Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 156 (2016) 105–111
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Development of a novel pH sensor based upon Janus Green B immobilized on triacetyl cellulose membrane: Experimental design and optimization Narges Chamkouri a, Ali Niazi a,⁎, Vali Zare-Shahabadi b a b
Department of Chemistry, Faculty of Sciences, Islamic Azad University, Arak Branch, Arak, Iran Department of Chemistry, College of Chemical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
a r t i c l e
i n f o
Article history: Received 3 July 2015 Received in revised form 10 November 2015 Accepted 18 November 2015 Available online 1 December 2015 Keywords: pH optical sensor Janus Green B Triacetylcellulose membrane Box–Behnken design
a b s t r a c t A novel pH optical sensor was prepared by immobilizing an azo dye called Janus Green B on the triacetylcellulose membrane. Condition of the dye solution used in the immobilization step, including concentration of the dye, pH, and duration were considered and optimized using the Box–Behnken design. The proposed sensor showed good behavior and precision (RSD b 5%) in the pH range of 2.0–10.0. Advantages of this optical sensor include on-line applicability, no leakage, long-term stability (more than 6 months), fast response time (less than 1 min), high selectivity and sensitivity as well as good reversibility and reproducibility. © 2015 Published by Elsevier B.V.
1. Introduction Maintaining a proper pH value is important in many of the physical and chemical reactions that take place during manufacture processing and quality control [1]. Continuous pH control is essential as variation in pH can be affected on product quality and efficiency of chemical reactions. Among the devices used for pH monitoring, optical pH sensors have been actively investigated for their potential in practical uses such as food processing, environmental analysis, ecology, biotechnology and pharmaceutical industry [2–6]. The advantageous features of the optical sensors include easy preparation with low cost, long-term stability, and high sensitivity and selectivity. Based on these advantages, several optical sensors have been developed for the determination of different cations and anions [7–13]. Design of experiments (DOEs) has generally been applied for simultaneous optimization of effect of variables to improve characteristics' performance and/or minimize error with minimum number of experimental runs. There are different DOE methodologies such as central composite design (CCD), Box–Behnken design (BBD) and three-level factorial design which have different properties and characteristics [14]. A Box–Behnken design does not contain an embedded factorial or fractional factorial design, but it is a rotatable second-order design based on three-level incomplete factorial design that received a wide application [15–18]. Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. Response surfaces are used to determine an optimum. In ⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Niazi).
http://dx.doi.org/10.1016/j.saa.2015.11.016 1386-1425/© 2015 Published by Elsevier B.V.
addition, it is a good way to graphically illustrate the relation between different experimental variables and the response(s). The main purpose of the present article is to describe the preparation a new pH optical sensor with a wide pH range. Janus Green B (8-(4Dimethylaminophenyl)diazenyl-N,N-diethyl-10-phenylphenazin-10ium-2-amine chloride), as an inexpensive azo dye, is used as pH indicator. The dye is immobilized on triacetylcellulose membrane. Condition of the dye solution used in immobilization step, including concentration of the dye, pH, and duration were considered and optimized using the Box–Behnken design. Characteristics and behaviors of the produced sensors were examined by UV–visible spectrophotometer. Structure of Janus Green B is shown in Fig. 1. 2. Experimental 2.1. Instrumentation and software A double beam spectrophotometer (PG Instruments, model T80+, UK) was used. pH value measurements were performed by a pH meter (Metrohm, model 827, Switzerland) using a combined glass electrode. Before application, the pH meter was calibrated with standard buffers (pH = 4.0 and pH = 7.0). Absorbance data were obtained using a designed cell replaced in the cell holder of spectrophotometer instrument. The pH sensors were placed between the two plates of the cell. A Watson–Marlow 101F peristaltic pump at a flow rate of 7 mL min−1 was employed for pumping the solution through a simple flow cell in which the immobilized membrane was installed (Fig. 2). The STATISTICA 10 was used for experimental design, analysis and their subsequent regression analysis.
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Fig. 1. Chemical structure of Janus Green B.
