Several methods of mechanical and chemical immobilization for fabrication of ...... Among the great variety of organic chromophores such as azo dyes, nitrophenols .... Through not a physical method in its original sense, lipophilic indicators dissolved in a ..... The aim of this thesis is to develop some modern, powerful, and.
IN THE NAME OF God SOME NOVEL TECHNIQUES IN WIDE-TO-FULL RANGE OPTICAL PH MEASUREMENT AND A NOVEL METHOD FOR FABRICATION OF PH OPTODES BY ABOLFAZL ROSTAMZADEH THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (M.Sc.) IN ANALYTICAL CHEMISTRY SHIRAZ UNIVERSITY SHIRAZ, IRAN EVALUATED AND APPROVED BY THE THESIS COMMITTEE AS:
…………………………… A. Safavi, Ph.D., Prof. of Chemistry (Chairman) …………………………… N. Maleki, M. Sc., Assist. Prof. of Chemistry …………………………… M. Shamsipur, Ph.D., Prof. of Chemistry …………………………… A. K. Abbaspour, Ph.D., Assist. Prof. of Chemistry
April 2001
38
ABSTRACT
NOVEL TECHNIQUES IN WIDE-TO-FULL RANGE OPTICAL PH MEASUREMENT AND A NOVEL METHOD FOR FABRICATION OF OPTODES BY ABOLFAZL ROSTAMZADEH Several methods of mechanical and chemical immobilization for fabrication of some optodes are investigated. By applying a new procedure, seven pH optodes have been constructed using some unmodified hydrophilic transparent triacetyl cellulose membranes. Although in batch assays with limited linear dynamic range (often 2-4 pH units only), it is necessary to operate via steady-state conditions; in this research it is shown that this is not essential. The treatment of optodes with samples in a controlled manner and under precise conditions gives accurate and precise analytical results. In order to correlate the diffusionbased responses with pH values an Artificial Neural Networks model is applied. As a result, pH-measuring range is extended from 2-4 pH units in conventional pH measurements to more than 11 units. In addition, here it is shown that a highly selective and sensitive optode can be used for wide range pH measurements (pH=0-10) in a time-based flow analysis and the validity of the hypothesis is investigated from the experimental viewpoint. Advances in efficient and rapid data acquisition using a charge-coupled device (CCD) are combined with known pH optodes to create an array sensor for pH measurements in full range. In order to associate sensor 39
array responses with pH values, several mathematical models with Solver tool of Microsoft Excel software are investigated.
40
TABLE OF CONTENTS CONTENTS
PAGE
LIST OF TABLES ………………………………………………...…...xii LIST OF FIGURES ……………………………………………..……...xv
CHAPTER ONE: INTRODUCTION 1.1 pH Optical Sensors ……………………………………..…………....1 1.1.1 Development of pH Optodes …………….………………………2 1.1.1.1 Problems Inherent to the Use of pH Electrodes …………..…2 1.1.1.2 pH Optosensing versus pH Electrosensing ……...…………..3 1.1.2 pH Indicators …………………………………………………….5 1.1.2.1 Indicator Theory ……………………………………………..6 1.1.2.2 Absorbance-Based Indicators ………………………………..8 1.1.2.3 Influence of Important Factors on Indicator Performance .…..8 1.1.2.3.1 Ionic Strength Effects ………………………………….…8 1.1.2.3.2 Effect of Solvents ……………………………………….11 1.1.2.3.3 Influence of Proteins and Colloids ……………………...11 1.1.2.3.4 Influence of Temperature ...……………………………..11 1.1.2.4 Acid-Base Indicator Resins ………………………………...12 1.1.3 Polymeric Supports …………………………………………….12 1.1.3.1 Silicones …………………………………………………….13 1.1.3.2 Other Hydrophobic Materials ………………………………13
41
CONTENTS
PAGE
1.1.3.3 Silica Materials ……………………………………………..13 1.1.3.4 Mixed Hydrophilic/Hydrophobic Materials ………………..14 1.1.3.5 Hydrophilic Supports ……………………………………….14 1.1.4 Immobilization Techniques …………………………………….15 1.1.4.1 Mechanical Immobilization …………………………….…..15 1.1.4.2 Electrostatic Immobilization ………………………………..15 1.1.4.3 Chemical Immobilization …………………………………..16 1.1.4.4 Preactivated Materials for Immobilization …………………16 1.2 Charge-Transfer Devices (CTDs) in Analytical Instrumentation .…17 1.3 Data Calibration and Quantitation Using Modeling ………………..17 1.3.1 Solver Tool of Microsoft Excel Software ………………………17 1.3.2 Artificial Neural Networks (ANNs) ..…………………………..18
CHAPTER TWO: LITERATURE SURVEY 2.1 pH Optodes …………………………………………………………19 2.2 Using CCD Cameras for Recording Signals Involving pH Data …..23 2.3 Calibration and Quantitation of Spectroscopic and Imaging Data Using Different Modeling Systems ..……………………….…25 2.3.1 Reported Modeling Systems for Evaluation of pH ……………25 2.3.2 Modeling Using Solver Tool of Microsoft Excel Software
..…..26
2.3.3 Modeling Using Artificial Neural Networks (ANNs) ……….....26 2.3.3.1 Use of ANNs in Optode Field ………...……………………...27
42
CONTENTS
PAGE
2.3.3.2 Use of ANNs for Evaluation of pH Data in Other Fields .…....28 2.4 Purpose of This Research .………………….………………………28
CHAPTER THREE: EXPERIMENTAL 3.1 Fabrication of Optodes …………………………………………..…30 3.1.1 Reagents …………………………………………...……………30 3.1.2 Apparatus ……………………………………………………….30 3.1.3 Procedure ……………………………………………………….30 3.1.3.1 Membranes with Chemical Immobilization ………………..30 3.1.3.1.1 Membranes …………………………………………..…30 3.1.3.1.2 Activation of Membranes ……………………………….31 3.1.3.2 Porous Membranes for Mechanical Immobilization .……....31 3.1.3.2.1 Membranes ……………………………………………...31 3.1.3.2.2 Activation of Membranes ……………………………….31 3.1.3.3 Membranes with Dissolved Indicators ……………………..32 3.1.3.3.1 Membranes ……………………………………………...32 3.1.3.3.2 Buffer Solutions ………………………………………...32 3.1.3.3.3 Measurements …………………………………………..33 3.2 A Diffusion-Based Method in pH Optosensing …………..…...…...33 3.1.1 Apparatus ……………………………………………………….33 3.1.2 Procedure ……………………………………………………….33 3.1.2.1 Membrane …………………………………………………..33 3.2.2.2 Measurements and Modeling ……………………………….34
43
CONTENTS
PAGE
3.3 A Time-Based Flow Analysis in pH Optosensing …...…….….…...34 3.3.1 Apparatus ……………………………………………………….34 3.3.2 Procedure ……………………………………………………….34 3.4 Full Range Optical pH Measurement Using an Optode Array and a Video Camera ………….……………………………..……..35 3.4.1 Apparatus ……………………………………………………….35 3.4.2 Procedure ……………………………………………………….36 3.4.2.1 Buffer Solutions …………………………………………….36 3.4.2.2 Sensor Array ….…………………………………………….36 3.4.2.3 Modeling
…….…………………………………………….36
CHAPTER FOUR: RESULTS AND DISCUSSION 4.1 Fabrication of Optodes …………………………………………..…38 4.1.1 Membranes with Chemical Immobilization ……...…………..38 4.1.2 Porous Membranes for Mechanical Immobilization .…...…....39 4.1.3 Membranes with Dissolved Indicators ………...……………..41 4.1.3.1 Conventional studies on Nile Blue pH optode ……………41 4.1.3.1.1 Absorption spectra …………………………………….42 4.1.3.1.2 Measuring Range .……………………………………..44 4.1.3.1.3 Apparent Dissociation Constant (K´) ……………...….46 4.1.3.1.4 Response Reproducibility and Membrane’s Stability ...46 4.1.3.1.5 Response Times ……………………………………….48 4.1.3.1.6 Conclusion .……………………………………………49
44
CONTENTS
PAGE
4.1.3.2 Other pH optodes ….……………………………………...49 4.2 A Time-Based Flow Method in pH Optosensing for Wide Range Measurements Using a Single H+-Selective Chromoionophore …………………………………………………..55 4.2.1 Dynamic Studies ………...………………………..…………….55 4.2.2 Simulation of Flow Responses …….………………………..….55 4.2.3. Flow Responses ……………………………….…….…………56 4.2.3.1 Flow Responses Reproducibility …………………………...61 4.2.4 Conclusion ……………………………………………………..65 4.3 A Diffusion-Based Method in pH Optosensing for Wide Range Measurements Using a Single H+-Selective Chromoionophore with Approaches to Mathematical and Artificial Neural Networks Modeling .……………………………..66 4.3.1 Modeling of Diffusion-Controlled Results ………………….....66 4.3.1.1 Mathematical Modeling …………………………………....66 4.3.1.1.1 Modified Mathematical Model of Kostov and Coworkers ……………………………………….……...66 4.3.1.1.2 The Analyte Concentration-Dependent Diffusion Coefficient D(pH) ………………………………………67 4.3.1.1.3 A Computer Program for Evaluation of The Mathematical Model ……………………………………69 4.3.1.2 Other Mathematical Models ………………………………..69 4.3.1.2.1 Mathematical Modeling Using a Multi-Linear Model ....70
45
CONTENTS
PAGE
4.3.1.2.1.1 Data Description …………………………….………70 4.3.1.2.1.2 Calibration Procedures ………..…………….………70 4.3.1.2.1.3 Conclusion ………………………………………….72 4.3.1.3 ANNs Modeling …………………………………………....73 4.3.1.3.1 Data Description …………………………….……….…73 4.3.1.3.2 Calibration Procedures ………………………….………73 4.3.1.3.3 Conclusion ……….……………………………….…….74 4.4 Full Range Optical pH Measurement Using an Optode Array and a Video Camera with Approach to a Novel Method in Image Processing ……….………………………………75 4.4.1 Data Description …………………………………………….….75 4.4.1.1 RGB Values ………………………………………………...75 4.4.1.2 Analytical Signals …………………………………………..75 4.4.2 Calibration Procedures …………………………………………78 4.4.3 Interferences ……………………………………………………83 4.4.4 Measuring Ranges ……………………………………………..84 4.4.4.1 Imaging Results in comparison with Spectroscopic Results ………………………………………………………84 4.4.5 Conclusion ……………………………………………………...85 Appendix A …………………………………………………………….87 REFRENCES …………………………………………………………..89 ABSTRACT AND TITLE PAGE IN PERSIAN ………………………96
46
LIST OF TABLES TABLE
PAGE
4.1 List of indicators studied in our preliminary experiments using Kostov et al. procedure ……...…………….………………...40 4.2 List of indicators studied with mechanical procedure in preliminary experiments ………………………………………..40 4.3 Some pH optodes developed using dissolution of dyes on the unmodified triacetylcellulose membranes ……………………...42 4.4 Data of optode absorbance at 645 nm at different pH values ………45 4.5 Data of response-reproducibility of the Nile Blue membrane ……..47 4.6 The response times of NB optode for acidic to basic forms transitions …………………………………………………..48 4.7 The response times of NB optode for basic to acidic forms transitions …………………………………………………..48 4.8 Absorbances of Congo Red optode at 600 nm and at different pH values; and measuring range computation …………………....50 4.9 Absorbances of Solo Chrome Dark Blue optode at 646 nm and at different pH values; and measuring range computation …..……51 4.10 Absorbances of Titan Yellow optode at 510 nm and at different pH values; and measuring range computation …………..52 4.11 Absorbances of Hexanitro-Diphenylamine optode at 428 nm and at different pH values; and measuring range computation …..53 4.12 Absorbances of Victoria Blue optode at 618 nm and at
47
TABLE
PAGE
different pH values; and measuring range computation ……..…...54 4.13 The absorbance data for the time response profiles at different pH values ………………………………………………..57 4.14 Absorbances of the proposed optode at 30 and 45 s in the flow studies ……………………………………………………………..62 4.15 Data for monitoring flow response reproducibility ……………….64 4.16 Diffusion coefficient values calculated from required time for observation of 100% response in different pH values ..……….68 4.17 The setting for Solver tool of Microsoft Excel 2000 ……………...71 4.18 Calculated coefficients of the multi-linear model with Solver. The C0 and C35 correspond to the response profile of 10 and 180 s, respectively ……………………………………………71 4.19 The True and Predicted pH values with the proposed model ……..72 4.20 True and Predicted pH data ……………………………………….73 4.21 Red Analytical Signals for pH optodes in different buffer solutions …………………………………………………………..77 4.22 Green analytical signals for pH optodes in different buffer solutions …………………………………………………………..79 4.23 Blue analytical signals for pH optodes in different buffer solutions ………………………………………………………….81 4.24 The setting for Solver tool of Microsoft Excel …………………..82 4.25 Calculated coefficients of the multi-linear model with Solver …...82 4.26 True and Predicted pH values with the proposed model …………82
48
TABLE
PAGE
4.27 List of reported interferences for the free indicators ……………...83 4.28 The pH measuring ranges obtained from Red, Green, and Blue analytical signals of the proposed optodes …………..……..84
49
LIST OF FIGURES FIGURE
PAGE
3.1 Set-up of flow measuring system …………………………………..35 3.2 Schematic diagram of the video imaging …………………………..37 4.1 Absorption Spectra of Nile Blue optode in aqueous buffer solutions with pH values of: (1) 1.0, (2) 1.8, (3) 2.2, (4) 2.6, (5) 3.3, (6) 4.4, (7) 5.0, (8) 5.7, (9) 6.4, (10) 7.5, (11) 8.4, (12) 9.4, (13) 10.4, (14) 11.2, (15) 12.0, and (16) 12.7 ……………………………43
4.2 Absorption Spectra of Nile Blue indicator in alcoholic buffer solutions (6.510-5 g/ml) with nominal pH values of: (1) 3.0, (2) 5.3, (3) 6.7, (4) 9, (5) 11.3 and (6) 12.8 ……………44 4.3 The absorbance of Nile Blue optode at 645 nm as a function of pH is illustrated .…………………………………..…………….44 4.4 The response-reproducibility of the membrane at 645 nm, for alternative changes of pH between 7.5 and 11.2 …………………..46 4.5 The changes in the absorbance of the membrane at 645 nm for alternative changes of pH from 12.3 to three different pH values of 10.1, 9.2, and 8.0 ………………………………………...47 4.6 Spectra of Congo Red pH optode at different pH values: (1) 0, (2) 1, (3) 1.81, (4) 2.87, (5) 3.78, (6) 4.78, (7) 5.72, (8) 6.80, (9) 7.96, (10) 8.95, (11) 9.91, (12) 10.88, (13) 11.98 ……..….50 4.7 Spectra of Solo Chrome Dark Blue pH optode at different pH values. (1) 3.29, (2) 3.78, (3) 4.56, (4) 5.02, (5) 5.72, (6) 6.09,
50
FIGURE
PAGE
(7) 6.37, (8) 7.00, (9) 7.54, (10) 7.96, (11) 8.95, (12) 9.37, (13) 9.91, (14) 10.38, (15) 11.20 ………………………………………..51 4.8 Spectra of Titan Yellow pH optode at different pH values: (1) 11, (2) 11.4, (3) 11.98, (4) 12.48, (5) 12.7, (6) 13.18, (7) 13.7, (8) 14 …………………………………………………………52 4.9 Spectra of Hexanitro-Diphenylamine pH optode at different pH values: (1) 5.02, (2) 4.56, (3) 4.10, (4) 3.78, (5) 3.29, (6) 2.87, (7) 2.58, (8) 2.09, (9) 1.81, (10) 1, (11) 0.6, (12) 0.3, (13) 0, (14) –0.4, (15) –0.44, (16) –0.48, (17) –0.51, (18) –0.54, (19) –0.57 ……………………………………………………53 4.10 Spectra of Victoria Blue pH optode at different pH values: (1) 5.72,(2) 6.09, (3) 6.37, (4) 7.00, (5) 7.54, (6) 7.96, (7) 8.95, (8) 9.37, (9) 9.91, (10) 10.38, (11) 11.20, (12) 11.98, (13) 12.5, (14) 13, (15) 13.5, (16) 14 ……………………………..54 4.11 The time response profiles of the NB pH optode when the proposed optode with initial pH value of 13.3 is treated with 22 different universal pH buffer solutions with pH values between 0 and 12.07 ………………………………………56 4.12 Simulated flow analyses results at 5 to 180 s from the time response profile …………………………………………………..61 4.13 Typical flow results in preliminary studies at 30 ( ▲) and 45 s () for different universal pH buffer solutions …………..…62 4.14 Typical flow diagram at 30 and 45 s for different universal
51
FIGURE
PAGE
pH buffer solutions in preliminary studies. From three peaks for each pH, the average of the second and the third peak was used in calculations …………………………………….…….63 4.15 The response-reproducibility of the membrane at 645nm, for alternative change of pH between 7.5 and 11.98 in flow system …………………………………………………………….64 4.16 Diffusion coefficient as a function of pH for the purposed NB pH optode …………………………………………………………68 4.17 Correlation plot for the True and Predicted pH data using the purposed multi-linear model with Solver tool ………………..72 4.18 The correlation between True and Predicted pH data with Artificial Neural Networks model …………………………..74 4.19 Red Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes ………………………………………76 4.20 Green Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes ………………………………………78 4.21 Blue Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes ……………………………………………….80 4.22 Correlation plot for True and Predicted pH data using the proposed multi-linear model with Solver tool ……………………83 4.23 The pH measuring range obtained for Imaging (red, green, and blue light intensities) and Spectroscopic (absorbance at maxima) results. The black lines represent spectroscopic
52
FIGURE
PAGE
measuring range. The red, green, and blue components have been shown in their respective colors ………………..……...85
53
CHAPTER ONE INTRODUCTION 1.1 pH Optical Sensors Optical methods have always played the dominant role in various fields of analytical sciences. Colorimetry, photometry, and spot tests have been used to qualitatively determine chemical and biochemical species. Introduction of strip tests based on dry reagent chemistries, which allowed visual or instrumental evaluation of analytical results, mainly in the clinical chemistry field is from latest breakthrough in Optical Sensing.1 Only a limited number of analytes has an intrinsic absorption or a related spectroscopic property that can be utilized for direct sensing without compromising selectivity. For several important species including pH, metal ions, and oxygen in water, no direct and sensitive methods are known. The remedy for this situation is the well-established indicator chemistry. By immobilizing a proper indicator on a waveguide, a device is obtained whose spectral properties reflect the analyte concentration. Two words have been created for this device: optode (from the Greek oios oos, the “optical way” or “optical path”) or optrode (from optical electrode).1 Both expressions stress the fact that the primary information is optical rather than electrical.
