperature. A saturated calomel electrode (SCE) was used as the reference electrode and a large silver wire, isolated in a compartment containing PBS, served as the auxiliary electrode. Three types of carbon paste working electrodes were used: unmodified electrodes (CPEs); electrodes treated with lipid by contact with brain tissue9.22 (LCPEs); and electrodes modified by immersion in surfactant solution (Triton-X 100, 0.2%) for 30 min (SCPEs). To attain effective de-aeration, all solutions were vigorously purged with 02-free N2 (BOC Gases, Guildford, Surrey, UK; average 0 2 content 2 ppm, maximum 0 2 content 5 ppm) for at least 30 min before recording began and an N2 atmosphere was maintained over the cell thereafter.In experiments involving solution 0 2 , either atmospheric air (from a RENA 101 or 102 air pump) or pure O2 (compressed gas) was bubbled through the PBS. Mixing in AA calibrations and interference experiments was achieved by bubbling with N2. Voltammetric techniques used were cyclic staircase voltam- metry at 50 mV s-1 and differential-pulse amperometry. Voltammetric Experiments In Vivo Male Sprague-Dawley rats (200-300 g) were anaesthetized with a combination of fentanyl-fluanisone (0.25 and 0.8 mg kg-l ip, respectively) and midazolam (0.4 mg kg-l ip) and placed in a stereotaxic frame.Body temperature was maintained at 37 "C with a heating pad (Braintree Scientific, Braintree, MA, USA). The CPE was implanted in the left striatum. Coordinates used with the skull levelled between bregma and lambda were A/P +1.0 (from bregma), M/L -2.5 and D/V -5.0 from dura. The reference electrode was placed in the cortex, the auxiliary electrode placed between the skull and dura and an earth wire attached to one of the support screws; the last three were Ag wires (200 pm). The reference potential provided by the Ag wire in brain tissue is very similar to that of the SCE.9 The electrodes were fixed to the skull with screws and dental acrylate.The animals were then allowed to recover from anaesthesia. Post-operative analgesia was provided in the form of a single injection of buprenorphine (0.1 mg kg-1 sc) given immediately following the surgery. The rats were housed in large plastic bowls (Johnson's Garden Centre, Oxford, UK), with free access to food and water. Experiments were carried out with the animal in its home bowl. The health of the animals was assessed following recovery according to published guidelines23324 and all procedures were specifically licensed under the Animals (Scientific Procedures) Act 1986. All data are reported as means f SEM with n = number of electrodes. The significance of differences observed was estimated using Student's two-tailed t-tests (Instat, V 2.02, GraphPad Software).Paired t-tests were used for comparing signals recorded with the same electrode (e.g., before and after lipid modification); unpaired t-tests were used for comparing data from different electrodes. Results and Discussion Differential-pulse Amperometry for the Detection of O2 and Ascorbic Acid Differential-pulse amperometry (DPA), where a double pulse of fixed potential difference is applied at fixed amplitude, has previously been used in vivo for the detection of catecholam- ine~.8,~~-26 As we are interested in monitoring O2 and AA simultaneously, we have extended this approach by applying two pairs of pulses, one cathodic and the other anodic. Reduction of 0 2 at carbon electrodes is a two-electron process producing H202.27928 The rate-limiting step for this reduction is the initial one-electron step followed by protonation of the superoxide ion and further reduction.27 In order to determine the position of 0 2 reduction on the voltage axis, cyclic staircase voltammetry was performed at Triton-X 1 00-modified electrodes (SCPEs), as surfactant treatment of CPEs has been shown to have the same effect as brain tissueAnalyst, June 1996, Vol.121 763 0- 2 2 -50- 0 s -100- -150- modification, i.e., removal of the oil from the surface, thus increasing the rate of charge transfer9922 (see Sensor Sensitivity below). Typical voltammograms recorded at 50 mV s-l with an SCPE in N2 (background) and air-saturated PBS solutions are shown in Fig. 1. The potentials used for the two cathodic pulses were chosen from these voltammograms.The first pulse was applied from the resting potential (-150 mV) to -350 mV, which corresponds to the foot of the reduction wave for 0 2 , and the second from -350 to -550 mV, which corresponds to the peak of the reduction wave (see Fig. 1). The oxidation of AA has been studied extensively at different electrode materials and is considered to follow an EC mechanism (electron transfer followed by a chemical reaction) involving a two-electron transfer.29.30 At carbon electrodes a large overpotential is required to drive the oxidation reaction, which means that the electron transfer can be considered irreversible and thus the subsequent chemical reaction has no effect on the stationary-electrode voltammogram.30 The oxida- tion of AA in vivo at CPEs has been well characteri~ed.