2.2. Chemicals and reagents Janus Green B, hydrochloric acid, sodium hydroxide, and sodium dihydrogen phosphate were purchased from Merck (Darmstadt, Germany). Buffer solutions were prepared from sodium dihydrogen phosphate of 0.03 mol L−1. The pH of buffer solution was adjusted by adding appropriate amounts of 1.0 mol L−1 hydrochloric acid or sodium hydroxide solutions. Dye stock solution (0.1 mol L−1) was prepared by dissolving Janus Green B in methanol. Working solutions with different concentrations were prepared by appropriate dilution of the stock solution with deionized water. 2.3. Preparation of the sensor membrane Triacetylcellulose film was cut into pieces with suitable size and stored in water. The membranes were transferred to sodium hypochlorite 0.1 mol L− 1 for seconds in order to remove colored gelatinous layers. The membranes were washed with deionized water and detergent. Afterwards, they were transferred to sodium hydroxide solution 0.1 mol L−1 for 24 h for activation. The activated membranes were washed with deionized water and stored in water. The membranes were then transferred to a beaker typically containing 10 mL solution of 6 × 10−5 mol L−1 Janus Green B in sodium dihydrogen phosphate of 0.03 mol L−1. After 2 min, the membranes were removed and washed with deionized water to remove any unbounded dye. The sensors were stored in deionized water until the analysis time.
Fig. 3. Different pHs (1.0, 1.5, 2.0, 2.5, 3.0, 5.0, 7.0, 10.0 and 11.0) on the absorbance.
3.2. Optimization variables using one-at-a time method Four variables that might be affecting the sensor behavior were firstly examined by one-at-a-time procedure. Condition of the dye solution used in the immobilization step, including pH of solution, concentration of the dye, and ionic strength as well as duration of immobilization were considered and optimized. 3.3. Effect of pH of dye solution To study the effect of pH on immobilization of the dye, different sensors were constructed at various pHs. To do so, several dye solution at constant dye concentration but different pHs were prepared. The results are depicted in Fig. 3. As shown in Fig. 3, the absorbance of the constructed sensors was decreased as pH of dye solution increased. In the other words, as the pH of the dye solution increases the amounts of dye immobilized on the film is decreased. It should be noted that immobilization was stopped at higher pH values. The sensors built at pH lower than 2.0 did not show good stability.
3. Results and discussion
3.4. Effect of dye concentration
3.1. Optimization the experimental conditions
Different sensors were developed in the dye solutions with different concentrations, in the range of 1.0 × 10−5–9.0 × 10−5 mol L−1, to study the effect of dye concentration of the sensor's characteristics. It was found that increasing in the amount of the dye caused to increase absorbance of the sensor. On the other hand, the analytical signal increased by increasing the amount of the dye. Based upon the results shown in Table 1, the dye concentration of 6 × 10− 5 mol L− 1 was selected as the optimum value and used for further studies.
In order to optimize the experimental variables, two methods were employed. In the first stage, one-at-a time procedure was used for screening the several factors to determine significant factors that have a key role in the characteristics of the sensor. After screening out the factors with insignificant effect, the remaining factors were optimized using the Box–Behnken design.
Fig. 2. Set-up of measuring system.
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Table 1 Variation of the absorbance of optical sensor by different concentrations of Janus Green B solutions on the sensor membrane at 539 nm. pH
2.0 10.0
Concentration (mol L−1) 1 × 10−5
2 × 10−5
3 × 10−5
4 × 10−5
5 × 10−5
6 × 10−5
7 × 10−5
8 × 10−5
9 × 10−5
0.096 0.031
0.231 0.042
0.348 0.051
0.465 0.054
0.582 0.059
0.839 0.077
0.818 0.034
0.779 0.028
0.663 0.014
3.5. Duration of immobilization Based on the preliminary study, it was found that behavior of the sensor is affected by duration of immobilization. Effect of this time on the absorbance was studied at different pHs (2.0–5.0). The results are given in Fig. 4. The sensor membranes which were treated by 6 × 10−5 mol L−1 dye solution for 60 s showed the highest absorbance. Therefore, duration time of 60 s was selected as optimum.
number of replications [17–18]. Usually the central point of the design was replicated several times to estimate of experimental error. Considering cp equal to three, 15 experiments need for this design. The requested 15 experimental runs were generated and analyzed using STATISTICA 10. Absorbance recorded at 539 nm was considered as dependent variables. All variables in the design are listed in Table 3. The response variable (Y) and the tested variables were related by the following second-order polynomial equation (Eq. (1)). y ¼ 0:7991−0:0426x1 −0:0036x21 þ 0:0039x1 x2 −0:0072x1 x3 þ 0:0040x2 x3 n ¼ 15; R2 ¼ 0:989; RMSE ¼ 0:0045
3.6. Effect of ionic strength The effect of salt concentration on the sensor preparation in the immobilization step was examined at pH = 2.0. Sodium chloride was used to adjust the ionic strength. The results are shown in Table 2. The results indicated that the sensor response was approximately constant at different ionic strengths.