54
1.1.1 Development of pH Optodes In recent two decades there has been increasing interest in the development of optically based chemical sensors. Some factors stimulating this interest include the following. 1.1.1.1 Problems Inherent to the Use of pH Electrodes Glass electrodes for pH widely used for more than 60 years, with a high selectivity have become the standard pH sensor. Unfortunately, the nature of glass presents inherent disadvantages for certain applications. For instance, use of glass electrodes for in vivo measurements or in food processing is hazardous because glass easily breaks. The high resistivity of glass also makes miniaturization difficult and leads to relatively long response times for glass microelectrodes.2 Protein adsorption is a problem for miniaturized glass electrodes with high impedance. It is also expensive, require more care in applications and have to be carefully sterilized for use in tissue of critically ill patients.2 The electrical interference problem of electrodes is not limited to their use in medicine. In any environment where high sensitivity is critical, electrical noise generated by extraneous fields will cause trouble. In addition, pH electrodes do not work in solutions through which an electrical current is flowing, for instance in electrolysis cells and batteries. Other problems inherent to the use of electrodes include the susceptibility of wire leads and couplings to deterioration under corrosive conditions, or conditions of altering temperatures. Alternatives to pH glass electrodes, such as antimony electrodes
2
or
Al2O3- or Si3N4-based ion-sensitive field-effect transistors (ISFETs) 2 , exist but have their own disadvantages, such as interference from protein 55
adsorption or phosphate interference in the case of the antimony electrodes. Many of these problems can be overcome by optically measuring pH. The technique dates back to the days when litmus was used to indicate the acidity of a solution. Eventually, pH indicator strip tests became popular, the first of which suffered from dye bleeding. This was overcome by covalently immobilizing the dye on a rigid support. However, only during the past two decades, optical sensing of pH has made some important advances and became precise enough to be of practical utility. Optical sensing techniques are capable of measuring extreme values of pH with great precision. Optodes can be produced easily at very low cost without sacrificing accuracy and are sufficiently simple in design to be considered as disposable. 1.1.1.2 pH Optosensing versus pH Electrosensing Because of the quite different operating principle, pH optrodes (as compared to pH electrodes) offer quite new possibilities and at the same time are subject to some limitations that are not encountered with electrodes. The
fundamental
difference
between
optical
techniques
and
potentiometric measurements of pH is that the optical techniques measure the concentration of a dye species (that is related to pH) while pH is defined in the term of activity, which is what potentiometric measurements are based on. The interaction between the electromagnetic radiation with atoms, molecules, and ions results in counting their numbers, i.e. their concentration. The solute-solvent and solute-solute interactions, which determine the value of the activity, show up only as a second-order effect, such as shifts of the absorption maxima, etc. Janata 56
3
has clearly shown the compromises that have to be made in case of optical pH measurements. Difficulties limiting practical applications of optical pH measurements have also been discussed by Seitz4, Ratzlaff et al.5 and Edmonds et al.6 Electrochemical measurements face similar problems. They are done in cells with or without liquid junction, which separates the inner reference electrode compartment from the measured solution and contributes liquid junction potential to the cell voltage. A liquid junction separating two dissimilar solutions is a nonequilibrium element in this measuring circuit. Therefore, any measurements done in the cells with liquid junctions are not equilibrium measurements, by definition. The main reasons cited for development of optical sensors are the lack of necessity of a reference electrode and electrical safety. pH optodes are based upon pH-dependent changes of the optical properties (absorbance, reflectance, fluorescence, refractive index, and the like) of a thin reagent layer, attached to the tip or surface of an optical light guide through which these changes are detected. The reagent layer usually contains a dye, which reversibly react with the protons of the sample. In the most popular version, the pH-dependent absorption7,
8
or fluorescence 9,
10
is
monitored and related to pH. Since dissociation equilibria of indicators utilized in optodes fabrication are strongly affected by the composition of the surrounding medium, the concentration of diluted substances, and temperature, optical pH sensors perform best under well-defined sample conditions. Typical examples are seawater pH, biofermenters, agrifood industries, and other situations where the sample composition is almost invariable. Many optical sensors are simple in design and can easily be replaced by substitute parts, even if manufacturing the sensor head requires relatively 57
complex chemistry. They therefore can offer cost advantages over other sensor types. Consequently, it is likely that single use and disposable optical sensors will experience a bright future. While most pH optodes have a much smaller dynamic range as compared to electrodes, optical sensors based on dynamic fluorescence quenching have a useful dynamic range often larger than electrochemical ones. However, pH optodes can offer considerable economic and sampling advantages over pH electrodes. The measurement of pH using optodes is most advanced in the area of biochemical and physiological analysis because of the narrow pH range covered and the availability of many steam-sterilizable pH optical sensors. Agrifood (milk), environmental (seawater, geology) and industrial (process control) analyses are from new applications of optodes. In addition, pH optrodes act as transducers for CO2 and NH3 optrodes, and in certain biosensors in which an enzyme produces protons.1 1.1.2 pH Indicators An indicator acts as a transducer for the chemical species that cannot be determined directly by optical
means. Consequently, it is the
concentration of the indicator species that is determined rather than the analyte itself. Many indicators cannot be used in optodes because of unfavorable analytical wavelengths, poor photostability, low molar absorptivity, additional reagents (such as strong acid or alkali) that are needed in conventional spectrometry to adjust optimal conditions, or simply because they are not available in a purity required for optode application (e.g., xylenol orange). Indicators can be categorized as either absorbance- or fluorescence-based. Most absorbance-based indicators undergo a color change (with one band 58
appearing as the other disappears) rather than an intensity change of one band only. These are referred to as two-color indicators and are mostly compatible with light emitting diode (LED) or filament lamp light sources. They can easily be used in two-wavelength internal referencing methods.11 Indicators for use in optodes are known to undergo either ion combination reactions (such as with protons, cations, or anions) or oxidation-reduction reactions. Virtually, all indicators are weak acids or, less commonly, weak bases, or their salts, and in case of acid-base indicators even when they are used for purposes other than hydrogen ion indication, their reactions involve hydrogen ion. 1.1.2.1 Indicator Theory According to Ostwald, indicators are weak acids (HI) or bases (IOH) whose color is different from color of their ions (I-/+). The relation between pH, pKa, and absorbance of the two species is given by:1
pH pKa log
Ι-
HI
(1.1)
In this equation, [HI] represents the concentration of the undissociated indicator molecule whose color is called acid color, while [I-] denotes the concentration of the indicator anion. pKa represents the negative logarithm of the dissociation constant Ka. Indicator bases, which contain amino groups, are able to bind protons, due to the unshared electron pair of nitrogen atoms, and so dye cations with different charges can be produced depending upon the actual pH value. On the other hand, indicator acids possessing hydroxy groups, release the hydrogens of the hydroxy groups in alkaline medium and dye anions are produced. 59
The pKa value can be determined with the help of Equation 1.2:
pK a pH - log
(E X - E A ) (E B - E X )
(1.2)
where EX is the absorbance at the analytical wavelength at a certain pH. EA and EB are the absorbances at this wavelength for the pure acid and base forms, respectively. This equation assumes the Beer-Lambert law to be valid. In a more general sense than treated so far, indicator acids are not only phenols and carboxylic acids, but also protonated azo compounds and amines. Some indicators such as phenolphthalein, however, change color as a result of a complete rearrangement of the molecule and cannot be treated this way. Aside from this, this rearrangement requires some time and involves various intermediates. It should be noted, too, that even simple acid-base equilibria such as the nitrophenol-nitrophenolate equilibrium in fact can involve more than two species (in the given example the quinoid acid form of nitrophenol).