~?~~ Current-voltage plots from linear staircase voltammetry show an oxidation peak at approximately 200 mV with the foot of the wave occurring at approximately +50 mV.Thus, for the anodic recordings, two equally sized pulses were applied, the first from a resting potential at - 150 to +50 mV and the second from +50 to +250 mV. The difference in the current (A0 sampled during these cathodic and anodic pulse pairs corresponds mainly to faradaic 0 2 reduction and AA oxidation, respectively, with little contribution from capacitance effects. A schematic diagram representing the potential-time waveform and current transients is shown in Fig. 2. The complete sequence of pulses lasts 1 s and involves the application of an initial rest potential pulse (100 ms), the two cathodic pulses (100 ms each), a second rest potential period (500 ms) and the two anodic pulses (100 ms each).The separating 500 ms rest potential allows concentra- tions of the analytes to recover and minimizes the possibility of interference between the cathodic and anodic signals. The current sampling periods for all pulses were determined by examining the experimental current transients (see Instrumental and Software). After applying a potential pulse, the capacitance current decays faster than the faradaic current, so all currents were measured towards the end of the pulse when the current had reached a plateau (see Fig. 2). A pulse-free period (resting interval) of 1 s was allowed between each complete pulse sequence to minimize depletion of the analytes in the restricted diffusion environment of brain tissue.32.33 The effective time resolution for 0 2 and AA detection was therefore 2 s.-200 ! 1 I I I I I I - 7 4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 Poten tia W Fig. 1 Typical cyclic staircase voltammograms recorded at 50 mV s-l with a surfactant (0.2% Triton-X 100)-treated carbon paste electrode (SME) in N2-saturated (dashed line) and air-saturated (solid line) PBS (pH 7.4). Sensor Sensitivity AA calibrations were performed in vitro at unmodified CPEs over the range 0-1000 pmol l-1. Calibrations for dissolved 0 2 were obtained in N2-purged, air-saturated and 02-saturated solutions where the concentrations of solution O2 were taken as 0, 20034.35 and 1250 pmol l-1,35 respectively.All calibrations were linear with r2 > 0.997. The sensitivities for 0 2 and AA at CPEs were -43 k 7 nA 1 mmol-1 (n = 5 ) and 49 f 10 nA 1 mmol-1 (n = 6), respectively. Carbon paste electrodes are known to be modified after contact with brain tissue by removal of the pasting oil from the active sites on the electrode surface by lipids, which constitute approximately 40% of the dry mass of brain t i ~ s u e . ~ , ~ ~ This treatment decreases the over-potential for AA oxidation and thus shifts the position of the wave to more negative potentials with the peak occurring at approximately +200 mV and diffusion-limited currents at higher potentials. At untreated CPEs no defined peak for AA oxidation is observed and diffusion-limited currents are approached at potentials close to +600 mV.22 Since we are interested in using CPEs in the living brain, we therefore examined the effect of tissue modification on the sensitivity of CPEs for O2 and AA using the developed DPA technique.Significant changes were not observed for either compound; the AA sensitivity decreased from 60 f 11 nA 1 mmol-1 (n = 3) to 35 f 7 nA 1 mmol-1 (n = 3 , p > 0.15, paired t-test), while the sensitivity to 0 2 increased from -45 k 13 nA 1 mmol-1 (n = 3) to -151 k 47 nA 1 mmol-l (n = 3, p < 0.1, paired t-test) after treatment. A decrease in sensitivity for AA oxidation at tissue-modified CPEs has previously been reported and attributed to the blocking effect of adsorbed lipids.22 Clearly, these adsorbed lipids do not block the passage of 0 2 to the electrode surface since the sensitivity increases, as would be expected from the increase in surface area caused by removal of the pasting oil.A similar phenomenon has been observed for lipid-treated Pt/PPD/GOx electrodes, where the incorporated lipids have been used to improve the permselective properties of the electrodes by reducing interference from species such as AA, uric acid and acetaminophen, while having essentially no effect on H202 oxidation.36 All subsequent experiments were therefore performed with electrodes treated with lipid by contact with brain tissue (LCPEs). Typical current-time responses for an AA calibration and a change in solution O2 from N2 to air saturation at LCPEs are shown in Fig. 3(a) and (b), respectively.The detection limits, I I Fig. 2 Schematic diagram of the potential-time waveform corresponding to the differential-pulse amperometric (DPA) technique with alternating pulse pairs