ð1Þ Analysis of variance (ANOVA) was applied to estimate the statistical significance of the model. The extent of fitting the experimental results
Table 3 Factors and their levels in Box–Behnken design and obtained result for each run.
3.7. Optimization of the variables using Box–Behnken design According to results obtained from the one-at-a time study, three variables, including pH (x1), dye concentration (x2), and duration of immobilization (x3) are responsible to change the membrane absorbance. A 3-factor, 3-level Box–Behnken design was established for further optimization. For each variable, three levels were assigned and coded by (−1, 0, +1). The Box–Behnken design requires an experiment number according to N = k2 + k + cp, where (k) is the factor number and (cp) is
Factors
(x1) pH (x2) C (mol L−1) (x3) Duration (s)
Level Low
Center
High
−1
0
1
2.0 5.5 × 10−5 50
2.5 6 × 10−5 60
3.0 6.5 × 10−5 70
Run
x1
x2
x3
Absorbance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 −1 0 −1 −1 0 0 0 1 1 −1 0 0 1 0
1 0 −1 1 −1 −1 1 0 −1 0 0 0 0 0 1
0 1 1 0 0 −1 1 0 0 1 −1 0 0 −1 −1
0.742 1.153 0.828 1.044 0.877 0.864 1.029 0.504 0.955 1.276 1.113 0.528 0.54 0.827 0.566
Fig. 4. Three-dimensional plot of the duration of immobilization of Janus Green B at different pHs (2.0–5.0). The dye concentration was 6 × 10−5 mol L−1 in the solution.
Table 2 Effect of salt concentration (mol L−1) on the absorbance of optical sensor at pH 2.0. pH = 2.0
Salt concentration (mol L−1) NaCl
0 0.1 0.2 0.3 0.4 0.5
0.832 0.834 0.833 0.836 0.835 0.831
Fig. 5. The actual data versus predicted data for absorbance of Janus Green B.
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Table 4 Analysis of variance (ANOVA) of the proposed model. Source
SS
DF
MSS
F value
p-Value
Total Regression Residual
0.0271 0.0268 3.004 × 10−4
14 5 9
0.0054 3.337 × 10−5
160.44
1.63 × 10−8
SS = sum of squares; DF = degrees of freedom; MSS = mean sum of squares; F-value = F statistical value; p-Value = probability value.
to the polynomial model equation was expressed by the squared correlation coefficient R2. The higher adjusted R2 values indicate a good correlation and relationship between the experimental data and the obtained model. F-test was used to estimate the statistical significance of all terms in the polynomial equation within 95% confidence interval [14–15]. The resulted model had R2 and F-value of 0.99 and
160, respectively. Root mean squared error (RMSE) of the model is 0.0045 which shows good quality of the model. The plot of experimental versus predicted absorbance values are shown in Fig. 5. The results indicated that the model applied to the data was significant and adequate to represent the relationship between the response and the independent variables. The summery of the ANOVA is shown in Table 4. The main and interaction effects of variables can be estimated from the outputs of experimental design which is the main reason of doing experimental design. Based on the resulted model (Eq. (1)), it was found that dye concentration had no significant effect on the absorbance of the sensor. This observation is not surprising and indeed it is corresponding to the one-at-a-time results. The choosing levels of dye concentration are in the range of 5.5 × 10−5–6.5 × 10− 5 mol L−1. Based on the results of one-at-a time, the change in absorbance of the sensor due to the change of dye concentration in this range was low.
Fig. 6. Response surfaces plot for the Box–Behnken design: (a) (x1) pH–(x2) concentration (C mol L−1); (b) (x1) pH–(x3) duration of immobilization (s); (c) (x2) concentration (C mol L−1)–(x3) duration of immobilization (s).
N. Chamkouri et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 156 (2016) 105–111
The pH of dye solution had significant effect on the absorbance. The appearance of quadratic terms of pH in the Eq. (1) indicates that the absorbance decreases sharply as pH values increases. Response surface curves facilitate investigating the interaction between the independent variables and finding the optimal level for each variable, as well. These curves are represented in Fig. 6(a)–(c). Inspecting these figures show that amounts of interactions between variables are little as the extension of curvature of the plots in not very high. In spite of this observation, the interaction terms appeared significantly in the model equation.
