78
Anal. Chem. 1988, 60, 76-01
(9) Khummongkd, D.; Canterford. C. S.; Fryer, C. Blotechnol. Bioeng. 1984,24. 2643. (10) NE-, A.; HcrlkosM, T.; Sakaguchi, T. J . Appl. Mlcrobol. Blotechnd. 1982, 18, 88. (11) Les, A.; Walker, R. W. Water, Ah, SdlPollut. 1984,23. 129. (12) Orwns, B.: Henzl, M. T.; Hosea, M.; Darnall, D. W. Biotechnol. Bloeng. 1988, 28,764. (13) Damall, D. W.; Oreene, 8.; Hmzl, M. T.; Hosea. M.; McPherson, R. A,; sneddon, J.; ~ l e mD.~~ , ~ sei. Techno/. ~ h1988. ~20, 206. ~ (14) Greene, 8.; Hosea, M.; McPherson, R.; Henzl, M.; Alexander, M. D.; Darnall, D. W. Environ. Sci. Techno/. 1986,20,627. (15) Hosea, M.; Grwne, B.; McPherson, R.; Henzl, M.; Alexander, M. D.; Damn. D. w. I W ~ . chim.ACB 1986, 123, 161. (16) Darnail, D. W.; Orwne, B.; Hosea, M.; McPherson, R. A.; Henzl, M.; Alexander, M. D. I n Trace &tal Removal from Aqueous Solution; Thompson, R., Ed.; Special Publication No. 61, Royal Society of Chemfstry: London, 1986;p 1.
(17) Watkins, J. W., 11; Elder, R. C.; Greene, 8.; Darnall, D. W. Inorg. Chem. 1907, 26. 1147. (18) Kobos, R. K. Trends Anal. Chem. 1983, 2 , 154. (19) O'Riordan, D. M. T.; Wallace, G. G. Anal. Chem. 1986, 5 8 , 128. (20) Wang, J.; Frelha, 8. A. Anal. Chem. 1983, 55, 1285.
.
RECEIVED for review June 1,1987. Accepted September 18, 1987. This work was supported by the National Institutes of Health (Grant No. GM30913-04) and the National Science Foundation (Grant No. CBT-8610461). J.G.T. acknowledges the financial support of the Mexican Council of Science and (CoNACYT) and thanks B. Greene for helpful discussions.
Simplex Optimization of a Fiber--Optic Ammonia Sensor Based on Multiple Indicators Timothy D. Rhines and Mark A. Arnold* Department of Chemistry, University of Iowa, Iowa City, loova 52242
An optlmlzatlonstrategy for a flberoptlc ammonia sensor is presented. Thk rtrategy condderr the v e r b experlmcHltai parameters that affect both steadystate and dynamic rerpol#rr ch8ractwktlCr. The wo ot multiple lncHcators to enhance the deadpatate perfomance of the sensor Is demonstrated. A simplex OptknlzaUOn routine has been developed to identify the best comblnatlon of two lndlcators for a particular analyte commtrat!on range. OptMZatlon strategy k WhstrateU by developing a smsor for wastewater analysls. I n addftbn, respOme characterlstlcs of the resufnng fiberoptic sensor are dlrectly canpared to those of the conventional potentlometrlc ammonla electrode. The fiber-optic msor k W a M e for wastewater analyses, and it compares wen wRh the conventional ekctrode system. In comparleon to the electrode, the fiberoptlc sensor possesms equivalent sensltivlty, similar response times, and superlor recovery times.