1.1.2.2 Absorbance-Based Indicators Among the great variety of organic chromophores such as azo dyes, nitrophenols,
phthaleins,
sulfophthaleins,
anilinesulfophthaleins,
triphenylmethane dyes, polymethines, and others, only a few have so far been considered to be useful and applied to sensor technology. 7,
12, 13
A
variety of absorbance-based pH probes with potential of utility in optical sensors, along with their pKa values and spectral data has listed in reference 1. It is obvious that many of them can be excited with a blue, green, or yellow LED, or with a frequency-doubled diode laser. It is
60
important to note that a number of nonfluorescent indicators display fluorescence when they are bounded to a rigid solid support.1 1.1.2.3 Influence of Important Factors on Indicator Performance Janata3 gave an excellent discussion of the source of errors in optical pH determination, some of which has been thoroughly discussed decades ago for dissolved indicators but also hold for immobilized reagents. The main sources are the effects of ionic strength and dissolved polyelectrolytes (i.e. proteins) of added solvent, and of surface structural effects of optodes. It has been followed that for thermodynamic reasons neither optical nor electrochemical sensors can measure pH precisely, but that on grounds of error minimization in electrodes, the electrochemical measurements of ion activities (in samples with unknown composition) are superior to the optical sensors. 1.1.2.3.1 Ionic Strength Effects
In
practice, the working concentrations (C) are such that it is no longer identical with the actual activity ( a) of the ion. The relation between a and C is given by:
a C.f
(1.3)
where f is the activity coefficient which has been shown by Debye and Hückel
14
to be related to ionic strength (I), effective diameter of the
hydrated ion ( ) and ion charge ( z) by:
- log f
0.51z 2 I (1.4)
1 3.3 I
where the constants 0.51 and 3.3 are applicable to aqueous solutions at 25 C. First of all, the effect of foreign neutral electrolytes, that is, the 61
salt effect, manifests itself by altering the indicator equilibrium. The phenomenon may be easily explained especially for media of small ionic strength by the theory of Debye and Hückel. Secondly, foreign electrolytes change the activity of the analyte. For the three most common types of charged indicators (that is, cationic, neutral, and monoanionic), the pKi alter with ionic strength (I) in the following ways: HInd+ H+ + Ind
pKa = pKa + 0.5 I½
(1.5)
HInd H+ + Ind¯
pKa = pKa 0.5 I½
(1.6)
HInd¯ H+ + Ind=
pKa = pKa 1.5 I½
(1.7)
Consequently, the salt error of di- and tri-sulfonate indicators is relatively great, since the alkaline forms of these indicators are ions of several negative charges. In solutions of small or medium ionic strength, those indicators, which exhibit a dipolar ion structure (methyl orange, methyl red, etc.), have a small salt error, because the dipolar ion behaves like a neutral molecule. In solutions of great ionic strength, dipolar ions possess two separate charges; hence, the salt error increases. The difference in the ionic strength dependence of indicators has been exploited to sense the ionic strength of an unknown solution by making use of two pH sensors with different ionic strength sensitivity 1. The apparent dissociation exponent (pKa) of the indicator depending upon its ionic strength is defined by the expression:
f pK a pKa log b fa
(1.8)
According to the above definition, the salt error is already reflected in the variation of pKa, due to the variation of the ratio f b/fa. 62
Summarized, it may be said that in the presence of foreign neutral salts the transition interval of the indicator acids will be shifted towards lower pH values, whereas that of the indicator bases will be shifted in the direction of higher pH values. Beside the alteration of indicator equilibria, the presence of foreign salts also changes the optical absorption intensity of the indicator colors. The color of solutions containing neutral salts is in general less intensive than that of diluted acids or alkaline solutions. The ionic strength dependence present a major obstacle in precise determination of pH using optodes.3,
6
It has been concluded6 that direct
use of a fiber optic sensor for the determination of pH in an unknown solution with an accuracy of ±0.1 pH units is unrealistic, and this may well be true. 1.1.2.3.2 Effect of Solvents Different solvents exercise different effects upon indicator dyes, so color changes as well as dissociation exponents of the indicators vary with the solvent. In aqueous methanolic or ethanolic solutions the alteration is relatively not significant; in anhydrous alcohol, however, it becomes greater, while in other solvents one can meet quite new phenomena. 15 1.1.2.3.3 Influence of Proteins and Colloids Proteins and substances consisting of macromolecules may adsorb the indicators, through which the color change will become completely different. Proteins bind the indicator acids through their basic group and indicator bases through their acid group. The charge of particles plays an important role in the phenomena of placing particles on macromolecular surfaces. The result of such binding processes is a shift in the apparent
63
dissociation or binding constant. Among these effects, the apparent shift in the pKa of pH indicators upon addition of protein is most important. This so-called protein error is defined as the difference between the colorimetrically determined pH value in the presence and absence of the protein. The error can be important.3 1.1.2.3.4 Influence of Temperature The color of many indicators depends on the temperature. When heated up in solution to the boiling point, the color of alkali-sensitive indicators is shifted in the direction of the alkaline, whereas color of acid-sensitive indicators is shifted in the direction of the acid side. The alkali sensitive methyl orange changes, for instance, at room temperature between pH 3.1 and 4.4, whereas at 100ºC the pH interval is between 2.5 and 3.7. This is due first of all to the fact that the ion product of water changes significantly with the temperature. It is 14.2 at 18ºC, but 12.2 at 100ºC. Data on the effect of temperature on some acid-base indicators are given in reference 1. The second source for the observed temperature coefficient (tempco) is the shift in the pKa of indicator itself. It is difficult to separate the tempco of the ion product of water and that of the dissociation constant. 1.1.2.4 Acid-Base Indicator Resins The indicator resins provide an interesting form of acid-base indicators. The preparation of the material is very simple. The resin beads are shaken for a fixed period with an alcoholic solution of the indicator. Then the material is washed until no more leaching is observed. 1 1.1.3 Polymeric Supports
64
The polymers used in optodes can have one or more of the following functions: (1) They may act as rigid supports onto which the dyes are immobilized. (2) They may act as solvents or cages for the materials to be immobilized. (3) They can provide selectivity for certain species by virtue of the permselectivity of most polymers. (4) Polymeric covers are frequently used as protective covers for sensitive working chemistries. (5) They can serve as optical isolation so to avoid ambient light to enter the optical systems of the optodes. The choice of polymer is governed by the permeability of the polymer for the analyte, its stability and availability, its suitability for dye immobilization, its compatibility with other materials used in the fabrication of optodes, and its compatibility with the sample to be investigated.1 1.1.3.1 Silicones Silicones are polymers with excellent optical and mechanical properties and excellent gas permeability. In the case of oxygen it exceeds all other polymers. Numerous silicon prepolymers are available commercially and allow easy manufacturing of membranes, emulsions, suspensions, or other kinds of sensing chemistries.1 1.1.3.2 Other Hydrophobic Materials Poly (vinyl chloride) (PVC), polyethylene, poly (tetrafluoro-ethylene) (PTFE), and polystyrene are other hydrophobic materials. Except for polystyrene, they are difficult to chemically modify so that their function is confined to a solvent for indicators that penetrate the polymer, or as a gas-permeable cover.1 1.1.3.3 Silica Materials 65
Given the mechanical stability and favorable optical properties of glass, it has been the material of choice for many sensing purposes. Because glass is impermeable to practically every analyte, its function has always been that of a rigid support whose surface was modified by chemical means. The popularity of glass is also due to the fact that its surface can be made both hydrophilic and hydrophobic, simply by treating it with the proper reagent.1 An interesting sol-gel glass was obtained by controlled hydrolytic polycondensation of Si(OEt) 4 to give a fairly inert inorganic glassy matrix whose porosity and size of pore network can be varied, to a certain degree, by polymerization conditions at room temperature. 1 1.1.3.4 Mixed Hydrophilic/Hydrophobic Materials When the surface of a hydrophobic polymer is modified with charged functions, or when a hydrophilic polymer is surface-modified with polar groups, new materials with promising properties are obtained. They can serve as double purpose solid supports for the indicator to be immobilized, and sometimes provide some selectivity by retaining undesired components of the analyte to reach the indicator. On the other hand, these
“mixed polarity” materials sometimes have limited
compatibility with other polymers. Typical examples are ion-exchange materials.16, 17 1.1.3.5 Hydrophilic Supports Hydrophilic supports are characterized by a large number of hydrogenbridging functions such as OH or NH2, or by large numbers of charged groups such as COO or SO3 on the polymer chain. Typical examples are the polysaccharides (cellulose), polyacrylates, polyacrilamides,
66
polyimines, polyglycols, and the variety of so-called hydrogels. Depending on the degree of polymerization and cross-linking, with water swelling characteristic, they are water-soluble or water-insoluble. Generally they are easily penetrated by aqueous solutions and have limited compatibility with hydrophobic polymers. Cellulose in either bead or membrane form has found widespread application as a support for indicators, 18 chelating agents,19 and proteins.20
1.1.4 Immobilization Techniques Following the choice of indicator and polymeric support, the next step in sensor design frequently will involve immobilization of the dye on the support to give the so-called sensing chemistry or working chemistry. 1.1.4.1 Mechanical Immobilization Mechanical (physical) immobilization involves: (1) adsorption (which plays a minor role in sensor chemistry) and (2) inclusion of molecules in a sphere, which they cannot leave. Thus, indicators may be entrapped in capsules of only a few nanometers in diameter. The method usually confined to high molecular-weight indicators, which react with low molecular-weight analytes (that can permeate the membrane). Through not a physical method in its original sense, lipophilic indicators dissolved in a lipophilic polymer practically can be considered to be immobilized and are not washed out by aqueous samples. Typical examples include oxygen-sensitive polymers with dissolved lipophilic dyes,21 and lipophilic indicators dissolved in lipid bilayers. 22, 23 1.1.4.2 Electrostatic Immobilization
67
When the surface of a rigid support is fitted with charged groups such as sulfo groups or quaternary ammonium groups, the material is capable of binding ions having opposite charge. Sulfonated polystyrene, for instance, binds cations with varying binding strength. The major advantages of electrostatic immobilization are the ease of the procedure and its reproducibility. Loading can be easily governed by the time of immobilization. The fabrication is very simple. In that the charged polymer is immersed, for a defined period of time, into a methanolic solution of the dye.10 1.1.4.3 Chemical Immobilization Chemical (covalent) immobilization is accomplished by creating a covalent bond between indicator and a polymer surface. Numerous methods of surface modification for polymers exist and can yield materials capable of covalently binding indicators via their reactive groups. Seitz11 has presented a review on the immobilization of indicators on various materials including cellulose. Some references for covalent immobilization on different supports such as quartz, silica gel, conventional glasses, metals such as iron and platinum, elemental carbon, cellulose, carboxymethylcellulose (CMC), polyacrylamide (PAA), poly (vinyl alcohols), polyglycols, poly (ethylene imines) and polystyrene has been given in reference 1. 1.1.4.4 Preactivated Materials for Immobilization Given the widespread use of immobilized compounds in chemical sciences, various preactivated polymers have become commercially available in the past years. The surfaces of these materials are equipped with a reactive group, which usually, under mild conditions, react with
68
amines and related nucleophiles. Thus, different companies 1 offer various types of materials, which require a very simple immobilization protocols.
69
1.2 Charge-Transfer Devices in Analytical Instrumentation Charge-transfer device (CTD) array detectors have become an invaluable tool for analytical chemists. The CTDs are an attractive alternative to film, vidicons, photodiode arrays, and photomultiplier tubes. The broad class of CTDs includes two subclasses: charge-coupled devices (CCDs) and charge-injection devices (CIDs). CTDs absorb photons, convert them to charge carriers, and measure the carriers to create an analytical signal. From their inception as detectors for molecular absorbance and atomic emission spectroscopy, CTDs are now commercially available for use as detectors in nearly all areas of chemical analysis, including molecular luminescence, atomic emission spectroscopy, Raman spectroscopy, X-ray diffraction, microscopy, separations, and mass spectrometry. 24-27
1.3 Data Calibration and Quantitation Using Modeling Simulation, modeling, curve fitting, statistical, and numerical analysis are often a major and inevitable part of chemical experimentations. 1.3.1 Solver Tool of Microsoft Excel Software The relatively simple user-friendly interface, and the fact that many analysts are familiar with Microsoft Excel spreadsheet, has made it a common software.28 Solver, an analysis tool incorporated into Excel can be successfully used for mathematical modeling data obtained in many analytical situations.29,
30
The use of Solver for these purposes is more
straightforward and more readily available than the use of computer programs that have been written, previously.30 1.3.2 Artificial Neural Networks (ANNs)
70
In the past years, the topic of neural computing has generated widespread interest and popularity. The popularity of this technique is due in part to the analogy between artificial neural networks and biological neural networks. Artificial neural networks are thought to have the ability to learn during a training process where they are presented with a sequence of stimuli (inputs) and a set of expected responses (outputs). Numerous applications have been investigated by using artificial neural networks including pattern recognition, signal processing, process control, and modeling.31, 32
71
CHAPTER TWO LITERATURE SURVEY 2.1 pH Optodes The use of indicator dyes for the determination of pH is a very old concept (for example in the litmus paper). Since the first detailed report of a fiber-optic pH sensor
7
in 1980, miscellaneous pH optodes based on
various transduction principles, such as changes in absorbance, 33-38 fluorescence (direct or with energy transfer from a donating to an accepting fluorophore),10,
39-41
light intensity
7, 42
or refractance,43 have
been reported. These sensors can be classified as to whether the indicator dye is trapped or immobilized, (i) on the surface of an inert (sometimes porous) support such as glass or an ion-exchanger,33,
43, 44
(ii) in a
hydrophilic polymer,33-37 or (iii) in a hydrophobic film that forms a phase clearly distinct from the sample and can be regarded as organic gels. 38, 45 Even though many optodes could be easily used to determine pH, but only few have so far served for this purpose. The first efforts in this field were those of Peterson, Lubbers, and Opits who developed pH, carbon dioxide, and oxygen sensors.46 The largest part of studies has done on finding the most appropriate indicators for physiological pH measurements. Phenol red
6, 7, 11
and the
trisodium salt of 8-hydroxy-1, 3, 6-pyrenetrisulfonic acid (HPTS) 4,
10, 11
are some of indicators that were applied for this purpose. Covalently immobilized phenol red has been used in a sensor based on the ratio of reflected intensity at 558 nm (where the base form of the indicator absorbs) to reflected intensity at 600 nm (where neither forms of the indicator absorbs).7,
10
The suitability of this device for in vivo 72
measurements has been demonstrated. Because the optical measurements are made at long wavelengths, plastic fiber could be used in the sensor. A single-fiber pH sensor has been developed based on the effect of phenol red on the fluorescence of eosin.39 The ground-state pKa of HPTS in solution is 7.3, ideal for physiological measurements. 11 In the exited state, HPTS is a stronger acid than in the ground state. Because in buffered media, excited state deprotonation of HPTS is faster than fluorescence, fluorescence from the base form of the indicator is observed even when the indicator is initially in the ground state. For sensing applications, the measured parameter is the ratio of fluorescence intensity excited at 405 nm (where the acid form absorbs selectively), to the fluorescence intensity excited at 470 nm (where only the base form absorbs). In addition to being amenable to two-wavelength intensity measurements in the visible region of the spectrum, HPTS has the advantage of being highly stable with respect to photo-degradation. Sensors have been described using both ionically and covalently immobilized HPTS. 4,
10, 11
On the one hand, several neutral H+-selective chromo-ionophores, most of them Nile Blue derivatives,38 were well-characterized by their pKa in PVC optode membranes, lipophilicity, chemical stability, and absorption properties. Interestingly, they are slightly more basic in the membrane phase than in methanol or water, which may be due to stronger salvation of H+ in the latter. The wide range of their pKa values (5.2-14.0) makes it possible to design bulk optodes for specific measuring ranges. Chromoionophore decomposition was found to be only a problem under direct sunlight irradiation. On the other hand, nine electrically charged H + chromoionophores
2
were shown to cover a range of more than 10 pK a
units. Other charged H+ chromoionophores that were used in carrier
73
based optodes are for example o-cresol phthalein octadecyl ester or substituted diphenylamines resembling the Takagi reagent.47, 48 Some free-of-dye pH optodes have been developed based on polypyrrole 49
and substituted polyanilines .50 This type of optodes does not require a
dye to be immobilized. A pH optode was presented which is based on the use of a thin polypyrrole (PPy) film obtained by chemical oxidation of pyrrole and deposited on the walls of a polystyrene cuvette. The shape and intensity of its spectra are pH dependent between pH 6 and 12. The apparent pKa is around 8.6, but once exposed to pH values higher than 6, the films need to be reconditioned with 0.1 N HCl in order to give the same response to pH. The films are an interesting alternative to indicator based pH sensor films in that they are fully compatible with LED and diode laser light sources, and can be easily prepared. 49 Clear blue membranes of polypyrrole doped with Prussian blue also display pH dependent spectra in the red and near-infrared.51 Polyanilines (PANI) are viable materials for use in optical sensing of pH. Thin films of substituted PANI readily deposite on the inner walls of polystyrene cuvette when substituted anilines are chemically polymerized in hydrochloric acid solution. The films undergo pH dependent changes in their absorption spectra in the physiological pH range, and substituents exhibit a strong effect on pKa values. They are not based on the use of indicator dyes, are compatible with LED and diode laser light sources, and can be easily prepared. Copolymers of aniline substituted PANI are redox-active and respond to reductants such as ascorbic acid and hydrogen sulfide. Poly anilines (with various substitution patterns) were reported to be LEDcompatible sensor materials that can be easily deposited on solid support by oxidation of the respective aniline using ferric chloride. 50
74
Sol-Gel
based
pH
optodes
were
obtained
by
both
covalent
immobilization of aminofluorescein (AF) via isocyanate or epoxy groups, and by co-condensation of tetramethoxysilane (TMOS) and phenyltrimethoxysilane (ph-TriMOS) in the presence of AF. The addition of phTriMOS is found to exhibit a pronounced effect on the performance of the materials. Sensor layers based on TMOS doped with AF were found to be most appropriate for purpose of sensing pH in giving large relative signal changes and displaying rapid response times.52 The absorption indicator naphtholphthalein in a sol-gel gave a material with an optical response between pH 4 and 11,53 while naphtholfluoresceins were employed for measuring physiological pH values.54-56 Other sol-gels with immobilized pH probes have been described,
57, 58
one for solutions of
very high acidity.59 Fluorescent pH sensitive probes out of the class of the photoinduced electron transfer (PET) probes were immobilized in plasticized poly (vinyl chloride) membranes to result in a material that can be used for fiber optic pH sensing.