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3.9. Spectra of the sensor's membrane Fig. 8(a)–(b) shows the absorbance spectra of the dye in aqueous solution and its immobilized form at different pHs (2.0–10.0). Higher and lower pH values did not study because stability of the sensor diminished dramatically at these pHs. In order for pH monitoring, wavelength of 539 nm was selected due to its higher sensitivity. As pH of the solution is increased the absorbance of the dye in both forms decreased at 539 nm. It is clear from Fig. 8 that the absorbance changes for dye immobilized on the membrane are greater than that dissolved in aqueous solution.
3.8. Optimization of Box–Behnken design by desirability function (DF) 4. Analytical features After generation of the polynomial equations that relate the absorbance to the independent variables, the desirability function (DF) in STATISTICA 10 software was used for optimization of the process (Fig. 7). The profile for desirable responses was chosen after specifying the DF for each dependent variable by assigning predicted values. Profiling the desirability of responses involves specifying the DF for each dependent variable, by assigning predicted values a scale ranging from 0.0 (undesirable) to 1.0 (very desirable). According to these values, desirability function settings for each dependent variable on absorbance are depicted at the right hand side of Fig. 7. Desirability of 1.0 was assigned for maximum response (0.8750), 0.0 for minimum (0.7280) and 0.5 for middle (0.80150). The overall response obtained from these plots with the current level of each variable in the model is depicted at the top (left) of Fig. 7. On the basis of these calculations and desirability score of 1.0, maximum absorbance (0.9920) was obtained at optimum values of the tested variables found to be as follows: pH (X1 = 2.06), dye concentration (X2 = 5.8 × 10− 5 mol L− 1), and duration of immobilization (X3 = 63 s).
4.1. Response time One of the important analytical features of any sensing phase is its response time. The response time is defined as the time required for 95% of the total signal change [19]. The continuous response curves at wavelength 539 nm were recorded every second when the injected buffers were alternated between pH 2.0, 8.0 and 10.0. The result showed that the sensor absorption reached a constant value in less than 1 min. Therefore, 1 min was selected as the response time of this optical sensor. 4.2. Reproducibility and long-term stability of sensor membrane Reproducibility and reversibility are the important characteristics of a sensor. In order to monitor the reproducibility and reversibility of the sensor membranes, buffer solutions with pHs of 2.0, 7.0 and 9.0 were subsequently pumped through the flow cell and absorbance was recorded at 539 nm. Fig. 9 shows the corresponding results. The relative standard deviations (Reds) of replicate samples were less than 5%.
Fig. 7. Profiles for predicated values and desirability function for Janus Green B. Dashed lines indicate current values after optimization.
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Fig. 8. Absorption spectra of Janus Green B in aqueous solution (a) and immobilized on triacetylcellulose membrane (b) at different pHs (2.0–10.0). The dye concentration was 6 × 10−5 mol L−1 in the solution.
In order to study the long-term stability of the sensor, the proposed sensor membranes were maintained in the deionized water for 6 more than months. No significant variation in the sensor response was observed during this period. Moreover, no leakage of the reagent and no drift in signal were detected.
4.3. Calibration curve
Fig. 9. Variation of the absorbance of the membrane at 539 nm by changing different pH values.
Fig. 10. Calibration curve of the optical sensor at 539 nm. The error bars are expressed as percentage of relative standard deviation (RSD %).
Linearity is the ability of the proposed method to provide results that are directly proportional to analyte concentration within a given range. The linearity of the proposed optical sensor was determined using
N. Chamkouri et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 156 (2016) 105–111
buffer solutions with different pHs in the range of 2.0 to 10.0. Calibration curve was linear over the studied range with the equation of y = − 0.0809x + 1.1547. The calculated correlation coefficient for this curve was found to be R = 0.9981 (Fig. 10). It shows that triacetylcellulose membrane has a wide dynamic range with straight line fitted to the experimental data. 5. Conclusion As a conclusion, we proposed an applicable pH opcode based on the immobilization of Janus Green B indicator on triacetylcellulose membrane. The influences of experimental parameters on the optical sensor were investigated by the Box–Behnken experimental design. The calibration curve of this sensor was linear in the pH range of 2.0–10.0. The proposed optical sensor had several advantages including, on line applicability, cheap and easy preparation, long-term stability, fast response time, high selectivity and sensitivity, good reversibility and reproducibility (RSD b 5%). Acknowledgments
References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
The authors are gratefully acknowledging the support of this work by the Islamic Azad University, Arak Branch.