-
Several different approaches toward the development of fiber-optic ammonia sensors have been presented (1-4). David, Willson, and Ruffin (1) were the first to report a fiber-optic ammonia sensor. Their system involved a quartz rod coated with a polymer/ninhydrin reagent, and ammonia vapor was detected colorimetrically. Jarvis and co-workers (2) reported a reversible fiber-optic sensor for vaporous ammonia Their sensor was baaed on a thin solid film of oxazine perchlorate dye on a glass capillary. Wolfbeis and Posch (3) have recently described a fluorometric-based fiber-optic sensor for aqueous ammonia determinations. In their system, a fluorescent pH indicator was trapped in a polymeric membrane which was held a t the tip of a bifurcated fiber-optic bundle. Diffusion of ammonia from solution into this membrane was detected and the resulting fluorescence signal was related to the sample ammonia concentration. We have introduced an ammonia-gas sensor based on the entrapment of an indicator solution behind a gas-permeable membrane (4).This indicator solution is composed of ammonium chloride and a suitable chromophoricor fluorophoric pH indicator. Ammonia diffuses across this membrane from 0003-2700/88/0360-0076$01.50/0
the sample into the indicator solution. This ammonia influx continues until the ammonia partial pressure is equal on both sides of the membrane and a steady-state ammonia concentration is established in the indicator solution. The pH of the indicator solution varies according to this steady-state ammonia Concentration. By selection of pH indicators with appropriate acid dissociation constants, the change in pH can be monitored optically. A set of optical fibers is positioned in the indicator solution to measure changes in the relative concentrations of the protonated and nonprotonated forms of the indicator. An equation has been developed which relates absorbance by the nonprotonated form of the indicator to the sample ammonia concentration (4). It must be recognized that a sensor based on the titration of a single indicator will be limited with respect to dynamic range of response. Response of a single pH indicator will be restricted to a narrow pH range which corresponds to a narrow dynamic range of detection for ammonia. This limitation is particularly evident in comparison to potentiometric-based sensors which typically provide a dynamic range of several orders of magnitude (5). This problem is not limited to the fiber-optic ammonia sensor described here but is a general problem with all fiber-optic sensors that are based on a single indicator. Although it is neither practical nor necessary to develop fiber-optic gas sensors that respond over several orders of magnitude, it is desirable to develop sensors which respond over the entire concentration range of interest for a particular analysis. One approach is to use several pH indicators in the indicator solution. By selection of indicators with complementing acid dissociation constants and by use of appropriate relative concentrations of these indicators, sensors with the required dynamic response range can be developed. The primary goal of our investigation has been to develop a practical optimization strategy for our fiber-optic ammonia sensor. Such a strategy is presented here where sensor response is optimized over a particular concentration range of interest. In this paper, we demonstrate for the first time the use of multiple indicators for the development of a fiber-optic ammonia-gas sensor. Application of this sensor for the determination of ammonia in wastewater samples is used to 0 1987 Amerlcan Chemlcai Society
ANALYTICAL CHEMISTRY, VOL. 60, NO. a
illustrate our approach. Optimal response for the fiber-optic sensor is obtained by using a simplex optimization routine to identify the best combination of two indicators for this particular analysis. The steady-state response of the sensor is optimized with respect to the relative amounts and the acid dissociation constants of the two indicators. In addition, response characteristics of the resulting fiber-optic ammonia sensor are directly compared to those of the conventional potentiometric ammonia sensor.