60
Since most fluorescent pH probes have decay times
in the order of nanosecond, probes with longer decay times are desired. Strategies to design pH sensor with luminescence decay times in the microsecond time regime have been presented.61 The pH-sensitive optodes could be used not only for pH sensing but also for the sensing of acidic and alkaline gas species such as CO2 and NH3.6265
Enzyme-based biosensing optodes could be prepared based on pH
optodes in which they measure pH change by monitoring protons generated or consumed by the enzyme reaction.
33, 65
In addition, the use
of H+ chromoionophore in determination and measurement of many cations are popular.2 For instance, a combination of Na+ ionophore and 75
one of many charged H+ chromoionophores incorporated into an oNPOEPVC membrane supported by a filter paper was used for visual and photometric determinations of Na+ in urine.66 Similar assays, based for example
on
4-[(2,6-diboromo-4-nitrophenyl)azo]-2-octadecyloxy-1-
naphthol as charged H+ chromoionophore, were reported for K+ analysis.2 Theory and Principles of pH optosensing are described in several publications.1, 3, 6, 7, 11, 67, 68 In addition, Reviews on the optode technology have been prepared by Wolfbeis,69 Bühlmann and coworkers, 2 Seitz,11 and others.46 2.2 Using CCD Cameras for Recording Signals Involving pH Data In 1995, a pH sensor array and an acetylcholine biosensor array, each of which contained approximately 6000 optical sensors, were coated with an immobilized layer of poly (hydroxy-ethylmetha-crylate)-N-fluoresceinylacrylamide and acetylcholin-esterase fluore-scein iso-thiocyanate isomer poly (acrylamide-co-N acryloxysuccin-imide), respectively. Fluorescence measurements and imaging were carried out using a modified epifluorescence microscope and a CCD camera. The response time of the pH sensor was 2 s. for a 0.5 unit increase in pH. The biosensor had a detection limit of 35 micro molar acetylcholine and a linear response in the range 0.1-5 mM.70 In 1996, Jaillard 71 applied the colored-pH-indicator method using a video camera
for
localizing
the
zones
along
plant
roots
where
acidification/alkalization occurs. It can also be used to assess the direction and intensity of the proton fluxes. The pH distribution around roots can be mapped with a relative error of 0.03 pH units. Toward the development of an Electronic Tongue, a new sensor methodology, which allows for the simultaneous identification of 76
multiple analytes in solution, was introduced
72 .
Advances in micro-
machining techniques, efficient and rapid data acquisition using a CCD were combined with known chemical indicators to create a single sensor. Polyethylene glycol-polystyrene (PEG-PS) resin beads that were derivatized with a variety of indicator molecules (fluorescien for pH, o cresolphthalein complexes for Ca2+ and pH, alizarin complexes for Ce 3+, Ca2+, pH) were exploited. Data streams composed of red, green, and blue light intensity were acquired for each of the individual beads. The resulting patterns were stored in a computer for analyte identification and ultimate quantification. From the latest application of CCD in chemical analysis, the images taken by the Mars Pathfinder satellite camera are used to obtain information about the chemical and mineralogical properties of the Martian stones and soil at the landing site.73
2.3 Calibration and Quantitation of Spectroscopic and Imaging Data Using Different Modeling Systems 2.3.1 Reported Modeling Systems for Evaluation of pH In 1991, Baumann and Buchanan 74 determined the pH of wastewater with Azo violet solution. Both a single-wavelength (554 nm) quadratic and a full-spectrum polynomial model for the calibration data were tested. The quadratic fit was easy to use, but the polynomial fit provided a measure of how well the spectrum fits the model, which was independent of colorimetric interference. Although there are some reports on the dynamic model of the pH optodes,
33, 75
the quantitative study of dynamic pH response of optodes 77
have not been fully discussed in the past. In 1992, a model, which describes the behavior of optical sensors with a hyperbolic type response, was published.76 This model is devoted mainly to the description of fluorescent sensors and does not take into account some special features of the absorption sensors. In 1993, a good theoretical description of pH optode was introduced by Kostov et al.75 They simulated the pH response behavior of an optical absorption based pH optode. They showed one experimental result in which the pH response was compared with the theoretical equation. However, their mathematical simulation expressed an ideal proton diffusion process and did not take into account the real response phenomena. The response change of a poly-HEMA / Nile blue pH optode was simulated by applying Fick’s law of diffusion via introduction of the diffusion factor variable with pH, D(pH) into a theoretical response equation by Hisamoto et al.33 However, because both pKa of immobilized pH indicator and diffusion coefficient of membrane depend on other factors such as temperature, ionic strength and composition of samples, more approaches for better dynamic models of pH optodes are demanded. 2.3.2 Modeling Using Solver Tool of Microsoft Excel Software In some of papers on least square fitting with Solver, the term of “Modeling” was used for description of “fitting experimental data on known models”. Virtually, no model for experimental data was presented and an appropriate term for description of the papers was “Curve Fitting”. They are limited to the following few works. In 1995, Walsh and Diamond 29 presented curve fitting of experimental data in chromatography peaks, fluorescence decay process, ion-selective electrode (ISE) characteristics, and ISE dynamic response in flow 78
injection analysis. In another work, a nonlinear curve fitting was performed using the quasi-Newton method for first-order kinetic reactions
with
Solver.77
78
Harris
fitted the
experimental
gas
chromatography data with the van Deemter equation and estimated uncertainties in the least-squares parameters using Solver. For mixtures of n-heptane and n-octane, the use of nonlinear regression analysis for the deconvolution and quantification of overlapped gas chromatographic peaks with the skewed Gaussian shape function using Solver were performed.79 2.3.3 Modeling Using Artificial Neural Networks (ANNs) History of using ANNs in optode field and for evaluation of pH data in other fields is limited to the following works.
2.3.3.1 Use of ANNs in Optode Field In 1994, applications of an optode to the Griess nitrite test, the biodegradation of p-nitrophenol in an Arthrobacter fermentation broth, and the immobilization of indicator dyes at the surface of different carriers for optical pH measurements were presented. In addition, the role of artificial neural network chemometric techniques to optode analyses was discussed.80 In 1996, an artificial neural network (ANN) was used to extend the working range of an optical fiber pH sensor. The application of the ANN allowed the working range of the sensor to be extended from its linear range (pH 5-7.25) to the full calibration range (pH 2.51-9.76). The sensor was based on the pH indicator, 3,4,5,6-tetra-bromo-phenol sulfonephthalein (TBPSP). The network was trained using the recursive 79
prediction error algorithm and spectral data for buffer solutions of pH 2.51-9.76. The worst error obtained during the testing of the network was 0.08 pH units.81 In 1997, a multilayer feed-forward artificial neural network was used to model the input-output data of an optical-fiber pH sensor. A set of 34 test solutions at pH 10.18-1.79 was used as the training set. For each test solution, the absorbances at three wavelengths were measured and used as the input data. The resulting model was tested with approximately 70 solutions of pH 10.17-1.60. The average prediction error was 0.2 pH units.82 Biosensors based on a pH optode were fabricated for penicillin, urea, glucose, and creatinine. A layer of the appropriate enzyme was immobilized directly on the optode tip carrying the fluorescent dye. For application in real processes, multichannel measurement was used and the biosensor was integrated into a FIA system. Evaluation of data by the trained neural network allowed measurement of urea in serum and penicillin G in buffer.83 An optode device containing two ion-selective membranes, one for K+ and one for Na+, was used for simultaneous determination of K+ and Na+. A back-propagation artificial neural network model was used to analyze the optode spectra and was found to be satisfactory for treating the nonlinearity embedded in the data.84 2.3.3.2 Use of ANNs for Evaluation of pH Data in Other Fields In 1994, a feed-forward neural network was developed which allowed both the penicillin G (benzyl-penicillin) concentration and the phosphate buffer concentration to be calculated from a FIA signal of a
80
semiconductor pH-FET detector. The total average errors were 4.7% and 4.9% for benzyl-penicillin and phosphate buffer ion, respectively.85 In 1997, a light emitting diode (LED) array was constructed with the use of six individual LED of four colors. The array was coupled to an artificial neural network for single heavy metal analysis (Cu, Hg, Zn, Pb, and Cd), simultaneous two-metal analysis (Hg and Cu), and pH determination. In the pH determination, the accuracy of results was very good for phenol red, bromothymol blue and bromocresol purple, but the standard errors of prediction for methyl red was double that for phenol red.86 2.4 Purpose of This Research The aim of this thesis is to develop some modern, powerful, and inexpensive techniques for wide-to-full range optical pH measurements using pH optodes. As a part of the thesis, attempts are going to be undertaken for the construction of simple, fast response and reversible optodes.
81
CHAPTER THREE EXPERIMENTAL 3.1 Fabrication of Optodes 3.1.1 Reagents Victoria blue (VB), hexanitro-diphenylamine (DPA), solo chrome dark blue (SCDB) and congo red (CR) were supplied by BDH; alkazine blue 4B (AB) was from Fluka; titan yellow GR (TY), and nile blue hydrogen sulfate (NB) were from Merck. All other indicators were analytical or bacteriological grade. Ethyl alcohol (96°) was medical grade. Ethylene diamine (97%) and all other reagents were analytical grade. 3.1.2 Apparatus Spectrophotometric measurements were performed with a flow cell mounted in a Philips PU8625 UV-Vis spectrophotometer and a Philips PU4815
computing
integrator
or
a
Jasco
V-530
UV-Vis
spectrophotometer attached to an IBM compatible personal computer. A Metrohm 632 pH meter with a Metrohm 6.0202.1200 glass electrode was used for monitoring pH adjustments. 3.1.3 Procedure 3.1.3.1 Membranes with Chemical Immobilization 3.1.3.1.1 Membranes The transparent triacetylcellulose membranes were produced from waste photographic film tapes that were previously treated with hot (< 85°C) sulfuric acid solution (about 0.6% (v/v)) in order to remove colored gelatinous layers. 3.1.3.1.2 Activation of Membranes 82
The activation of triacetylcellulose has been described previously. 37 Triacetylcellulose was previously hydrolyzed with 0.1 M KOH for 24 h to de-estrify the acetyl groups and increase the porosity of the membranes. The urea derivative was then prepared with treatment of the hydrolyzed membranes with sodium periodate 0.25 M at 22°C and pH 5 for 2 h in darkness. The membranes were washed with distilled water. The oxidized membranes were immediately treated with 15% (w/v) urea for 14h in the presence of 0.9% (v/v) sulfuric acid at 60°C. The membranes were washed with distilled water. The membranes were subsequently treated with 12.5% (v/v) formaldehyde in 0.1 M phosphate buffer (pH 7.5) at 45°C for 16 h with continuous stirring in a closed vessel. After careful washing with distilled water, the membranes were treated with a 0.01 g/ml solution of indicators. 3.1.3.2 Porous Membranes for Mechanical Immobilization 3.1.3.2.1 Membranes Membranes were prepared based on the procedure in section 3.1.3.1.1. 3.1.3.2.2 Activation of Membranes For improving the hydrophilicity, the membranes were placed between two filter papers moistened with 1:4 HClO4 :H2 O solution for 135 min (the optimum conditions) at ambient temperature of 30°C. The membranes were washed with distilled water. Then the membranes were treated with 0.1 M NaOH for 24 h (the optimum conditions). After washing with distilled water, the membranes were treated with a 0.1% (w/v) solution of indicators. These membranes were washed with hot acetone or ethanol for removal of extra dye; so that their absorbances were reached to an appropriate value often between 1.5 to 2. 3.1.3.3 Membranes with Dissolved Indicators 83
3.1.3.3.1 Membranes The transparent triacetylcellulose membranes were produced from waste photographic film tapes that were previously treated with commercial sodium hypochlorite for several seconds in order to remove the colored gelatinous layers. They simply were treated with a clear solution of indicators in ethylene diamine (about 510-3 g/ml) for five minutes (the optimum time) at ambient temperature. Then they were washed with water for removing ethylene diamine and loosely trapped dyes. These membranes were washed with hot ethanol for removing extra dye; so that their absorbances were reached to an appropriate value often between 1.5 to 2. Finally, the membranes were washed with detergent solution and water and kept under water when not in use. 3.1.3.3.2 Buffer Solutions Universal
aqueous
pH
buffer
solutions
were
prepared
from
acetic\phosphoric\boric acids (0.04 M respectively). The final pH was adjusted by the addition of 0.2 M sodium hydroxide or 4 M hydrochloric acid solutions. Alcoholic buffers were prepared from acetic\phosphoric \boric acids (0.04 M alcoholic solutions respectively), 0.2 M alcoholic sodium hydroxide solution. Alcoholic composition of buffers with nominal pH values of 1.81, 3.78, 5.72, 7.96, 9.91, and 11.98 were 82%, 67%, 59%, 51%, 46%, and 41% Vethanol/Vbuffer, respectively. 3.1.3.3.3 Measurements Absorption spectra of NB optode and NB alcoholic solutions (6.510-5 g/ml) were recorded at different pH values and at ambient temperature. From the absorption of NB optode in buffer solutions with different pH values, the pK´ of Nile blue in the membrane and its measuring range were determined at half-protonation point. The response-repeatability of 84
the membrane at 645 nm, for alternative changes of pH between 7.5 and 11.2 and alternative changes of pH from 12.3 to three different pH values of 10.1, 9.2, and 8.0 were recorded. 3.2 A Diffusion-Based Method in pH Optosensing 3.2.1 Apparatus A Watson Marlow 101F peristaltic pump was employed for pumping the solutions through the flow cell. 3.2.2 Procedure 3.2.2.1 Membrane The Nile Blue membrane was prepared based on the procedure in 3.1.3.3.1 section. This membrane had been washed with hot ethanol for removal of extra dye; so that its absorbance at max of 645 nm in acidic buffer solution (pH = 1.8) was about 1.8.