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Contents lists available at ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Development of a novel pH sensor based upon Janus Green B immobilized on triacetyl cellulose membrane: Experimental design and optimization Narges Chamkouri a, Ali Niazi a,⁎, Vali Zare-Shahabadi b a b
Department of Chemistry, Faculty of Sciences, Islamic Azad University, Arak Branch, Arak, Iran Department of Chemistry, College of Chemical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
a r t i c l e
i n f o
Article history: Received 3 July 2015 Received in revised form 10 November 2015 Accepted 18 November 2015 Available online 1 December 2015 Keywords: pH optical sensor Janus Green B Triacetylcellulose membrane Box–Behnken design
a b s t r a c t A novel pH optical sensor was prepared by immobilizing an azo dye called Janus Green B on the triacetylcellulose membrane. Condition of the dye solution used in the immobilization step, including concentration of the dye, pH, and duration were considered and optimized using the Box–Behnken design. The proposed sensor showed good behavior and precision (RSD b 5%) in the pH range of 2.0–10.0. Advantages of this optical sensor include on-line applicability, no leakage, long-term stability (more than 6 months), fast response time (less than 1 min), high selectivity and sensitivity as well as good reversibility and reproducibility. © 2015 Published by Elsevier B.V.
1. Introduction Maintaining a proper pH value is important in many of the physical and chemical reactions that take place during manufacture processing and quality control [1]. Continuous pH control is essential as variation in pH can be affected on product quality and efficiency of chemical reactions. Among the devices used for pH monitoring, optical pH sensors have been actively investigated for their potential in practical uses such as food processing, environmental analysis, ecology, biotechnology and pharmaceutical industry [2–6]. The advantageous features of the optical sensors include easy preparation with low cost, long-term stability, and high sensitivity and selectivity. Based on these advantages, several optical sensors have been developed for the determination of different cations and anions [7–13]. Design of experiments (DOEs) has generally been applied for simultaneous optimization of effect of variables to improve characteristics' performance and/or minimize error with minimum number of experimental runs. There are different DOE methodologies such as central composite design (CCD), Box–Behnken design (BBD) and three-level factorial design which have different properties and characteristics [14]. A Box–Behnken design does not contain an embedded factorial or fractional factorial design, but it is a rotatable second-order design based on three-level incomplete factorial design that received a wide application [15–18]. Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. Response surfaces are used to determine an optimum. In ⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Niazi).
http://dx.doi.org/10.1016/j.saa.2015.11.016 1386-1425/© 2015 Published by Elsevier B.V.
addition, it is a good way to graphically illustrate the relation between different experimental variables and the response(s). The main purpose of the present article is to describe the preparation a new pH optical sensor with a wide pH range. Janus Green B (8-(4Dimethylaminophenyl)diazenyl-N,N-diethyl-10-phenylphenazin-10ium-2-amine chloride), as an inexpensive azo dye, is used as pH indicator. The dye is immobilized on triacetylcellulose membrane. Condition of the dye solution used in immobilization step, including concentration of the dye, pH, and duration were considered and optimized using the Box–Behnken design. Characteristics and behaviors of the produced sensors were examined by UV–visible spectrophotometer. Structure of Janus Green B is shown in Fig. 1. 2. Experimental 2.1. Instrumentation and software A double beam spectrophotometer (PG Instruments, model T80+, UK) was used. pH value measurements were performed by a pH meter (Metrohm, model 827, Switzerland) using a combined glass electrode. Before application, the pH meter was calibrated with standard buffers (pH = 4.0 and pH = 7.0). Absorbance data were obtained using a designed cell replaced in the cell holder of spectrophotometer instrument. The pH sensors were placed between the two plates of the cell. A Watson–Marlow 101F peristaltic pump at a flow rate of 7 mL min−1 was employed for pumping the solution through a simple flow cell in which the immobilized membrane was installed (Fig. 2). The STATISTICA 10 was used for experimental design, analysis and their subsequent regression analysis.
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Fig. 1. Chemical structure of Janus Green B.