1, JANUARY 1, 1988
77
c
EXPERIMENTAL SECTION Apparatus and Reagents. Optical measurements were made with the following equipment from Oriel Corp., Stratford, C T 100-W quartz-halogen lamp (Model 6333), illuminator housing (Model 77504),fiber-opticinput assembly (Model 77800), constant voltage transformer (Model 6393), detector housing (Model 77760), collimating beam probe assembly (Model 77652), photomultiplier tube detector (Model 77761), fiber-optic input mount (Model 77802), and a photometer (Model 7070). Wavelength selection was made with two modified Beckman DU monochromators. These units were modified to accommodate fiber-opticinput and output. Plastic optical fibers (type EK-20 Eska Extra from Mitsubishi Rayon America, Inc., New York) were used for sensor construction. These plastic fibers had an outer diameter of 0.5 mm, a core diameter of 0.48 mm, and a numerical aperature of 0.47 0.03. Teflon membranes were purchased from Gore and Associates, Elkton, MD. These membranes were microporous in nature with an average pore size of 0.02 pm. Computations and plots of simulated response curves were performed with an IBM System 9OOO computer in conjunction with an IBM XY/749 digital plotter. The simplex optimization routine was written in Microsoft QuickBASIC, and the corresponding computations were carried out with a Leading Edge Model D personal computer. Nonlinear regression was performed with a SIMFIT routine that was provided by D. R. Henry, Molecular Design Corp., Haywood, CA. All solutions were prepared with distilled-deionizedwater that was purified immediately before use with a Milli-Q three house water purification unit. Chlorophenol red was obtained from Sigma Chemical Co., St. Louis, MO, and bromothymol blue was purchased from Fisher Scientific, Itasca, IL. Other reagents were analytical grade quality and were purchased from common suppliers. Procedures. Figure 1 shows a schematic diagram of the fiber-optic ammonia-gas sensor. This sensor was prepared by inserting three plastic optical fibers through a plastic pipet tip. The fibers were held in place by an adhesive sealant, and the end of the pipet tip was cut to an approximate diameter of 5 mm. A plastic spacer ring was wedged over the end of the pipet tip and glued in place. After the sensor was inverted, approximately 8 p L of the indicator solution was placed in the cavity formed by the spacer ring. Unless otherwise noted, the indicator solution was composed of 0.04 M ammonium chloride,32 pM chlorophenol red, and 21 pM bromothymol blue. A gas-permeablemembrane made of Teflon was placed over the tip of the sensor and held in place with an O-ring. The space between the ends of the optical fibers and the membrane was approximately 0.5 mm. The optical arrangement used was similar to that described previously (4). Radiation from the source was focused onto the common end of a bifurcated glass fiber-opticbundle (Oriel Model 77533). Each arm of the bifurcated bundle was connected to the input port of a monochromator. One monochromator was set at 578 nm (the wavelength of maximum absorbance for the nonprotonated form of chlorophenol red) and the other was set at 618 nm (the correspondingwavelength for the nonprotonatedform of bromothymol blue). Slit widths of 0.3 mm were used for both monochromators which resulted in band-passes of approximately 10 nm for each wavelength. These two incident beams were directed to the indicator solution at the sensor tip with two of the plastic fibers. The third fiber was connected to a photomultiplier tube (PMT) detector (Oriel Model 77761) which was operated at 500 V. Sensor calibration was performed by placing the sensor in a 10-mL aliquot of a pH and ionic strength control solution. This solution was composed of 0.01 M sodium hydroxide and 0.03 M
t 4 Figure 1. Schematic of fiber-optic ammonia gas sensor: a, input optlcal Rber 1,578 nm; b, input optical flber 2,618 nm; c, output optical fiber connected to PMT; d, plastic pipet tip; e, adhesive sealant; f, plastic spacer ring; g, O-ring; h, internal indicator solution; and i, microporous Teflon membrane. sodium chloride. Constant temperature was maintained with a glass-jacketed cell in conjunction with a water bath. Response curves were generated by making multiple microliter additions of an ammonium chloride standard. Absorbance values were calculated as -log (Z/lo)where Io and I were the incident and sample radiation intensities, respectively. The incident radiation intensity was measured as the light intensity with no ammonia in the sample solution. Wastewater samples were analyzed by adding 1 mL of the sample to 10 mL of the pH and ionic strength control solution. The resulting steady-state intensity was recorded, and the corresponding absorbance value was calculated. Ammonia concentrations were determined from a previously prepared calibration curve.