for detecting O2 reduction and AA oxidation. Current-time traces for the various pulses are also shown with solid bars indicating approximate data sampling periods.764 Analyst, June 1996, Vol. 121 defined as the analyte concentration yielding a signal equal to three times the standard deviation of the background current, were calculated at 10 k 3 pmol 1-l (n = 5) for AA and 8 k 1 pmol l-1 (n = 4) for 02. Sensor Selectivity To test for the possibility of cross interference, i.e., interference by AA in 0 2 detection and vice versa, we examined in vitro the effect of 1 mmol 1-l AA on the cathodic At for N2- and air- saturated PBS and the effect of air saturation on the anodic At signal in the absence and presence of 1 mmol 1-1 AA.No significant differences were detected in the cathodic At for N2- saturated PBS in the absence (-78.7 f 10.2 nA, n = 3) and presence (-78.8 f 9.8 nA, n = 3, p > 0.9) of 1 mmol l-1 AA [see Fig. 3(a)], or in the air-saturated response in the absence (-108.2 k 8.9 nA, n = 3) and presence (-108.2 k 9.1 nA, n = 3, p > 0.9) of 1 mmol 1-l AA, indicating that interference by AA in 0 2 detection is not a problem. Similarly, interference by 0 2 in AA detection was not observed as no significant difference was detected in the anodic At signal in N2-saturated (84.3 k 26.4 nA, n = 3) and air-saturated (86.3 k 26.6 nA, n = 3,p > 0.9) PBS, or in the 1 mmol 1-* AA signal in Nz-saturated (102 f 21 nA, n = 3) and air-saturated (103 k 22 nA, n = 3, p > 1.5) solutions [see Fig.3(b)]. The large background currents for the cathodic and anodic At in N2-saturated PBS result from increased surface wettability due to lipid removal of the insulating 0i1.9,22 The selectivity of LCPEs for 0 2 and AA relative to a variety of potential interferents present in brain ECF was also characterized in vitro. The compounds tested included the neurotransmitters dopamine (DA) and 5-hydroxytryptamine ::;70 0 7 14 21 28 Time/min 504 0 7 14 21 28 Ttme/min Fig. 3 (a) Typical current-time response from potential pulses from - 150 to +50 mV and from +50 to +250 mV recorded at a lipid-treated carbon paste electrode (LCPE) in N2-saturated PBS (pH 7.4) for successive 200 pmol 1-1 increments of ascorbic acid (AA) in the range 0-1000 pmol 1-I.Also shown is the absolute O2 current ( I AI I ) from potential pulses from - 150 to -350 mV and from -350 to -550 mV recorded during the AA calibration. (b) Typical amperometric responses recorded at an LCPE in PBS (pH 7.4) during bubbling with either N2 or air. Absolute 0 2 current (1 All) from potential pulses from -150 to 350 mV and from -350 to -550 mV and AA current (AZll .5) from potential pulses from - 150 to +50 mV and from +50 to +250 mV. (5-HT), their metabolites 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA) and 5-hydroxyin- doleacetic acid (5-HIAA) and other electroactive species such as L-tyrosine, L-cysteine, L-tryptophan, L-glutathione, dehydro- ascorbic acid and the purine metabolite uric acid (UA).The results are summarized in Table 1 and, although in most instances there was little or no immediate response to the interferent, sometimes slight positive or negative drifts were observed over several minutes. However, it is clear from Table 1 that the above compounds have no appreciable effect on the sensor response for 0 2 or AA. Hence these in vitru results suggest that both cross interference and direct interference by endogenous compounds are minimal, and that these electrodes should have interference-free signals for 0 2 and AA in vivo. Experiments in Vivo As our primary interest is the direct measurement of O2 and AA in brain ECF, it remained to be seen whether the electrodes responded to changes in both compounds in vivo.Carbon paste electrodes were therefore implanted in the rat brain in a region known as the corpus striatum. The concentration of AA in the extracellular fluid (ECF) of the brain has been estimated to be between 100 and 500 pmol l-1.8,38 The O2 concentration in the ECF is a dynamic balance between supply of 0 2 via blood flow in capillaries and 0 2 consumption associated with metabolism in ~ e l l s . ~ 1 Dissolved 0 2 concentrations form a gradient which is highest at the capillary wall and lowest at the site of metabolic consumption. The range measured with implanted micro- electrodes is 5-50 pmol 1-1.28737941 As the dimension (125 pm active id) of the sensors used in this study is greater than the scale of a capillary zone ( < 100 pm),42 an average tissue oxygen level should be detected. Tail pinch has been used in the past as a stimulus to produce behavioural activation.43.44 The stimulation is achieved by applying gentle pressure to the rat’s tail for 5 min by means of a paper clip attached about 3 cm from the tip.This stimulus produces a well characterized behaviour pattern which consists of gnawing, licking, eating and a general increase in the level of motor