2.2. Chemicals and reagents Janus Green B, hydrochloric acid, sodium hydroxide, and sodium dihydrogen phosphate were purchased from Merck (Darmstadt, Germany). Buffer solutions were prepared from sodium dihydrogen phosphate of 0.03 mol L−1. The pH of buffer solution was adjusted by adding appropriate amounts of 1.0 mol L−1 hydrochloric acid or sodium hydroxide solutions. Dye stock solution (0.1 mol L−1) was prepared by dissolving Janus Green B in methanol. Working solutions with different concentrations were prepared by appropriate dilution of the stock solution with deionized water. 2.3. Preparation of the sensor membrane Triacetylcellulose film was cut into pieces with suitable size and stored in water. The membranes were transferred to sodium hypochlorite 0.1 mol L− 1 for seconds in order to remove colored gelatinous layers. The membranes were washed with deionized water and detergent. Afterwards, they were transferred to sodium hydroxide solution 0.1 mol L−1 for 24 h for activation. The activated membranes were washed with deionized water and stored in water. The membranes were then transferred to a beaker typically containing 10 mL solution of 6 × 10−5 mol L−1 Janus Green B in sodium dihydrogen phosphate of 0.03 mol L−1. After 2 min, the membranes were removed and washed with deionized water to remove any unbounded dye. The sensors were stored in deionized water until the analysis time.
Fig. 3. Different pHs (1.0, 1.5, 2.0, 2.5, 3.0, 5.0, 7.0, 10.0 and 11.0) on the absorbance.
3.2. Optimization variables using one-at-a time method Four variables that might be affecting the sensor behavior were firstly examined by one-at-a-time procedure. Condition of the dye solution used in the immobilization step, including pH of solution, concentration of the dye, and ionic strength as well as duration of immobilization were considered and optimized. 3.3. Effect of pH of dye solution To study the effect of pH on immobilization of the dye, different sensors were constructed at various pHs. To do so, several dye solution at constant dye concentration but different pHs were prepared. The results are depicted in Fig. 3. As shown in Fig. 3, the absorbance of the constructed sensors was decreased as pH of dye solution increased. In the other words, as the pH of the dye solution increases the amounts of dye immobilized on the film is decreased. It should be noted that immobilization was stopped at higher pH values. The sensors built at pH lower than 2.0 did not show good stability.
3. Results and discussion
3.4. Effect of dye concentration
3.1. Optimization the experimental conditions
Different sensors were developed in the dye solutions with different concentrations, in the range of 1.0 × 10−5–9.0 × 10−5 mol L−1, to study the effect of dye concentration of the sensor's characteristics. It was found that increasing in the amount of the dye caused to increase absorbance of the sensor. On the other hand, the analytical signal increased by increasing the amount of the dye. Based upon the results shown in Table 1, the dye concentration of 6 × 10− 5 mol L− 1 was selected as the optimum value and used for further studies.
In order to optimize the experimental variables, two methods were employed. In the first stage, one-at-a time procedure was used for screening the several factors to determine significant factors that have a key role in the characteristics of the sensor. After screening out the factors with insignificant effect, the remaining factors were optimized using the Box–Behnken design.
Fig. 2. Set-up of measuring system.
N. Chamkouri et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 156 (2016) 105–111
107
Table 1 Variation of the absorbance of optical sensor by different concentrations of Janus Green B solutions on the sensor membrane at 539 nm. pH
2.0 10.0
Concentration (mol L−1) 1 × 10−5
2 × 10−5
3 × 10−5
4 × 10−5
5 × 10−5
6 × 10−5
7 × 10−5
8 × 10−5
9 × 10−5
0.096 0.031
0.231 0.042
0.348 0.051
0.465 0.054
0.582 0.059
0.839 0.077
0.818 0.034
0.779 0.028
0.663 0.014
3.5. Duration of immobilization Based on the preliminary study, it was found that behavior of the sensor is affected by duration of immobilization. Effect of this time on the absorbance was studied at different pHs (2.0–5.0). The results are given in Fig. 4. The sensor membranes which were treated by 6 × 10−5 mol L−1 dye solution for 60 s showed the highest absorbance. Therefore, duration time of 60 s was selected as optimum.
number of replications [17–18]. Usually the central point of the design was replicated several times to estimate of experimental error. Considering cp equal to three, 15 experiments need for this design. The requested 15 experimental runs were generated and analyzed using STATISTICA 10. Absorbance recorded at 539 nm was considered as dependent variables. All variables in the design are listed in Table 3. The response variable (Y) and the tested variables were related by the following second-order polynomial equation (Eq. (1)). y ¼ 0:7991−0:0426x1 −0:0036x21 þ 0:0039x1 x2 −0:0072x1 x3 þ 0:0040x2 x3 n ¼ 15; R2 ¼ 0:989; RMSE ¼ 0:0045
3.6. Effect of ionic strength The effect of salt concentration on the sensor preparation in the immobilization step was examined at pH = 2.0. Sodium chloride was used to adjust the ionic strength. The results are shown in Table 2. The results indicated that the sensor response was approximately constant at different ionic strengths.