RESULTS AND DISCUSSION Sensor Response Function. The following response function has been derived to describe the steady-state response characteristics of the fiber-optic ammonia-gas sensor (4):
A =
eb[Inl(Ka)1n["31 Wa)-Cemm
- Wa)emm[aJH,I + (Ka)1n["31
(1)
In this equation, A is the measured absorbance, e is the molar absorptivity of the non-protonated form of the indicator, b is the effective optical path length of the sensor, [In] is the concentration of the indicator in the indicator solution, (Ka),,, and (Ka)mmare the acid dissociation constants for the indicator and ammonia, respectively, C ,, is the total ammonium ion concentration in the indicator solution, and [NH,]is the sample ammonia concentration. This response function is appropriate when a single indicator is employed, and the nonprotonated form of this indicator is monitored colorimetrically (4). Equation 1identifies the experimental parameters to consider when designing a fiber-optic ammonia sensor. These parameters involve aspects of the sensor body geometry, the analyte, and the indicator. The effective optical path length is governed by the geometry of the sensor body. This path length can be approximated as twice the distance from the ends of the optical fibers to the gas-permeable membrane (see Figure 1). Incident radiation exits the fiber, travels through the indicator solution,
78
ANALYTICAL CHEMISTRY, VOL. 60, NO. 1, JANUARY 1, 1988 5.5 6.0 0.24
6.5
0.1E
7.0
W 0
f
m n! 0
2 0.1;
v)
7.5
8.0
* O e 0O . 0 0 0.00
0.60
1.20
1.80
AMMONIA CONCENTRATION
2.40
3.00
(M X IO?
Figure 2. Effect of indicator pK, on the simulated response. The soli curves are generated by eq 1 with the following parameters: b = 0.1 cm, [In] = 0.1 mM, t = 30000 cm-' M-', (K,),,, = 5.62 X lo-'', and C ,, = 40 mM.
scatters off the membrane, travels again through the indicator solution, and enters the second fiber. This effective path length is related to the sensitivity of the sensor response. Longer path lengths correspond to larger signals and greater sensitivities. A compromise is required, however, because longer path lengths require larger volumes of the indicator solution which increase the response time of the sensor. We have found reasonable responses with path lengths between 0.5 and 1.0 mm. The acid dissociation constant of the analyte is not a parameter that can be varied. The concentration of the protonated form of the analyte in the indicator solution, however, must be considered. The response function in eq 1has been derived with the assumption that the ammonium concentration in the indicator solution remains constant at all sample ammonia concentrations. This latter condition is maintained in practice by adding a high concentration of ammonium chloride to the indicator solution. The most important parameter to consider when designing a fiber-optic ammonia sensor for a particular application is the acid dissociation constant of the indicator. Figure 2 shows a family of simulated response curves for a range of acid dissociation constants. The ammonia concentration range used for these simulated curves covers the anticipated values of interest in wastewater samples. Low pK, values result in an initial sharp rise in absorbance with a subsequent leveling off. High pK, values, on the other hand, produce curves with a steady, continuous response over the entire ammonia concentration range. The magnitude of the overall absorbance change is much greater with lower pKa values. Intermediate acid dissociation constants gradually go from one extreme to the other. Molar absorptivity and concentration of the indicator must also be considered. For a given indicator acid dissociation constant, the indicator with the largest molar absorptivity provides the greatest sensitivity. As will be discussed in detail below, a compromise is required with respect to indicator concentration. Although high indicator concentrations give large responses, they also require longer times to establish the steady-state condition (4). Two other parameters that strongly affect the sensor response, but which are not identified in eq 1, are temperature and osmolarity. An increase in temperature decreases the acid dissociation constant of the indicators and increases the ammonium ion dissociation constant. These changes result in a smaller sensor response. On the other hand, higher temperatures facilitate diffusion processes which result in faster sensor response times. A temperature of 30 O C results in