3.2.2.2 Measurements and Modeling Time response profiles for NB pH optode in 22 buffer solutions with different pH values were recorded. For each pH, a series of 35 absorbance signals from time response profile were chosen. The data were randomly split into calibration and validation sets consisting of 14 series and 8 series for a multi linear mathematical model and artificial neural networks model definition and evaluation, respectively.
3.3 A Time-Based Flow Analysis in pH Optosensing 3.3.1 Apparatus A Watson Marlow 101F peristaltic pump and a Desaga PLG-Peristaltic pump were employed for pumping the solutions through the flow cell. A 85
lab-made electrical power equipment driver with a computer interface was constructed for automatic control of pumps. 3.3.2 Procedure Signals of a time-based flow measurement were recorded with NB pH optode exposing to different pH buffer solutions (from 60 to 10 s in different\). The observed results validate the theoretical flow responses that are simulated from time response profiles. A schematic diagram of the experimental setup is shown in Figure 3.1.
Pump1
Pump2
Washing solution Sample buffer solutions
Integrator
Waste
Spectrophotometer
Figure 3.1 Set-up of flow measuring system.
3.4 Full Range Optical pH Measurement Using an Optode Array and a Video Camera
86
3.4.1 Apparatus Optode array responses were recorded using a Sony CCD TR750E video Hi8 Handycam. Setting all controls on Manual, its video output were connected to an IBM compatible personal computer with 24 bits true color Trident 9440 PCI graphic card via a PV-BT848 video capture card. Image analyzer program was written in Delphi 5 Software. All imaging and computing programs were run on a PC with 166 MHz Pentium processor and 32 MB RAM. 3.4.2 Procedure 3.4.2.1 Buffer Solutions Universal aqueous pH buffer solutions (pH=1.81-11.98) were prepared from acetic\phosphoric\boric acids (0.04 M respectively). The final pH is adjusted by the addition of 0.2 M sodium hydroxide solutions. For lower and higher pH values, -log [H+] and -log [OH] were used to calculate the actual pH values of the solutions. 3.4.2.2 Sensor Array Seven different pH optodes were prepared by coating TY, DPA, SCDB, CR, AB, VB, and NB indicators on the membrane as described previously. The proposed pH optodes were attached to a plastic square frame. The frame is fixed in a Petri dish (10 cm diameter 1 cm height) by a plastic net. The set up of the array was positioned on a piece of white paper in order to have a white background. The system was stable over 43 successive pH measurements in the pH range of 0-14. Sensor array was kept under water when not in use. A schematic diagram of the experimental setup is shown in Figure 3.2. 3.4.2.3 Modeling
87
In order to correlate sensor array responses with pH values, several mathematical models with Solver tool of Microsoft Excel software were investigated.
Figure 3.2 Schematic diagram of the video imaging.
88
CHAPTER FOUR RESULTS AND DISCUSSION 4.1 Fabrication of Optodes The immobilization of analyte-sensitive dyes on membranes is one of the most important steps in optode fabrication. The pH optodes mostly utilize pH indicator dyes, in which several kinds of dye immobilization methods in the bulk or surface optode sensing phase are used. Unique techniques such as trapping in dialysis tubing, adsorbing in polymer beads, covalently immobilizing on to porous glass or a cellulose membrane and ionically immobilizing on to an anion-exchange resin or sulfonated polystyrene surface for fabrication of proposed optodes were reported.1, 33 Among them, the covalent bonding immobilization is the most effective in terms of long optode lifetime, but this immobilization technique has not been fully established although it has been continuously investigated. Some pH optomembranes based on unmodified triacetyl cellulose have been prepared for much useful applications.34-37 In contrary to the others; these hydrophilic membranes do not need any modification for response time or stability improvement. 4.1.1 Membranes with Chemical Immobilization It is important to note that covalent immobilization methods developed for indicators containing amino group(s),
1, 37, 87
show good performance
for a limited number of these dyes. Kostov et al.37 showed that some indicators having amino groups or a free ortho position in their structures may be immobilized on a hydrolyzed triacetyl cellulose film. In our preliminary experiments, some indicators were immobilized with Kostov 89
et al. procedure.37 They are shown in Table 4.1 with plus sign for immobilization test. The immobilization test for optodes was performed using control of leakage of the dye when they were kept under pure water for 24 h. The other indicators in Table 4.1 (with minus sign for immobilization test)
showed considerable
immobilization
The
test.
reproducibility
leakage of
the
of
dye
procedure
after for
immobilization of proposed indicators was poor. It was not possible to obtain a reproducible qualitative optode when Nile blue (NB) stain was covalently immobilized on different batches, in spite of the efforts to reproduce the procedure accurately. 4.1.2 Porous Membranes for Mechanical Immobilization In preliminary experiments, some indicators were immobilized with the proposed procedure. The best optodes (using proposed procedure) were developed after treating the triacetyl cellulose with NaOH 0.1 M for 24 h (the optimum conditions) followed by placing the membrane between two filter papers moistened with 1:4 HClO4 :H2O solution for 135 min. The reagents are shown in Table 4.2 with plus sign for immobilization test.
Unfortunately,
the
reproducibility
of
the
procedure
for
immobilization of the proposed indicators was poor.
Table 4.1 List of indicators studied in our preliminary experiments using Kostov et al. procedure. Indicator
Formula
1
Nile Blue
C18 H17 N3 O5 S
+
2
Sudan Black B
C29 H24 N6
+
90
Immobilization Test
3
Victoria Blue
C33 H32 ClN 3
+
4
Fluorescent Yellow 3G
C36 H45 NO2 S
-
5
Fast Sulphon Black F
C30 H20 N4 O11 S3
-
6
Methyl Red
C15 H15 N3 O2
+
7
Fluoreszenz
C15 H10 O7
+
8
Ponceau S
C22 H12 N4 Na4 O13S4
-
9
Fuchsin
C20 H20 N3 Cl
+
10
PAR-Na
C11 H8 N 3 NaO 2 .aq
-
11
Pyrogallol Red
C19 H12 O8 S
-
12
Murexide
C8 H8 N 6 O6
-
Table 4.2 List of indicators studied with mechanical procedure in preliminary experiments. Indicator
Formula
Immobilization Test
1
Nile Blue
C18 H17 N3 O 5 S
+
2
Resazurin
C12 H6 NO4 Na
-
3
Victoria Blue
C33 H32 ClN 3
-
4
Fuchsin
C20 H20 N3 Cl
+
5
Janus Green
C30 H31 ClN 6
-
6
Fluoreszenz
C15 H10 O7
+
4.1.3 Membranes with Dissolved Indicators Among several solvents such as N, N-dimethylformamid (DMF), tetrahydrofuran (THF), ethylene diamine, ethylacetate and their mixtures, ethylene diamine was found as the best solvent for dissolution of indicators on the proposed triacetylcellulose membranes. The transparent membranes were treated with a clear solution of indicators in ethylene diamine (about 510-3 g/ml) for five min (the optimum time) at ambient 91
temperature. Then they were washed with water to remove ethylene diamine and loosely trapped dyes. These membranes were washed with hot ethanol to remove extra dye; so that their absorbances were reached to an appropriate value often between 1.5 and 2. Since the treatment of immobilized membranes with hot ethanol or acetone, resulted in the removal of the dyes, it is suggested that the proposed procedure is a physical immobilization method. The presented immobilization method is simple and rapid. It allows pH indicators with low solubility in water to be used widely in aqueous media. It leads to the development of inexpensive pH surface optodes. Several surface pH optodes (see Table 4.3) were successfully developed by this method. Due to its very low cost and rapid procedure of immobilization, it can be used for fabrication of disposable or single use probes. 4.1.3.1 Conventional studies on Nile Blue pH optode It has been shown that the Nile blue series chromoionophores are highly H+ selective.38 Highly sensitive Nile blue (NB) pH indicator with Table 4.3 Some pH optodes developed using dissolution of dyes on the unmodified triacetylcellulose membranes. Indicator
Formula
1
Nile Blue Hydrogen Sulfate
C18 H17 N3 O 5 S
2
Hexanitro-Diphenylamine
C12 H5 N7 O 12
3
Victoria Blue
C33 H32 ClN 3
4
Solo Chrome Dark Blue
C20 H14 N2 O 5 S
5
Congo Red
C32 H22 N6 Na2 O 6 S2
6
Alkazine Blue 4B
C32 H28 N3 NaO 4 S
7
Titan Yellow GR
C28 H19 N5 Na2 O 6 S4
92
wide use in non-aqueous media has been recommended 88 as an excellent indicator for the pH range of 9.0-10.4. Unfortunately, it suffers from extreme insolubility of its basic form in water. 88 However, by the use of the proposed procedure it is possible to use NB indicator in aqueous media. In order to investigate the performance of the proposed optode, the following studies were performed. 4.1.3.1.1 Absorption spectra Absorption spectra of NB optode and NB in alcoholic solutions (6.510-5 g/ml) taken at different pH values are shown in Fig. 4.1 and Fig. 4.2. The results show that the acidic maxima of the absorption spectra of immobilized dye are red shifted in comparison to those of its solution form and have maxima at 645 nm at the extreme pH values in comparison
93
2.0
1
Absorbance
1.6
1.2
16
0.8
12 13
0.4
1 0.0 400
16
500
600
700
800
Wavelength / nm. Fig. 4.1 Absorption spectra of Nile Blue optode in aqueous buffer solutions with pH values of: (1) 1.0, (2) 1.8, (3) 2.2, (4) 2.6, (5) 3.3, (6) 4.4, (7) 5.0, (8) 5.7, (9) 6.4, (10) 7.5, (11) 8.4, (12) 9.4, (13) 10.4, (14) 11.2, (15) 12.0, and (16) 12.7.
to the solution with maxima at 635 nm. It is in the nature of the interaction between the electromagnetic radiation with atoms, molecules, and ions that it results in counting their numbers, i.e. their concentration. Hence, here the concentration of H+ is measured instead of its activity. The solute-solvent (dye-triacetylcellulose) and solute-solute (dye-dye) interactions, which determine the value of the activity, show up only as a second-order effect, such as shifts of the absorption maxima, etc.3 The clear and fixed isobestic point of NB optode (Fig. 4.1) is a suitable wavelength for measuring the reference intensity for precalibration of sensor.11
94
2.0
1 2
Absorbance
1.6
3
1.2
6 0.8
4
5
0.4
5 6
0.0 400
500
600
700
800
Wavelength / nm. Fig. 4.2 Absorption spectra of Nile Blue indicator in alcoholic buffer solutions (6.510-5 g/ml) with nominal pH values of: (1) 3.0, (2) 5.3, (3) 6.7, (4) 9, (5) 11.3 and (6) 12.8.
4.1.3.1.2 Measuring Range The two limiting activities at which the slope of the response function (dependence of absorbance signals on pH values of samples) reduces to a quarter of its maximum value have been used to quantify the practical measuring range (7.3-10.8) of the pH optode described herein (see Fig. 4.3 and Table 4.4). Estimation of the best fitting curve on the curve of Fig. 4.3 for slope calculation was performed by Table Curve Windows Software. The sigmoidal transition function of Y=a+b/(1+exp(-(X-c)/d) was used in curve fitting, where a, b, c and d are constants.
95
1.8
Absorbance
1.5 1.2 0.9 0.6 0.3 0.0 0
2
4
6
8
10
12
14
pH Fig. 4.3 The absorbance of Nile Blue optode at 645 nm as a function of pH. Table 4.4 Data of optode absorbance at 645 nm at different pH values. pH Abs.
pH Abs.
pH Abs.
pH Abs.
1.0 1.701
3.3 1.597
6.4 1.353
10.4 0.241
1.8 1.679
4.4 1.543
7.5 1.235
11.2 0.146
2.2 1.673
5.0 1.499
8.4 1.022
12.0 0.096
2.6 1.643
5.7 1.437
9.4 0.567
12.7 0.085
96
4.1.3.1.3 Apparent Dissociation Constant (K´) The pK´ = 9.1 of Nile Blue in the membrane was determined at halfprotonation point (mid-point of pH measuring range)
88
using the
absorbance of NB optode in buffer solutions with different pH values (Fig. 4.3). The pK´ values of the NB pH indicator in the membrane (9.1) and water (9.7),88 show that as expected,35,
89
the acidity of the
immobilized indicator is greater than that of its solution form. 4.1.3.1.4 Response Reproducibility and Membrane’s Stability The reproducibility of the membrane at 645 nm, for alternative changes of pH between 7.5 and 11.2 (Fig. 4.4) and alternative changes Table 4.5
2
pH =7.5
Absorbance
1.6
1.2
0.8
0.4
pH =11.2 0 0
10
20
30
40
50
60
Time / min. Fig. 4.4 The response-reproducibility of the membrane at 645 nm, for alternative changes of pH between 7.5 and 11.2.
Data of response-reproducibility of the Nile Blue membrane. Exp. Num.
1
2
3
4 97
5
6
7
RSD
Acidic Abs 1.714
1.698
1.694
1.687
1.682
1.678
1.672
0.83%
Basic Abs.
0.258
0.256
0.254
0.254
0.254
0.258
0.95%
0.260
of pH from 12.3 to three different pH values of 10.1, 9.2, and 8.0 (Fig. 4.5) were recorded. The relative standard deviation is less than 1% for seven measurements of the maximum changes at 645 nm from the acidic form of the indicator to its basic form (Fig. 4.4 and Table 4.5).
1.6
pH = 8.0
pH = 8.0
9.2
9.2
Absorbance
1.2
0.8
10.1
10.1
0.4
12.3
12.3
0.0 0
10
20
30
40
Time / min. Fig. 4.5 The changes in the absorbance of the membrane at 645 nm for alternative changes of pH from 12.3 to three different pH values of 10.1, 9.2, and 8.0.
The proposed optodes are stable after several weeks of storing them under pure water. They show low leakage of dye (less than 1% in the absorbance at max ) under continuous flow (with flow rate of 14 ml/min)
98
using the proposed buffer solutions for more than 60 min (see Fig. 4.4 and Table 4.5). 4.1.3.1.5 Response Time The response time (the time required for the output signal to reach a value that differs by less than 5% from the steady-state signal) for six successive acidic to basic and basic to acidic forms of indicator transitions is 51 and 65 s, respectively. Tables 4.6 and 4.7 contain the data for response time calculation.