ð1Þ Analysis of variance (ANOVA) was applied to estimate the statistical significance of the model. The extent of fitting the experimental results
Table 3 Factors and their levels in Box–Behnken design and obtained result for each run.
3.7. Optimization of the variables using Box–Behnken design According to results obtained from the one-at-a time study, three variables, including pH (x1), dye concentration (x2), and duration of immobilization (x3) are responsible to change the membrane absorbance. A 3-factor, 3-level Box–Behnken design was established for further optimization. For each variable, three levels were assigned and coded by (−1, 0, +1). The Box–Behnken design requires an experiment number according to N = k2 + k + cp, where (k) is the factor number and (cp) is
Factors
(x1) pH (x2) C (mol L−1) (x3) Duration (s)
Level Low
Center
High
−1
0
1
2.0 5.5 × 10−5 50
2.5 6 × 10−5 60
3.0 6.5 × 10−5 70
Run
x1
x2
x3
Absorbance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 −1 0 −1 −1 0 0 0 1 1 −1 0 0 1 0
1 0 −1 1 −1 −1 1 0 −1 0 0 0 0 0 1
0 1 1 0 0 −1 1 0 0 1 −1 0 0 −1 −1
0.742 1.153 0.828 1.044 0.877 0.864 1.029 0.504 0.955 1.276 1.113 0.528 0.54 0.827 0.566
Fig. 4. Three-dimensional plot of the duration of immobilization of Janus Green B at different pHs (2.0–5.0). The dye concentration was 6 × 10−5 mol L−1 in the solution.
Table 2 Effect of salt concentration (mol L−1) on the absorbance of optical sensor at pH 2.0. pH = 2.0
Salt concentration (mol L−1) NaCl
0 0.1 0.2 0.3 0.4 0.5
0.832 0.834 0.833 0.836 0.835 0.831
Fig. 5. The actual data versus predicted data for absorbance of Janus Green B.
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Table 4 Analysis of variance (ANOVA) of the proposed model. Source
SS
DF
MSS
F value
p-Value
Total Regression Residual
0.0271 0.0268 3.004 × 10−4
14 5 9
0.0054 3.337 × 10−5
160.44
1.63 × 10−8
SS = sum of squares; DF = degrees of freedom; MSS = mean sum of squares; F-value = F statistical value; p-Value = probability value.
to the polynomial model equation was expressed by the squared correlation coefficient R2. The higher adjusted R2 values indicate a good correlation and relationship between the experimental data and the obtained model. F-test was used to estimate the statistical significance of all terms in the polynomial equation within 95% confidence interval [14–15]. The resulted model had R2 and F-value of 0.99 and
160, respectively. Root mean squared error (RMSE) of the model is 0.0045 which shows good quality of the model. The plot of experimental versus predicted absorbance values are shown in Fig. 5. The results indicated that the model applied to the data was significant and adequate to represent the relationship between the response and the independent variables. The summery of the ANOVA is shown in Table 4. The main and interaction effects of variables can be estimated from the outputs of experimental design which is the main reason of doing experimental design. Based on the resulted model (Eq. (1)), it was found that dye concentration had no significant effect on the absorbance of the sensor. This observation is not surprising and indeed it is corresponding to the one-at-a-time results. The choosing levels of dye concentration are in the range of 5.5 × 10−5–6.5 × 10− 5 mol L−1. Based on the results of one-at-a time, the change in absorbance of the sensor due to the change of dye concentration in this range was low.
Fig. 6. Response surfaces plot for the Box–Behnken design: (a) (x1) pH–(x2) concentration (C mol L−1); (b) (x1) pH–(x3) duration of immobilization (s); (c) (x2) concentration (C mol L−1)–(x3) duration of immobilization (s).
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The pH of dye solution had significant effect on the absorbance. The appearance of quadratic terms of pH in the Eq. (1) indicates that the absorbance decreases sharply as pH values increases. Response surface curves facilitate investigating the interaction between the independent variables and finding the optimal level for each variable, as well. These curves are represented in Fig. 6(a)–(c). Inspecting these figures show that amounts of interactions between variables are little as the extension of curvature of the plots in not very high. In spite of this observation, the interaction terms appeared significantly in the model equation.