responses with reasonable steady-state and dynamic response properties. As with potentiometric ammonia sensors (6),the osmolarity of the sample solution must match that of the indicator solution. Under conditions of lower osmolarity in the sample solution, water vapor crosses the microporous membrane into the indicator solution. Besides dilution of the indicator solution, this condition causes the indicator solution to swell and the membrane to bulge out from the sensor tip. The change in distance from the tip of the optical fibers to the membrane surface corresponds to a change in optical path length. During the swelling process, a slow, but continuous, drift in the sensor response is observed. This problem is conveniently avoided by matching the osmolarities of the sample and indicator solutions. Optimal Response Curve. The optimal response curve must be some combination of the large absorbance changes for small pK, values and the continual response of high pK, values. A response curve with large absorbance changes over the entire concentration range of interest is desired. By combination of indicators with appropriate acid dissociation constants and by consideration of the relative concentrations of these indicators, sensors with such an optimal response curve can be developed. Numerous data treatment strategies can be developed for multiple indicator type sensors. One approach is to measure absorbance ratios. Such ratio measurements are inherently stable because the effects of various operational parameters, such as path length, are effectively cancelled. Equation 1can be used to derive a general response function which relates the absorption ratio to the sample ammonia concentration. A complex function with little analytical value is the result. An unusable function is obtained even for the simplest case of only two indicators. Alternatively, the sum of individual absorbances can be related to the sample ammonia concentration. Equation 1 can be expanded for multiple indicators by summing the individual absorbance contributions from each indicator. The general form of the response function is as follows: n i=l
n
where Abd is the total absorbance of the indicator solution, A , is the absorbance contribution from indicator i, n is the total number of indicators, m is the total number of individual wavelengths monitored, q, is the molar absorptivity of indicator i at wavelength j , and (Ka)Iniis the acid dissociation constant for indicator i. In the present example, two indicators and two wavelengths are employed ( n = m = 2). Unlike the absorbance ratio method, a straightforward function that closely resembles the original function for a single indicator is obtained. This general function is simple even when more than two indicators are employed. Total absorbance can be obtained in two ways. First, individual absorbances can be measured by simultaneously monitoring the intensity of the appropriate wavelength for each indicator. In this case, the summation is accomplished computationally. Second, the total absorbance of the indicator solution can be measured directly by selecting appropriate incident radiation wavelengths for each indicator and by monitoring the intensity of the total returned light. In this second case, the returning light contains information from all the preselected wavelengths. These two strategies provide the same information. In the first case, wavelength selection is made after interaction with the internal solution and, in
ANALYTICAL CHEMISTRY, VOL. 60, NO. 1, JANUARY 1, 1988
the second case, wavelength selection is made before the internal solution. We have selected the second approach because fewer PMT detectors are required. In either case, it is critical that the protonated form of each indicator does not absorb any of the monitored radiation. A simplex optimization routine has been developed to find the best combination of acid dissociation constants and relative indicator concentrations for a given ammonia concentration range. A three-dimensional simplex is required to optimize the three variables involved (K, for indicators 1and 2, and relative concentration of these indicators). Before a simplex optimization strategy can be applied to this problem, criteria to evaluate the resulting response curves must be established. We have considered two possible evaluation schemes. In the first case, the curve with the greatest sensitivity over the entire ammonia concentration range is judged to be optimal. The magnitude of the sensitivity over the entire concentration range has been evaluated in the following manner. The concentration range is divided into 500 individual ammonia concentration values. The first derivative of the response function with respect to sample ammonia concentration (dA/d[NH,]) is used to provide the sensitivity at each of these ammonia concentration values. The overall sensitivity for a given set of parameters is directly compared to that of the rival sets of parameters. This comparison is made at each of the 500 ammonia concentrations. For a particular ammonia concentration value, the set of parameters that provides the highest sensitivity is assigned a score of 3, the set with the next highest gets a score of 2, the next receives 1point, and the set with the lowest sensitivity gets a score of 0. The total score for a set of parameters is obtained by summing the individual scores at each ammonia Concentration. The set with the highest total score is considered the best. In the second case, the set of parameters that gives a constant sensitivity throughout the entire concentration range is