activity.44 The rat vigorously chewed a wooden stick held by the observer until the clip was removed. An example of the effect of a 5 min tail pinch on the cathodic and anodic currents (At) recorded with a CPE implanted in the left striatum of a freely moving rat is shown in Fig.4. Both signals showed an Table 1 In vitro response of lipid-treated carbon paste electrodes (n = 3) for a variety of potential interferents expressed as a percentage of the O2 (50 pmol l-’)*8.37 and AA (500 pmol l-l)8,38 currents at physiologically relevant concentrations Interferent * DHAA Glutathione Dopa m i n e DOPAC HVA Uric acid L-Cy steine L-Tyrosine - 5-HT 5-HIAA L-Tryptophan 0 2 (%I 100 (8 _+ 2 nA) 1.68 f 0.45 3.59 f 0.18 < 0.01 0.07 f 0.02 < 0.01 0.29 f 0.05 0.59 k 0.33 0.19 f 0.07 3.21 +_ 0.94 1.67 f 0.64 1.72 k 0.60 AA (%) 100 (17 k 3 nA) 0.66 f 0.45 1.02 k 0.48 0.05 k 0.02 2.45 f 1.26 < 0.01 0.06 f 0.04 0.90 k 0.25 0.08 k 0.01 0.16 k 0.08 0.39 f 0.14 0.21 f 0.05 * DHAA, dehydroascorbic acid; DOPAC, 3,4-dihydroxyphenylacetic acid; 5-HT, 5-hydroxytryptamine; HVA, homovanillic acid; 5-HIAA, 5-hydroxyindoleacetic acid.100 pmol 1- I interferent or brain extracellular fluid (ECF) ~oncentration8.”~~0 if known: glutathione 50, dopamine 0.05, DOPAC 20, 5-HT 0.01, HVA 10, uric acid 50, 5-HIAA and L-cysteine 50 pmol 1-1.Analyst, June 1996, Vol. 121 765 immediate increase in concentration on application of the stimulus. The increase in the cathodic response rapidly fell to prestimulus levels when the stimulus was removed whereas the anodic response remained elevated over the period of recording. An increase in AA concentration in response to tail pinch is consistent with previous reports with CPEs implanted in rat striatum using constant-potential amper0metry,459~6 although with the latter technique the time course was very much faster.This difference may be due to the different sampling rates of the two techniques and the resultant different degrees of depletion. Although the direct effect of tail pinch on 0 2 has not been reported, it is known to increase striatal blood fl0w,~7 which would imply an increase in striatal 0 2 levels. The spontaneous baseline fluctuations observed in the cathodic signal shown in Fig. 4(a) were also observed by Clark and Lyons in 1965, being described as ‘abrupt and momentary changes’.2 These fluctuations appear to reflect rapid changes in 0 2 associated with the awake, freely moving animal, as they completely disappear when the animal is anaesthetized and after death.48 However, further characterization in vivo is necessary to confirm that these signals originate from changes in 0 2 and AA concentrations.Hence in vivo experiments involving induced changes in the levels of both compounds, by systemic administration of AA and inhalation of N2 and O2 gases, are in progress. Also, preliminary results for the simultaneous mon- itoring of the cathodic (A0 signal with an implanted CPE, and glucose with a Pt/PPD/GOx electrode, in rat striatum suggest that O2 interference is not a problem at the latter ‘first- generation’ sens0rs.~9 Conclusions Characterization in vitro of LCPEs using a differential-pulse amperometric technique suggest that 0 2 and AA can be measured simultaneously. The alternating pulse pairs chosen for monitoring 0 2 reduction and AA oxidation were -350 to -550 mV and +50 to +250 mV, respectively.Background pulses from 180 (4 1 170 - 3 160 h 5? c 6 L 3 150 0 I Tail Pinch 140 I I 1 1 -I 0 10 20 30 40 lime/min 127 3 126 2 2 E 125 0 5 5 124 t ’ 1 1 I I TiWmin 0 10 20 30 40 Fig. 4 Example of the effect of a 5 min tail pinch on the response of an LCPE implanted in the left striatum of a freely moving rat. (a) Absolute current ( I AZ I ) from the cathodic 0 2 reduction pulses from - 150 to -350 mV and from -350 to -550 mV. (b) Current (A0 from the anodic AA oxidation pulses from -150 to +50 mV and from +50 to +250 mV. a rest potential of -150 to -350 mV and +50 mV were subtracted in each case to minimize capacitance effects in the signals. The in vitro signals were shown to be free of both cross interference and direct interference from several endogenous compounds present in brain ECF.Preliminary experiments in vivo in the mammalian brain suggest that these electrodes do respond independently to changes in 0 2 and AA. Further work on in vivo characterization is in progress and will be submitted for publication at a later date. Although the impetus for the development of this technique was to permit the monitoring of both AA and O2 changes in vivo, and to examine their effects on the response of first-generation Pt/PPD/GOx sensors, the ability to measure O2 at an implanted CPE may have important applications in other studies, such as the use of 0 2 changes as an index of local blood flow. 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ISSN:0003-2654    

DOI:10.1039/AN9962100761

出版商:RSC    

年代:1996

数据来源: RSC