Table 4.6 The response time of NB optode for acidic to basic forms transitions. Initial Signal
0.260 0.258 0.255
0.254
0.254
0.254
Final Signal
1.701 1.695 1.688
1.683
1.678
1.673
62
67
69
Response Time
58
68
64
Average Response Time = 65 s
Table 4.7 The response time of NB optode for basic to acidic forms transitions. Initial Signal
1.701 1.694 1.688
1.683
1.678
1.672
Final Signal
0.258 0.255 0.254
0.254
0.254
0.258
54
50
50
Response Time
44
52
53
Average Response Time = 51 s
4.1.3.1.6. Conclusion The optode constructed by the above method possesses good stability, short response time, and good spectral characteristics. The ability of the membrane for pH measurement in aggressive samples such as hydrofluoric acid, fluorides, or in high-pressure environments; working under sterile conditions and the lack of interference from surface 99
potentials with NB optode is of particular interest, because a glass electrode cannot be used for these purposes. 4.1.3.2. Other pH optodes Spectra for Congo Red, Solo Chrome Dark Blue, Titan Yellow GR, Hexanitro-Diphenylamine, Victoria Blue pH optodes in different pH values are illustrated in Fig. 4.6 to Fig. 4.10. Tables 4.8 to 4.12 contain the observed data for measuring range computation at the wavelength of maxima of the proposed optodes.
100
3
13
Absorbance
2.5
1 3
2
2 2
1.5
3 1
1
0.5
4 13
0 400
500
600
700
800
Wavelength / nm. Fig. 4.6 Spectra of Congo Red pH optode at different pH values: (1) 0, (2) 1, (3) 1.81, (4) 2.87, (5) 3.78, (6) 4.78, (7) 5.72, (8) 6.80, (9) 7.96, (10) 8.95, (11) 9.91, (12) 10.88, (13) 11.98.
Table 4.8 Absorbances of Congo Red optode at 600 nm and at different pH values; and measuring range computation. pH Abs.
pH Abs.
pH Abs.
pH Abs.
09 2.173
2.87 0.177
5.72 0.085
8.95 0.085
19 1.647
3.78 0.100
6.80 0.084
9.91 0.082
1.81 0.944
4.78 0.087
7.96 0.085
10.88 0.084
Fitting function:
Y=a+b*erfc(((X-c)/d)2)
Measuring pH Range:
0.07-3.01
101
[Erfc Peak]
2
15 1.6
Absorbance
1 1.2
0.8
0.4
1
15
0 400
500
600
700
800
Wavelength / nm. Fig. 4.7 Spectra of Solo Chrome Dark Blue pH optode at different pH values. (1) 3.29, (2) 3.78, (3) 4.56, (4) 5.02, (5) 5.72, (6) 6.09, (7) 6.37, (8) 7.00, (9) 7.54, (10) 7.96, (11) 8.95, (12) 9.37, (13) 9.91, (14) 10.38, (15) 11.20.
Table 4.9 Absorbances of Solo Chrome Dark Blue optode at 646 nm and at different pH values; and measuring range computation. pH Abs.
pH Abs.
pH Abs.
pH Abs.
3.29 0.215
5.72 0.313
7.54 0.883
9.91 1.773
3.78 0.217
6.09 0.320
7.96 1.158
10.38 1.694
4.56 0.261
6.37 0.363
8.95 1.592
11.20 1.579
5.02 0.269
7.00 0.497
9.37 1.695
Fitting function:
Y=a+b/(1+exp(-(X-c)/d)
Measuring pH Range:
6.45-8.93
102
[Sigmoid]
2
7 8
Absorbance
1.6
1 6
1.2
5 5
0.8
4 0.4
1
0 400
450
500
550
600
Wavelength / nm. Fig. 4.8 Spectra of Titan Yellow pH optode at different pH values: (1) 11, (2) 11.4, (3) 11.98, (4) 12.48, (5) 12.7, (6) 13.18, (7) 13.7, (8) 14.
Table 4.10 Absorbances of Titan Yellow optode at 510 nm and at different pH values; and measuring range computation. pH Abs.
pH Abs.
pH Abs.
pH Abs.
110 0.170
11.98 0.317
12.70 0.729
13.7 1.913
11.4 0.189
12.48 0.452
13.18 1.366
140 1.777
Fitting function:
Y=a+b*exp(-0.5((X-c)/d)2)
Measuring pH Range:
12.17-13.65
103
[Gaussian]
3
1
Absorbance
2.5 2 1.5 1 0.5 0 300
19
400
500
600
700
800
Wavelength / nm. Fig. 4.9 Spectra of Hexanitro-Diphenylamine pH optode at different pH values: (1) 5.02, (2) 4.56, (3) 4.10, (4) 3.78, (5) 3.29, (6) 2.87, (7) 2.58, (8) 2.09, (9) 1.81, (10) 1, (11) 0.6, (12) 0.3, (13) 0, (14) –0.4, (15) –0.44, (16) –0.48, (17) –0.51, (18) –0.54, (19) –0.57.
Table 4.11 Absorbances of Hexanitro-Diphenylamine optode at 428 nm and at different pH values; and measuring range computation. pH Abs.
pH Abs.
pH Abs.
pH Abs.
5.02 3.089
2.87 2.973
0.6 1.705
-0.48 0.724
4.56 3.112
2.58 2.982
0.3 1.524
-0.51 0.655
4.10 3.089
2.09 2.722
00 1.241
-0.54 0.657
3.78 3.069
1.81 2.498
-0.4 0.822
-0.57 0.579
3.29 3.045
10 2.019
-0.44 0.752
Fitting function:
Y=(a+cX+eX2+gX3+iX4)/(1+bX+dX2+fX3+hX4)
Measuring pH Range:
(-0.57* )-2.67
104
* calculated
from -log[H+]
2
1
Absorbance
1.6
1.2
0.8
0.4
16
0 400
500
600
700
800
Wavelength / nm. Fig. 4.10 Spectra of Victoria Blue pH optode at different pH values: (1) 5.72,(2) 6.09, (3) 6.37, (4) 7.00, (5) 7.54, (6) 7.96, (7) 8.95, (8) 9.37, (9) 9.91, (10) 10.38, (11) 11.20, (12) 11.98, (13) 12.5, (14) 13, (15) 13.5, (16) 14.
Table 4.12 Absorbances of Victoria Blue optode at 618 nm and at different pH values; and measuring range computation. pH Abs.
pH Abs.
pH Abs.
pH Abs.
5.72 1.775
7.54 1.744
9.91 1.548
12.5 0.690
6.09 1.771
7.96 1.737
10.38 1.431
130 0.516
6.37 1.747
8.95 1.700
11.20 1.090
13.5 0.406
7.00 1.765
9.37 1.623
11.98 0.818
140 0.382
Fitting function:
Y=a+b*0.5(1+erf((X-c)/(20.5d)))
Measuring pH Range:
8.93-13.92
105
[Cumulative]
4.2 A Time-Based Flow Method in pH Optosensing for Wide Range Measurements Using a Single H+-Selective Chromoionophore 4.2.1 Dynamic Studies As mentioned previously, when the response signal of the NB pH optode was measured after equilibrium is reached, a measuring range of 7.3-10.8 was obtained. However, in order to obtain a wider dynamic range we tried to use the responses of optode before equilibrium is reached. In this case, the process of proton diffusion across the membrane mainly governs the response. This proton diffusion can be determined using the wellknown Fick’s law. Fig. 4.11 shows the typical experimental time response profiles of the proposed optode. The membrane shows a slower absorbance response change to protons when the difference between initial and final pH magnitude of membrane is decreased. Similar time response profile for a poly-HEMA / Nile blue pH optode has been reported previously (between pH 5-9).33 4.2.2 Simulation of Flow Responses Plotting the absorbance data (Table 4.13) from time response profile (Fig. 4.11) versus the corresponding final pH values, results in simulated pH response profile for different times of optode treatment with different pH solutions (Fig. 4.12). As a hypothesis, it is shown that with decreasing the time of optode exposure to the samples, pH-measuring range of optode shifts to acidic region so that an optode with conventional basic response region (7.3-10.8) can be used for measurement in high acidic region (02).
106
1.8
1
Absorbance
1.5
2
3
4
5
1.2 0.9
19
0.6 0.3
22 0.0 0
30
60
90
120
150
180
Time / min. Fig. 4.11 The time response profiles of the NB pH optode when the proposed optode with initial pH value of 13.3 is treated with 22 different universal pH buffer solutions with pH values between 0 and 12.07.
4.2.3 Flow Responses With well-known efficiency and ability of flow methods, it was decided to apply a flow system, in which the measurements are performed in the unfinished reaction mode. Preliminary experiments (Fig. 4.13) resulted from flow measurement at 30 and 45 s (Fig. 4.14) validated the above hypothesis. In flow measurements, the optode was treated with a high basic buffer washing solution (pH = 11.98) for reducing its absorbance to base line. The flow rates of washing solution and acidic solutions were 14 and 7.3 ml/min, respectively. In the proposed FA method with decreasing the
107
Table 4.13 The absorbance data for the time response profiles at different pH values. 0.00
0.50
0.90
1.50
2.07
pH 3.01
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
0.270 0.709 1.553 1.650 1.644 1.648 1.651 1.652 1.652 1.653 1.652 1.652 1.652 1.652 1.652
0.235 0.400 0.667 1.018 1.492 1.649 1.664 1.667 1.668 1.669 1.670 1.670 1.670 1.670 1.670
0.222 0.298 0.425 0.602 0.831 1.211 1.474 1.551 1.587 1.613 1.630 1.638 1.641 1.643 1.644
0.209 0.253 0.320 0.412 0.542 0.703 0.844 0.969 1.101 1.236 1.346 1.434 1.501 1.550 1.582
0.217 0.244 0.293 0.363 0.459 0.592 0.708 0.797 0.884 0.987 1.097 1.214 1.312 1.399 1.474
0.213 0.234 0.283 0.340 0.410 0.490 0.604 0.720 0.815 0.900 0.991 1.090 1.201 1.295 1.369
0.213 0.232 0.267 0.318 0.385 0.467 0.576 0.675 0.751 0.826 0.911 1.005 1.110 1.205 1.295
0.209 0.232 0.295 0.362 0.421 0.495 0.569 0.630 0.684 0.752 0.827 0.913 1.012 1.118 1.211
0.204 0.223 0.269 0.318 0.370 0.430 0.513 0.590 0.657 0.723 0.800 0.886 0.981 1.078 1.171
0.195 0.222 0.260 0.305 0.346 0.396 0.458 0.531 0.604 0.665 0.728 0.795 0.877 0.967 1.063
0.205 0.214 0.235 0.264 0.292 0.329 0.376 0.444 0.521 0.591 0.653 0.717 0.786 0.862 0.936
85 90 95
1.652 1.652 1.652
1.670 1.669 1.669
1.644 1.644 1.645
1.601 1.613 1.619
1.533 1.569 1.589
1.428 1.470 1.504
1.367 1.427 1.476
1.295 1.367 1.422
1.250 1.329 1.386
1.157 1.233 1.294
1.027 1.112 1.188
t (s)
57
3.42
4.06
4.38
4.96
5.80
Table 4.13 Continued
100 105 110 115 120 125 130 135 140 145 150 155 160
0.00 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651 1.651
0.50 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.669 1.668
0.90 1.645 1.644 1.645 1.644 1.644 1.644 1.644 1.644 1.644 1.644 1.644 1.644 1.644
1.50 1.623 1.624 1.625 1.625 1.625 1.626 1.626 1.626 1.624 1.626 1.626 1.625 1.625
2.07 1.602 1.609 1.614 1.615 1.616 1.617 1.617 1.617 1.617 1.617 1.617 1.619 1.618
pH 3.01 1.524 1.540 1.552 1.560 1.567 1.571 1.575 1.577 1.579 1.580 1.580 1.581 1.581
165 170 175 180
1.651 1.651 1.650 1.649
1.668 1.668 1.668 1.668
1.644 1.644 1.644 1.643
1.625 1.624 1.624 1.624
1.618 1.618 1.617 1.617
1.582 1.582 1.582 1.582
t (s)
58
3.42 1.509 1.532 1.547 1.559 1.568 1.574 1.578 1.583 1.586 1.589 1.591 1.593 1.594
4.06 1.458 1.486 1.502 1.514 1.523 1.529 1.528 1.539 1.540 1.542 1.543 1.542 1.542
4.38 1.426 1.453 1.474 1.488 1.500 1.509 1.515 1.520 1.524 1.527 1.530 1.531 1.533
4.96 1.341 1.381 1.406 1.426 1.441 1.453 1.463 1.472 1.479 1.485 1.490 1.494 1.497
5.80 1.247 1.291 1.325 1.349 1.366 1.380 1.390 1.398 1.405 1.410 1.415 1.420 1.424
1.595 1.596 1.595 1.595
1.541 1.540 1.540 1.539
1.532 1.533 1.533 1.532
1.500 1.503 1.500 1.506
1.422 1.431 1.434 1.434
Table 4.13 Continued t (s) 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
6.46
6.99
7.44
8.01
8.70
pH 9.04
0.203 0.212 0.226 0.246 0.271 0.294 0.326 0.377 0.448 0.541 0.643 0.738 0.832 0.913 0.991 1.069 1.140 1.195
0.209 0.220 0.231 0.247 0.266 0.283 0.303 0.326 0.359 0.416 0.498 0.603 0.718 0.812 0.888 0.957 1.016 1.070
0.217 0.222 0.229 0.241 0.251 0.262 0.274 0.291 0.317 0.363 0.432 0.534 0.631 0.714 0.787 0.846 0.898 0.945
0.209 0.216 0.220 0.226 0.234 0.238 0.245 0.257 0.270 0.294 0.330 0.382 0.453 0.546 0.644 0.730 0.803 0.867
0.210 0.217 0.220 0.223 0.228 0.232 0.238 0.247 0.263 0.286 0.321 0.371 0.442 0.522 0.602 0.677 0.742 0.795
0.212 0.214 0.213 0.217 0.219 0.220 0.223 0.228 0.236 0.248 0.269 0.302 0.349 0.414 0.493 0.564 0.628 0.684 59
9.47
10.19
10.47
11.14
12.07
0.209 0.216 0.217 0.219 0.222 0.224 0.227 0.230 0.235 0.241 0.252 0.266 0.286 0.318 0.359 0.409 0.463 0.514
0.211 0.214 0.215 0.217 0.218 0.220 0.222 0.224 0.226 0.229 0.233 0.238 0.246 0.256 0.269 0.284 0.303 0.328
0.214 0.215 0.216 0.217 0.219 0.221 0.222 0.224 0.226 0.229 0.233 0.239 0.246 0.254 0.265 0.277 0.291 0.305
0.214 0.214 0.215 0.216 0.217 0.219 0.220 0.222 0.223 0.226 0.229 0.232 0.235 0.240 0.244 0.250 0.256 0.262
0.209 0.214 0.214 0.215 0.216 0.216 0.217 0.218 0.219 0.221 0.223 0.224 0.226 0.226 0.228 0.229 0.230 0.231
Table 4.13 Continued t (s) 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180
6.46
6.99
7.44
8.01
8.70
pH 9.04
1.238 1.272 1.296 1.315 1.328 1.338 1.347 1.353 1.358 1.362 1.365 1.368 1.371 1.373 1.374 1.376 1.377
1.116 1.156 1.191 1.221 1.245 1.267 1.284 1.297 1.307 1.315 1.322 1.328 1.332 1.336 1.341 1.342 1.346
0.990 1.029 1.065 1.101 1.131 1.156 1.178 1.197 1.215 1.229 1.242 1.253 1.263 1.271 1.279 1.285 1.290
0.919 0.963 1.003 1.035 1.062 1.087 1.108 1.127 1.143 1.159 1.172 1.183 1.193 1.202 1.210 1.218 1.224
0.839 0.877 0.909 0.937 0.964 0.985 1.003 1.019 1.033 1.045 1.057 1.066 1.074 1.082 1.089 1.096 1.101
0.732 0.772 0.806 0.834 0.858 0.878 0.897 0.914 0.926 0.936 0.945 0.954 0.961 0.968 0.973 0.979 0.983 60
9.47
10.19
10.47
11.14
12.07
0.562 0.606 0.644 0.679 0.707 0.733 0.754 0.771 0.785 0.799 0.811 0.821 0.829 0.836 0.843 0.849 0.854
0.349 0.373 0.396 0.418 0.438 0.456 0.473 0.487 0.500 0.511 0.520 0.528 0.535 0.541 0.545 0.551 0.555
0.323 0.339 0.353 0.367 0.381 0.393 0.405 0.415 0.423 0.431 0.439 0.445 0.449 0.454 0.457 0.461 0.463
0.267 0.274 0.279 0.285 0.290 0.294 0.298 0.302 0.305 0.309 0.311 0.314 0.316 0.317 0.318 0.320 0.321
0.231 0.232 0.232 0.233 0.233 0.233 0.234 0.235 0.234 0.234 0.235 0.235 0.235 0.235 0.235 0.235 0.236
1.8
180s
1.5
Absorbance
1.2 0.9 0.6 0.3
15 10s
0 0
3
6
9
12
pH Fig. 4.12 Simulated flow analyses results at 5 to 180 s from the time response profile.