considered optimal. Here again, the first derivative of the corresponding response function is calculated at the 500 ammonia concentrations. For a set of parameters, the sensitivities for the entire concentration range are pooled. The variance of this pool is calculated, and the response curve with the lowest variance is considered the best. Conventional modified simplex rules (7,8) have been used to proceed through the optimization process. A set of boundary conditions must be established to keep the values associated with the simplex practical: a lower limit of 4 X lo4 has been set for the dissociation constants, and the concentration fraction of each indicator must be between zero and one. The following values have been used during the optimization: molar absorptivity, 30 000; path length, 1.0 mm; (Ka)- = 5.62 X 10-lo;and C ,, = 0.04 M. In addition, a total indicator concentration of 100 pM has been maintained. The sensor response has been optimized over an ammonia concentration range from 10 to 300 pM which is the range of interest for our wastewater analysis. To minimize the possibility of a local minimum being taken as optimal, 16 unique sets of parameters were arbitrarily established. Four groups of four settings were used to start the simplex routine four individual times. The resulting "optimal" set of parameters from each of these four groups was used as the starting point in a simplex routine from which the final optimal conditions were obtained. Figure 3 shows the optimal simulated response curves based on the two evaluation schemes. As expected, by minimization of the variation in sensitivity, a linear response is generated. Unfortunately, the overall magnitude of this response is small. On the other hand, by maximizing the sensitivity over the entire concentration range, a response curve is produced that
79
0.24
"'"1v / 0.00 0.00
0.60
1.20
1.80
2.40
3.00
AMMONIA CONCENTRATION ( M X 104 Figure 3. Comparison of the results of the two simplex optimization routines: S, maximum sensitivity; V, minimum variance.
provides a large change in signal over the entire concentration range. Although nonlinear regression methods are required for data analysis in this second case, the maximum sensitivity evaluation scheme is considered optimal. For our example, an indicator solution composed of 57% of an indicator with a pK, of 6.3 and 43 % of a second indicator with a pK, of 6.9 is optimal. To verify that two indicators are better than one, the simplex optimization routine has been modified to identify the best acid dissociation constant for a single indicator (i.e., relative concentration of indicator 2 is set at zero). An indicator with a pKa of 6.5 is optimal for the single indicator case. Maximum sensitivity scores for the dual and single indicator conditions are 379 and 121, respectively. On the basis of the higher sensitivity over the entire concentration range, the dual system is superior. A combination of chlorophenol red and bromothymol blue closely matches the optimal conditions. In a solution of 0.04 M ammonium chloride, the pKa values for these indicators are 6.1 f 0.1 and 7.1 f 0.1, respectively. Molar absorptivities for chlorophenolred are 51OOO f 500 cm-l M-' at 578 nm and 2497 f 20 cm-' M-' at 618 nm. Values for bromothymol blue at these wavelengths are 25 178 f 200 cm-l M-l and 37064 f 436 cm-' M-l, respectively. The fraction of each indicator must be adjusted to account for the differences between the actual molar absorptivities of the indicators and those used in the simplex optimization routine. This adjustment can be made by equating the following terms for indicator i:
(3) The product of the sum of the actual molar absorptivities at the monitored wavelengths and the actual indicator fraction ( X i ) is equal to the product of the molar absorptivity used in the optimization routine and the optimized fraction ( X , ) of the indicator. This treatment yields the actual indicator fractions that are required to match the optimized response curve when the actual molar absorptivities are used. For the example with chlorophenol red and bromothymol blue, the actual fractions are 0.604 and 0.396, respectively. The total indicator concentrationis the final parameter that must be considered. This parameter affects both the magnitude of the response and the time of response. Figures 4 and 5 show the effect of total indicator concentration on the steady-state (Figure 4) and dynamic (Figure 5) response characteristics of the sensor. These curves have been obtained from a sensor in which the optimized fractions of chlorophenol red and bromothymol blue are employed. Solid lines in Figure 4 are the simulated response curves generated from eq 2 for the corresponding conditions. An increase in the total indi-
80
ANALYTICAL CHEMISTRY, VOL. 60, NO. 1, JANUARY 1, 1988
Y
total indicator concn, p M
total absorbance
total response time, min
absorbance/ time ratio"
13 27 53 102 265
0.071 0.195 0.281 0.338 0.555
32 35 36 54 78
2.22 5.57 7.81 6.26 7.12
" (Absorbance/minute
X
0.24
1000).