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3.9. Spectra of the sensor's membrane Fig. 8(a)–(b) shows the absorbance spectra of the dye in aqueous solution and its immobilized form at different pHs (2.0–10.0). Higher and lower pH values did not study because stability of the sensor diminished dramatically at these pHs. In order for pH monitoring, wavelength of 539 nm was selected due to its higher sensitivity. As pH of the solution is increased the absorbance of the dye in both forms decreased at 539 nm. It is clear from Fig. 8 that the absorbance changes for dye immobilized on the membrane are greater than that dissolved in aqueous solution.
3.8. Optimization of Box–Behnken design by desirability function (DF) 4. Analytical features After generation of the polynomial equations that relate the absorbance to the independent variables, the desirability function (DF) in STATISTICA 10 software was used for optimization of the process (Fig. 7). The profile for desirable responses was chosen after specifying the DF for each dependent variable by assigning predicted values. Profiling the desirability of responses involves specifying the DF for each dependent variable, by assigning predicted values a scale ranging from 0.0 (undesirable) to 1.0 (very desirable). According to these values, desirability function settings for each dependent variable on absorbance are depicted at the right hand side of Fig. 7. Desirability of 1.0 was assigned for maximum response (0.8750), 0.0 for minimum (0.7280) and 0.5 for middle (0.80150). The overall response obtained from these plots with the current level of each variable in the model is depicted at the top (left) of Fig. 7. On the basis of these calculations and desirability score of 1.0, maximum absorbance (0.9920) was obtained at optimum values of the tested variables found to be as follows: pH (X1 = 2.06), dye concentration (X2 = 5.8 × 10− 5 mol L− 1), and duration of immobilization (X3 = 63 s).
4.1. Response time One of the important analytical features of any sensing phase is its response time. The response time is defined as the time required for 95% of the total signal change [19]. The continuous response curves at wavelength 539 nm were recorded every second when the injected buffers were alternated between pH 2.0, 8.0 and 10.0. The result showed that the sensor absorption reached a constant value in less than 1 min. Therefore, 1 min was selected as the response time of this optical sensor. 4.2. Reproducibility and long-term stability of sensor membrane Reproducibility and reversibility are the important characteristics of a sensor. In order to monitor the reproducibility and reversibility of the sensor membranes, buffer solutions with pHs of 2.0, 7.0 and 9.0 were subsequently pumped through the flow cell and absorbance was recorded at 539 nm. Fig. 9 shows the corresponding results. The relative standard deviations (Reds) of replicate samples were less than 5%.
Fig. 7. Profiles for predicated values and desirability function for Janus Green B. Dashed lines indicate current values after optimization.
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Fig. 8. Absorption spectra of Janus Green B in aqueous solution (a) and immobilized on triacetylcellulose membrane (b) at different pHs (2.0–10.0). The dye concentration was 6 × 10−5 mol L−1 in the solution.
In order to study the long-term stability of the sensor, the proposed sensor membranes were maintained in the deionized water for 6 more than months. No significant variation in the sensor response was observed during this period. Moreover, no leakage of the reagent and no drift in signal were detected.
4.3. Calibration curve
Fig. 9. Variation of the absorbance of the membrane at 539 nm by changing different pH values.
Fig. 10. Calibration curve of the optical sensor at 539 nm. The error bars are expressed as percentage of relative standard deviation (RSD %).
Linearity is the ability of the proposed method to provide results that are directly proportional to analyte concentration within a given range. The linearity of the proposed optical sensor was determined using
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buffer solutions with different pHs in the range of 2.0 to 10.0. Calibration curve was linear over the studied range with the equation of y = − 0.0809x + 1.1547. The calculated correlation coefficient for this curve was found to be R = 0.9981 (Fig. 10). It shows that triacetylcellulose membrane has a wide dynamic range with straight line fitted to the experimental data. 5. Conclusion As a conclusion, we proposed an applicable pH opcode based on the immobilization of Janus Green B indicator on triacetylcellulose membrane. The influences of experimental parameters on the optical sensor were investigated by the Box–Behnken experimental design. The calibration curve of this sensor was linear in the pH range of 2.0–10.0. The proposed optical sensor had several advantages including, on line applicability, cheap and easy preparation, long-term stability, fast response time, high selectivity and sensitivity, good reversibility and reproducibility (RSD b 5%). Acknowledgments
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The authors are gratefully acknowledging the support of this work by the Islamic Azad University, Arak Branch.
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