flow time, the measuring range shifts to high acidic region (Fig. 4.13). Table 4.14 contains absorbance of the proposed optode at 30 and 45 s in the flow studies. Fig. 4.13 and Fig. 4.14 show that the membrane can be used for pH measurement in the pH range 1-10. The data obtained in here is comparable with Fig. 4.12, which has been obtained by simulation of the results in Fig. 4.11. 4.2.3.1 Flow Responses Reproducibility The relative standard deviation is less than 1% (n = 7) in transitions between pH values of 7.5 and 11.98 at 45 s flow time (Fig. 4.15).
61
2.0
Absorbance
1.6 1.2 0.8 0.4 0.0 0
2
4
6
8
10
12
pH Fig. 4.13 Typical flow results in preliminary studies at 30 (▲) and 45 s () for different universal pH buffer solutions.
Table 4.14 Absorbances of the proposed optode at 30 and 45 s in the flow studies. pH 10.88 9.91 8.95 7.96 6.80 5.72
Abs. at 30 s Abs. at 45 s 0.293 0.301 0.338 0.374 0.441 0.572
0.252 0.269 0.417 0.607 0.873 1.144
pH 4.78 3.78 2.87 1.81 1.01
62
Abs. at 30 s Abs. at 45 s 0.614 0.756 0.860 1.363 1.773
1.268 1.467 1.580 1.582
1.01
1.81
2.87
3.78
4.78
5.72
6.80
7.96
pH=
Absorbance
1.4
8.95
1.6
9.91
10.88
1.8
(30 s)
1.2 1.0 0.8 0.6 0.4 0.2 0
6
12
18
24
30
36
Time / min.
1.8
(45 s) 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0
7
14
21
28
35
42
Time / min. Fig. 4.14 Typical flow diagram at 30 and 45 s for different universal pH buffer solutions in preliminary studies. From three peaks for each pH, the average of the second and the third peak was used in calculations.
63
1.0
7.5
Absorbance
0.9
0.7
0.6
11.2
0.4
0.3 0
3
6
9
12
15
18
Time / min. Fig. 4.15 The response-reproducibility of the membrane at 645 nm, for alternative change of pH between 7.5 and 11.98 in flow system.
Table 4.15 Data for monitoring flow response reproducibility. Exp. Num. Absorbance
Table
4.15
1 0.945
2 0.944
contains
data
3 0.940
for
4 0.941
5 0.945
investigation
of
6 0.960
7 0.961
flow response
reproducibility. It should be noted that (due to unknown factors) usually first peak in each series of pH measurements was useless. Therefore, the first peak in all measurements was omitted and all calculations were based on the other peaks.
64
4.2.4 Conclusion Regarding the above discussion, it can be concluded that the level of the output signals at different times depend on the difference between the initial and final pH values, the flow time of the flowing solutions, and the inherent response time of the sensor that influences the diffusion coefficient. The presented flow analysis (FA) has several important characteristics compared with batch measurements; higher sampling rate, enhanced response time, and improved precision. Furthermore, the sample is exposed to the detector for only a very short period. The rest of the time the detection system is cleansed by the washing solution, which means the easy control of the accurate behavior of system and precision of sequence measurements is possible. These advantages make it feasible and economic to apply automated measurements to a relatively few samples of a non-routine kind. One of the best feature of such optosensing method, apart from its wide measuring range, is its ability to measure pH values at two extreme values of the pH range, where glass electrodes encounter acid and alkaline errors.
65
4.3 A Diffusion-Based Method in pH Optosensing for Wide Range Measurements Using a Single H+-Selective Chromoionophore with Approaches to Mathematical and Artificial Neural Networks Modeling 4.3.1 Modeling of Diffusion-Controlled Results Simulation, modeling, curve fitting, statistical, and numerical analysis are often a major and inevitable part of chemical experimentations. In this section, approaches to mathematical and artificial neural networks (AANs) modeling, will be tried to simulate the diffusion-controlled response of the proposed optode. Finally, the pH-measuring range of the proposed pH optode will be extended to more than 11 units using AANs. 4.3.1.1 Mathematical Modeling 4.3.1.1.1 Modified Mathematical Model of Kostov and Coworkers In 1996, Hisamoto and coworkers
33
simulated the response change of a
poly-HEMA / Nile blue pH optode by applying Fick’s law of diffusion via introduction of the diffusion factor variable with pH, D(pH), rather than the diffusion constant, D, into a theoretical response equation (equation
S
4
10 - pHin
- 10 - pHfn
r 0
L
A (t, pH) 0
110 -pKind
2r 1 2 2r 1 exp - D x t sin 2r 1 l l 1
2 10 -pHfn S Tdx 10 - pKind - 10 - pHfn S
4.1) which had been presented previously.75 66
(4.1)
In equation 4.1 [T] is the total concentration of the indicator, D is the membrane diffusion coefficient, l is the membrane thickness, Kind is the dissociation constant, pHin is initial pH of the membrane, pHfn is final pH of the membrane, t is the time of exposing the membrane to a buffer solution with pHfn , and 1 and 2 are the molar absorption coefficients of the dissociated and undissociated forms of the immobilized indicator, respectively. 4.3.1.1.2 The Analyte Concentration-Dependent Diffusion Coefficient, D(pH) As already stated, introduction of the variable diffusion factor, D(pH), into the above theoretical equation was attempted in order to describe the real experimental response with mathematical simulation by Hisamoto et al.
33
They had simulated the response of a poly-HEMA / Nile blue pH
optode between pH values of 6 and 8, using their RFA (Rapid FlowThrough Analysis) system. Hence, we decided to use equation 4.1 for mathematical
simulation of
our
NB pH optode response. The
experimentally obtained diffusion coefficients as a function of pH are plotted in Fig. 4.16, in which the values were calculated with equation 4.2. D l 2 /t
(4.2)
The equation 4.2 was derived from Fick’s law of diffusion, where l and t represent the membrane thickness (approximately, 0.025 cm) and the required time (s) for 100% response, respectively. As shown in Fig. 4.16, there is an obviously difference in the diffusion coefficient values in 67
Diffusion Coefficient
1.2E-05 1.0E-05 8.0E-06 6.0E-06 4.0E-06 2.0E-06 0.0E+00 0
2
4
6
8
10
12
pH Fig. 4.16 Diffusion coefficient as a function of pH for the proposed NB pH optode.
different pH values. Hence, it is expected that the introduction of the analyte concentration-dependent diffusion coefficient, D(pH), rather than the diffusion constant, D, to the theoretical equation could better fit the real experimental response. Table 4.16 Diffusion coefficient values calculated from required time for observation of 100% response in different pH values. PH t (s) D (×106)
1.77 56 11.2
3.26 115 5.43
4.16 159 3.93
5.25 192 3.26
6.41 197 3.17
7.38 226 2.77
8.75 222 2.82
9.79 217 2.88
Therefore, D(pH) was used in equation 4.1, in which D(pH) was derived by Table Curve Windows Software. The software derived equation 4.3
68
for calculated D values from equation 4.2 (see Table 4.16) and corresponding pH values. D(pH) = (7.478723×10-12 + 6.5938295×10-10 × exp(-pHfn))½
(4.3)
4.3.1.1.3 A Computer Program for Evaluation of The Mathematical Model In preliminary experiments (with presented data by Hisamoto et al Equation 4.1 was integrated numerically using Trapezoid’s method
33 ),
90
in
41 points, and the infinite series (in S statement of Equation 4.1) was summed to the 10000 th term. It was found that the proposed mathematical model is nearly valid only for simulation of response profiles of the proposed pH optode in pH values between 6 and 8. Because the above computations are time consuming, more modification, and extension (and further complication) of the proposed mathematical model was useless. Hence, it was decided to use another mathematical model or a nonmathematical model (for instance, ANNs) for simulation of the proposed response profiles. The program for the above computations was written in a function using Matlab Command Window version 5.3 (see Appendix A). 4.3.1.2 Other Mathematical Models Using Solver
tool
mathematical
models
of Microsoft Excel 2000 software, several such
as
multi-linear,
power,
polynomial,
logarithmic, and exponential models were investigated. Finally, a multi linear model was found as the best model for fitting the experimental response profiles. 69
4.3.1.2.1 Mathematical Modeling Using a Multi -Linear Model 4.3.1.2.1.1 Data Description For each pH, a series of 35 absorbance signals from time response profile (Fig. 4.11) were chosen. The data (see Table 4.13) were randomly split into calibration and validation sets consisting of 14 series and 8 series for model definition and evaluation, respectively. 4.3.1.2.1.2 Calibration Procedures The calibration set of data was used as the input for Solver tool with Generalized Reduced Gradient (GRG2) nonlinear optimization algorithm. A
prediction mean square error (MSE) was calculated for the proposed method by: n MSE (t i - oi ) 2 /n i 1
(4.4)
where ti is the actual pH of the ith sample, oi is the predicted pH for the ith sample and n is the total number of samples. The multi-linear model (Y=C0+C1X1+C2X2+…+C35X35) estimates pH values (Yi) from absorbance signals (Xi1 … Xi35). Solver was used to optimize the values of Coefficients (C0-C35) to obtain the best fit of the experimental pH values to the estimated pH values. In the worksheet, the Target Cell represents the sum of the squares of the residuals (SSR), which Solver tries to minimize its value during adjustment of the coefficients with initial values of zero. Table 4.17 contains the setting for Solver tool. Table 4.18 contains data for calculated parameters of the proposed model. 70
Table 4.17 The setting for Solver tool of Microsoft Excel 2000. Set Target Cell: SSR
Equal to: Min
Max. Time: 10 second Tolerance: 5%
Iterations: 13 Convergence: 0.0001
Estimates: Tangent
By changing cells: Coefficients
Derivatives: Forward
Precision: 0.000001 Search: Newton
Table 4.18 Calculated coefficients of the multi-linear model with Solver. The C 0 and C 35 correspond to the response profile of 10 and 180 s, respectively. C0 =-1.485322685 C4 = 0.580770181 C8 =-0.062080709 C12 = 0.515817037 C16 = 0.604625741 C20 = 0.401398061 C24 = 0.192234925 C28 = 0.089075446 C32 =-0.002852511
C1 =-0.262584969 C2 = 0.041167859 C5 = 0.279640468 C6 =-0.419820493 C9 = 0.296158622 C10 = 0.468722964 C13 = 0.574260324 C14 = 0.622928416 C17 = 0.536310219 C18 = 0.521340244 C21 = 0.345848598 C22 = 0.282614746 C25 = 0.161420487 C26 = 0.134796531 C29 = 0.058859421 C30 = 0.040079608 C33 =-0.011624932 C34 =-0.029781493
C3 = 0.728065211 C7 =-0.538397530 C11 = 0.521077955 C15 = 0.629494188 C19 = 0.457979419 C23 = 0.229920174 C27 = 0.105987966 C31 = 0.018892059 C35 =-0.041733143
The True and Predicted pH values with the proposed multi-linear model are illustrated in Fig. 4.17. Table 4.19 contains the pH data and their correlation.
71
Predicted pH values
12 10 8 6 4 2 0 0
2
4
6
8
10
12
True pH values Fig. 4.17 Correlation plot for the True and Predicted pH data using the proposed multi-linear model with Solver tool.
Table 4.19 The True and Predicted pH values with the proposed model. True values 11.14 9.47 Predicted values 11.16 9.33 Correlation = 0.998
8.01
6.99
4.96
4.06
2.07
0.50
7.87
7.02
5.52
3.98
2.01
0.55
MSE = 0.046
Average Error = 0.13
4.3.1.2.1.3 Conclusion The calibration and prediction procedure by Solver is simple and user friendly, thanks to which dynamic displaying and complete observation of data and results are possible. A good correlation for true and predicted pH values with a multi-linear mathematical model was obtained. The
72
procedure at small number of iterations (13) was free from boring and time-consuming parameters optimization. 4.3.1.3 ANNs Modeling 4.3.1.3.1 Data Description For each pH, a series of 35 absorbance signals from time response profile (Fig. 4.11) were chosen. The data (see Table 4.13) were randomly split into calibration and validation sets consisting of 14 series and 8 series for model definition and evaluation, respectively. 4.3.1.3.2 Calibration procedures For the data set described above, the calibration set was used as the input for artificial neural network. A multilayer feed-forward artificial neural network with the back-propagation of errors learning algorithm was used to model the input-output data of the proposed optical pH sensor. A minimum in the error of prediction occurred when three nodes (with sigmoidal transfer function) were taken in one hidden layer. The optimized learning rate (η) of 0.1 and momentum () of 0.2 at continued training up to 6030 were obtained. The results with average error (average of differences between True and Predicted pH values) of 0.11, MSE value of 0.02 and correlation value
91
of 0.999 is illustrated in Fig.