Table 11. Comparison of Response and Recovery Times"
ammonia concn, pM
fiber-optic sensor, min
potentiometric sensor, min
29 85 141 197 278 recovery
14.8 f 2.3 10.2 f 1.3 7.9 i 1.6 6.3 f 1.1 6.1 f 1.2 26.3 f 3.2
11.6 f 2.5 6.9 f 1.6 4.7 f 0.8 3.4 f 0.9 2.9 f 0.7 47.4 f 3.4
0.00 0.00
1.20
0.60
1.80
2.40
3.00
AMMONIA CONCENTRATION ( M X I O ? Figure 8. Comparison between the optimum response curve (-), the simulated response curve for the actual internal indicator solution (- - -), and a calibration curve using the internal indicator solution (0).
" Values are the average of five determinations. cator concentration results in larger steady-state and slower dynamic responses. Clearly, a compromise is required. Table I tabulates the total absorbance change and the total time of response for the various sensor response curves at the specified total indicator concentrations. In addition, the ratio of total signal to total time for these curves is listed. A suitable indicator concentration provides the combination of a maximum ratio and a practical total time. A total indicator concentration of 53 p M provides the most signal per unit time with a reasonable total time of response. This concentration
has been used in subsequent studies. The optimal response curve generated from the simplex optimization routine, the simulated response curve with the actual indicator solution, and data from the resulting fiberoptic ammonia sensor are presented together in Figure 6. Excellent correlation between these curves is observed. Ammonia calibration curves are obtained by fitting sensor response data to the response function in eq 2 with a nonlinear regression routine. The best fit is attained by adjusting the effective optical path length. Comparison to Potentiometric Ammonia Sensor. Steady-state and dynamic characteristics of the fiber-optic ammonia sensor have been compared to those of the conventional potentiometric sensor. Relative sensitivity (9) has been used to compare the steady-state properties, and sensor
Table 111. Analysis of Wastewater Samples"
sample 1 2 3 4 5 6
av
CRWPCL 1118 94 1 824 824 588 588
X
f SDb
1277 f 30 853 f 12 829 f 64 729 f 6 594 f 30 811 f 18
fiber-optic aensor % errorc
% RSD
14.2 -9.4 0.7 -11.4 2.0 38
2.1 1.7 7.5 4.5 2.5
12.6
3.2
1.1
Potentiometric Sensor X i SDb % errorc % RSD 1200 f 941 f 806 f 882 f 524 f 794 f
18 18 18 18 12 18
7.4 -2.1 7.1 -10.1 35
1.6 1.9 2.1 1.8 2.5 2.2
10.3
2.0
"Micromolar concentrations; samples were diluted by a factor of 11. bAverage f standard deviation of three measurements. cPercent relative error with respect to value from CRWPCL.