4.18. Table 4.20 contains True and Predicted pH data.
Table 4.20 True and Predicted pH data. True pH Predicted pH
0.50 0.53
2.07 2.18
4.06 3.73
4.96 5.07 73
6.99 6.88
8.01 8.09
9.47 9.39
11.14 11.11
12
True pH values
10 8 6 4 2 0 0
2
4
6
8
10
12
Predicted pH values Fig. 4.18 The correlation between True and Predicted pH data with Artificial Neural Networks model.
4.3.1.3.3 Conclusion One of the best feature of such optosensing method, apart from its wide measuring range, is its ability to measure pH values at low extreme of the pH range, where glass electrodes encounter acid errors. In contrast to the complicated mathematical models that often require time consuming and complex computations, the proposed ANN model allows rapid estimation of pH values from input data.
74
4.4 Full Range Optical pH Measurement Using an Optode Array and a Video Camera with Approach to a Novel Method in Image Processing 4.4.1 Data Description 4.4.1.1 RGB Values Changes in colors of an array of optodes composed of five optical sensors that respond in full pH range (0-14), were recorded using a video camera. The data composed of red, green, and blue (RGB) light intensities from a CCD camera were transferred and stored in a computer through a video capture card. Forty-four frames with 144192 pixels format for 43 different pH solutions were recorded in the light of fluorescent tube lamps of the laboratory. Each pixel (elements of CCD array 26 ) represents three values in the range of 0 to 255 corresponding to the red, green, and blue (RGB) light intensities. So for black color R, G, B values are 0, 0, 0, and for white color R, G, B values are 255, 255, 255, respectively and for other colors, they are between these two extreme values. 4.4.1.2 Analytical Signals Instead of the absorbance of the proposed pH optodes (at their wavelength of maximum absorption), the RGB values of pixels of the pH optode’s pictures were taken as analytical signals. Hence, for each pH, a series of 15 signals (3 colors 5 optodes) were recorded. In order to correlate sensor array responses to pH values, the resulting patterns were analyzed using a multi linear mathematical model with Solver tool of Microsoft Excel software. The data were randomly split into calibration 75
and validation sets consisting of 30 and 13 spectra for model definition and evaluation, respectively. Imaging analytical signals for congo red (CR), nile blue (NB), solo chrome dark blue (SC), victoria blue (VB), and titan yellow G (TY) pH optodes are illustrated in Figures 4.19, 4.20 and 4.21. Tables 4.21, 4.22, and 4.23 contain data for analytical signals.
250
Red Intensity
200
150
100
50
0 0
2
4
6
8
10
12
14
pH Fig. 4.19 Red Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes.
76
Table 4.21 Red analytical signals for pH optodes in different buffer solutions. 0Indicator0 CR NB SC TY VB CR NB SC TY VB
pH values 0.00 0.30 0.60 1.00
1.20
1.50
1.81
2.09
2.21
2.56
2.87
3.29
3.78 4.10 4.35
60 57 61 81 81 81 113 112 124 206 208 205 67 70 74
56 80 107 204 71
60 80 108 207 72
56 79 106 205 73
59 77 104 201 67
69 80 113 209 71
78 80 122 207 72
84 78 121 207 72
102 75 122 206 69
114 76 122 212 70
130 74 118 208 70
4.56 4.78 5.02 5.33
5.72
6.09
6.37
6.59
6.80
7.00
7.24
7.54
7.96 8.36 8.95
141 141 141 74 75 73 120 121 118 211 213 216 70 71 73
139 69 108 216 72
141 67 105 212 72
141 66 102 216 74
141 64 101 216 73
140 63 95 217 72
140 63 82 217 74
135 62 76 217 70
138 60 73 217 71
138 60 68 217 69
142 72 117 215 70
9.37 9.62 9.91 10.38 10.88 11.20 11.40 11.70 11.98 12.50 13.00 13.50 14.00 CR NB SC TY VB
150 146 150 61 61 61 69 69 69 211 223 225 63 60 56
150 67 69 222 51
155 74 70 221 44
159 80 71 207 36
161 84 74 210 36 77
162 88 85 195 32
163 89 81 184 33
161 93 82 177 32
162 93 81 173 36
162 92 76 170 44
161 90 80 169 65
138 131 78 76 123 122 211 209 73 72 136 141 60 59 68 67 216 220 66 66
250
Green Intensity
200
150
100
50
0 0
2
4
6
8
10
12
14
pH Fig. 4.20 Green Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes.
4.4.2 Calibration Procedures The calibration set of data was used as the input for Solver Excel Microsoft with Generalized Reduced Gradient (GRG2) nonlinear optimization algorithm. A multi-linear model (Y=C0+C1X1+C2X2+…+C15X15) estimates pH values (Yi) from RGB patterns (Xi1 … Xi15). Solver was used to optimize the
values of Coefficients (C0-C15) to obtain the best fit of the experimental pH values to the estimated pH values. In the worksheet, the Target Cell represents the sum of the squares of the residuals (SSR), which Solver tries to minimize its value during adjustment of the coefficients with initial values of zero. Table 4.24 contains the setting for 78
Table 4.22 Green analytical signals for pH optodes in different buffer solutions. 0Indicator0 CR NB SC TY VB
1
1.2
1.5
pH values 1.81 2.09 2.21
60 110 66 195 89
58 111 67 196 89
53 110 66 198 88
48 103 65 194 85
51 107 70 202 88
52 107 72 203 88
54 107 74 203 87
58 103 74 202 86
62 106 75 212 87
67 101 73 209 86
4.56 4.78 5.02 5.33
5.72
6.09
6.37
6.59
6.80
7.00
7.24
7.54
7.96 8.36 8.95
74 74 74 72 73 73 74 74 75 95 94 90 84 78 75 74 71 70 76 74 75 72 72 73 73 74 64 212 215 215 215 211 215 215 216 215 87 87 88 89 89 88 89 90 90 9.62 9.91 10.38 10.88 11.20 11.40 11.70 11.98 12.5
71 67 61 215 86 13
73 66 63 216 86 13.5
72 63 63 215 82 14
87 50 98 104 30
86 50 88 98 31
86 48 80 100 42
0
0.3
0.6
73 70 66 119 118 115 69 69 74 194 197 196 88 90 90
CR NB SC TY VB
73 97 74 210 87 9.37
CR NB SC TY VB
80 77 79 50 48 47 83 82 84 207 221 222 74 71 67
80 44 85 218 57
82 45 88 214 49
85 47 89 195 39
86 48 92 192 37 79
87 50 108 170 31
87 50 103 139 31
2.56
2.87
3.29
3.78 4.10 4.35
86 49 101 120 31
73 68 100 99 74 75 209 209 87 87 71 73 60 54 65 74 216 217 82 78
250
Blue Intensity
200
150
100
50
0 0
2
4
6
8
10
12
14
pH Fig. 4.21 Blue Imaging Signals for CR (■), NB (◊), SC (●), VB (*), and TY (□) optodes.
Solver tool. Table 4.25 contains data for calculated parameters of the proposed model. A good correlation (0.99965) for true and predicted pH values with a multi-linear mathematical model was obtained in a short time. The procedure at small number of iterations (61) was free from boring and time-consuming parameters optimization. True and Predicted pH values with the proposed multi-linear model are illustrated in Fig. 4.22. Table 4.26 contains pH data and their correlation.
80
Table 4.23 Blue analytical signals for pH optodes in different buffer solutions. 0Indicator0 CR NB SC TY VB
0
0.3
0.6
1
1.2
1.5
pH values 1.81 2.09 2.21
111 158 73 121 142
107 157 73 127 147
100 157 78 127 150
90 153 70 129 147
83 153 70 133 149
76 153 69 134 148
66 145 68 130 139
60 149 73 140 145
57 148 76 141 147
57 149 81 141 145
54 144 80 143 142
56 147 81 155 143
57 142 80 152 143
4.56 4.78 5.02 5.33
5.72
6.09
6.37
6.59
6.80
7.00
7.24
7.54
7.96 8.36 8.95
62 61 59 61 62 62 60 58 138 132 127 122 118 116 115 114 81 84 79 81 81 83 84 77 156 161 158 153 157 159 158 158 145 142 149 147 144 147 148 148 9.91 10.38 10.88 11.20 11.40 11.70 11.98 12.5
61 110 72 159 141 13
55 107 73 159 142 13.5
58 106 78 157 138 14
69 74 127 70 35
72 73 115 67 31
70 71 107 68 42
CR NB SC TY VB
61 139 82 155 143 9.37
CR NB SC TY VB
67 64 66 85 80 76 105 103 104 150 163 165 123 119 112
63 140 83 157 146 9.62
65 73 108 157 94
66 71 111 153 75
68 70 111 130 55
70 73 116 125 47 81
69 72 134 103 41
71 71 129 83 39
2.56
2.87
3.29
3.78 4.10 4.35
69 72 130 72 36
60 58 143 142 81 81 151 153 143 144 56 61 100 90 83 95 158 158 134 130
82
Table 4.24 The setting for Solver tool of Microsoft Excel. Set Target Cell: SSR
Equal to: Min
By changing cells: Coefficients
Max. Time: 35 second Tolerance: 5%
Iterations: 61 Convergence: 0.0001
Precision: 0.000001
Use Automatic Scaling: True Estimates: Tangent
Derivatives: Central
Search: Conjugate
Table 4.25 Calculated coefficients of the multi-linear model with Solver. Colors
Indicator
Red
Green
C1 = 0.034898247* C2 = 0.038844321 C3 =-0.037742179 C4 = 0.058095757 C5 = 0.054076932
CR NB SC TY VB
Blue
C6 = 0.000369569 C7 =-0.033916849 C8 = 0.004834512 C9 =-0.032584788 C10 =0.008633708
C11 =-0.021566614 C12 =-0.022523454 C13 = 0.026546587 C14 =-0.006972518 C15 =-0.016377395
*C0= 0.000533905
Table 4.26 True and Predicted pH values with the proposed model. True pH
Predicted pH
True pH
Predicted pH
0.6 1.5 2.21 3.29 4.35 5.02 6.09
0.45 1.16 2.14 3.26 4.27 4.98 5.99
6.8 7.24 8.36 9.62 10.88 11.7 13
6.79 7.12 8.08 9.77 10.88 11.64 13.00
Correlation = 0.99965
MSE = 0.03
Average Error = 0.1
14
Predicted pH values
12 10 8 6 4 2 0 0
2
4
6
8
10
12
14
True pH values Fig. 4.22 Correlation plot for True and Predicted pH data using the proposed multilinear model with Solver tool.
4.4.3 Interferences The proposed immobilized indicators were tested for any possible interference from ions, which have been previously reported to react with indicators (see Table 4.27). No interference from the ions was observed when the indicators were immobilized on the proposed optode. Table 4.27 List of reported interferences for the free indicators.
Indicator
Ions*
NB VB TY Mg2+ (pH=12.5)92 CR Hg2+ (pH=5.5)93 SCDB Ca2+, Cd2+ (pH=11.5), Mg2+, Mn2+, Zn2+ (pH=10)93 * Interfering ions that were reported for the proposed free indicators.
4.4.4 Measuring Ranges The two limiting activities at which the slope of the response function reduce to quarter of its maximum value have been used to quantify the practical pH measuring ranges of the pH optodes described herein
(Table 4.28). Estimation of best fitting curves on the curves of RGB light intensity values versus pH values for slope calculation was performed with Table Curve Windows Software.
Table 4.28 The pH measuring ranges obtained from Red, Green, and Blue analytical signals of the proposed optodes. Signal0
Indicator SC
CR
TY
NB
VB
Red 5.42-7.83 1.64-4.33 10.88-12.47 9.68-12.25 7.76-14 Green 7.35-12.20 0-4.55 10.78-12.80 3.03-9.94 9.14-12.07 13.32-14 Blue
6.75-12.83
0-2.48
10.61-12.36 3.51-11.14
9.29-12
4.4.4.1 Imaging Results in comparison with Spectroscopic Results The pH optosensing results are usually obtained from single wavelength (max) measurements which results in limited measuring range (typically 2-4 units, only), while pH imaging results are obtained from three bands (Red, Green, and Blue regions). This type of measurements not only yields wider measuring range but it also offers other measuring ranges which could not be obtained from spectrophotometric measurement at a single wavelength (Table 4.28 and Fig. 4.23).
VB NB TY CR SC
0
2
4
6
8
10
12
14
pH Fig. 4.23 The pH measuring range obtained for Imaging (red, green, and blue light intensities) and Spectroscopic (absorbance at maxima) results. The black lines represent spectroscopic measuring range. The red, green, and blue components have been shown in their respective colors.
4.4.5 Conclusion The goal of the present work was to test the idea that a simple imaging system using input from an optode array is capable of recognizing and identifying pH in full range. The experimental devices are easy to use and common, thanks to which all acquisition and calculation procedures
can
be
automated.
These
results
encourage
the
construction of an imaging system, which will allow sensitive and precise remote detection of wide pH range or multi analyte analysis without any external signal carrier such as wire and optical fiber.
Appendix A A Matlab Function for Evaluation of the Proposed Mathematical Model The project function calculates absorbance, using the proposed mathematical model. function Project() x = 0 : 0.0005 : 0.02; %41 point y = x; res = eye (11,13); %results matrix disp('Please wait ...'); loop_n = 10000; T = 5e-3; pHin = 11; pKind = 7.5; len_op = 0.02; e1 = 4.8e2; e2 = 1.3e4; r=0; for pHfn = 0 : 10, r = r + 1; diffusion = 3.39e-10 * 10^(0.27 * pHfn); c = 0; for ftime = 0 : 5 : 60, c = c + 1; for i = 1 : 41, loopsec = 0; for m = 0 : loop_n, j = 2 * i + 1;k = pi * j / (2 * len_op); l = 1 / j * exp (-diffusion * ftime * (k^2)) * cos(k*x); loopsec = loopsec + l; end e2sec = 10^(-pHfn) + (4 / pi * ((10^(-pHin)) - (10^(-pHfn))) * loopsec); e1sec = 10^(-pKind); y(i) = ((e1 * e1sec) + (e2 * e2sec)) / (e1sec + e2sec) * T; end res (r, c) = trapz (x, y); temp = res (r, c); ans = [pHfn, ftime, temp]; disp(ans);
save d : \ yval.txt res ; end end
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