ANALYTICAL CHEMISTRY, VOL. 60, NO. 1, JANUARY 1, 1988
response and recovery times have been used to compare the dynamic properties. Relative sensitivity is given by the following equation (9): relative sensitivity = b/(sopt/spt)
(4)
where b is the slope of the correlation curve between the optical and potentiometric sensors and soptand spotcorrespond to the pooled standard deviations for the two sensors. Pooled standard deviations have been obtained by determining ammonia concentrations in six individual standards where the concentrations span the entire concentration range of interest (10-300 pM). All measurements have been performed in triplicate with each sensor. The pooled standard deviation of d 1 8 measurements reflects the repeatability of the sensors. Values for the optical and potentiometric sensors are 4.01 (fO.O1) pM and 4.62 (f0.04) pM, respectively. The smaller standard deviation indicates that the optical system is slightly more repeatable. The correlation curve for the two sets of measurements gives a line with a slope of 0.97 (f0.06) and a correlation coefficient of 0.992. The resulting relative sensitivity between these two sensors is 1.1(hO.1)which indicates that these sensors possess equivalent sensitivity for ammonia determination over this concentration range. The sensitivity of the optical sensor might be improved by using more indicators in the internal indicator solution to produce more sensitive response, particularly at high ammonia concentrations. Table I1 summarizes the dynamic response properties of the optical and potentiometric ammonia sensors. Response times for the optical sensor range from 6 to 15 min, whereas response times for the potentiometric sensor are quicker and range from 3 to 12 min. A comparison of recovery times, however, reveals that the optical system is superior. Only 26 min is required to reestablish the base line condition between measurements with the optical sensor, as compared to 48 min for the potentiometric system. Long recovery times for the potentiometric sensor have been recognized as a major problem (10, 11). The significant improvement in the recovery process for the optical system can be attributed to the lack of a bulk volume of the internal solution. Wastewater Analysis. The optical-fiber ammonia sensor has been used to determine the ammonia concentration in various wastewater samples. Samples have been obtained from the Cedar Rapids Water Pollution Control Laboratory (CRWPCL) at different points along the treatment process. These samples provide a suitable range of ammonia concen-
81
trations. A potentiometric ammonia sensor has also been used to analyze the same water samples. Results from both sensors have been compared to those of the CRWPCL where a standard Kjeldahl procedure has been used. Table I11 lists the resulk of the wastewater analysis. Results from the optical and potentiometric sensors correlate well with each other, and there is generally good agreement between these results and those from the Kjeldahl method. An exception is sample 6 for which the relative errors of the two sensors are high. The value reported by the CRWPCL in this case must be questioned because of the excellent agreement between the optical and potentiometric sensors. It should be noted that there is no need to remove particulate material from the sample when the optical sensor is used. The gas-permeable membrane of this sensor prohibits entry of any particulate material into the indicator solution; hence, sample turbidity does not interfere with the analysis. The wastewater samples used in this study had various degrees of turbidity and no correlation between turbidity and degree of error could be identified. Overall, the developed simplex optimization routine is a powerful tool that allows for a quick and simple optimization of the indicator solution for any particular application. Resulting sensors, based on multiple indicators, compare favorably with the conventional potentiometric gas-sensing electrode. Direct comparison of these sensors illustrates that the fiber-optic ammonia sensor possesses better repeatability, equivalent sensitivity, slightly longer response times, and superior recovery times. Finally, the fiber-optic sensor is suitable for wastewater analysis.
LITERATURE CITED (1) David, D. J.; Willson, M. C.; Ruffin, R. S. Anal. Lett. 1976, 9(4), 389-404. (2) Giulianl, J. F.; Wohltjen, H.; Jarvis, N. L. Opt. Len. 1963. 8(1), 54-56. (3) Wolfbeis, 0.S.; Posch, H. E. Anal. Chlm. Acta 1986, 185, 321-327. (4) Arnold, M. A.; Ostler, T. J. Anal. Chem. 1986, 58, 1137-1140. (5) Covington, A. K. lon-Selective Nectrw'e Methodology; CRC: B o a Raton, FL, 1979; Vol. I , Chapter 5. (6) Arnold, M. A.; Rechnltr. G. A. Anal. Chim. Acta 1964, 158, 209-214. (7) Derning, S. N.; Morgan, S.L. Anal. Chem. 1973, 45, 278A. (8) Nelder, J. A.; Mead, R. Comput. J . 1965. 7 , 308. (9) Mandel, John Treatise on Analytical Chemistry, 2nd ed.; Wiley: New York. 1978; Part 1, Vol. 1, Chapter 5. (IO) Arnold, M. A. Anal. Chlm. Acta 1883, 154, 33-39. (11) Coilison, M. E.;Arnold, M. A. Anal. Lett. 1986, 19, 1759-1776.
RECEIWDfor review June 25,1987. Accepted September 14, 1987. This work was supported by the National Institutes of Health (GM-35487).
