Several soptimisation parameters were studied to develop the sensor for instant As3+ detection. Initially, several electrolytes were tested using an electrochemical process to obtain the desired analytical signal for As3+ ions sensing. Further, electrochemical processes such as CV were performed for potential -0.2 to 1.2 V with a scan rate of 100 mV/Sec, and SWASV soptimisation studies were performed under the varying square wave parameters: deposition potential (−0.1 to 1V), deposition time (30–300s) and frequency (50–350 Hz).
3.1. Cyclic Voltammetry Analysis
Cyclic Voltammetry was used to study redox processes to understand reaction intermediates. Redox peaks in the CV indicate the involvement of electron transfer reactions. The peak potentials reveal the thermodynamic favorability of the redox reaction, while the peak currents reflect the reaction kinetics and concentration of the redox species. From the cyclic voltammetry response, refer to Figure 1 it was observed that there is no peak recorded for the blank solution of electrolyte while after adding the arsenic ions, the signal peak was observed at soxidised potential, i.e. 1.078V.
3.2. Square Wave Anodic Stripping Voltammetry Analysis
SWV excels at detecting analytes at low concentrations, making it ideal for trace analysis in environmental applications. Therefore, to achieve the sensitivity and lower detection limit, square wave voltammetry was studied, and we tuned the pre-concentration and stripping parameters to record the maximum current signal peak for targeted ions.
3.2.1 Redox reactions and current measurement
a). Preconcentration Step: A negative potential is applied to the working electrode (-0.3V) for a specific preconcentration time. This negative potential favours the reduction of the As3+ ions in the solution onto the electrode surface. The reduced arsenic atoms accumulate on the electrode, building up its concentration and effectively amplifying the subsequent stripping signal. This step effectively enhances the detection limit of the technique. In the preconcentration step, at a particular deposition potential i.e. -0.3V, arsenic ions (As^3) present in the solution get reduced at the Working Electrode, which means the transfer of arsenic ions from the solution to the WE electrode surface.
b). Stripping Step: After preconcentration in the reverse pulse, the previously reduced arsenic species can re-soxidise back to their original state, releasing electrons and generating an oxidation current peak. In this step, a positive potential scan is applied to the electrode, and the scan gradually reverses the reduction process applied earlier. The reduced As0 ions re-soxidise and re-dissolve back into the solution. This process generates a sharp current peak in the voltammogram.
c). Peak analysis: The current peak's height and potential are directly proportional to the concentration of arsenic ions present in the solution.
A simple acid-base reaction occurred in SWV where the protons from nitric acid combine with the lone pair electrons on the oxygen atoms of the arsenite ion, and the arsenite ion loses electrons (gets oxidized_As3+) to the nitrate ions in nitric acid, which gain electrons (get reduced) to form nitrite ions (NO2⁻).
The peak's height is proportional to the concentration of the arsenic ions in the solution, making the square wave highly sensitive. To obtain the desired sensor performance, the Square-wave Voltammogram was recorded after optimising the SWV parameters (Table 2).
3.2.2 Optimisation of SWV Parameter- Reduction potential
Deposition potential is the vital parameter affecting the targeted analytes’ sensitivity and selectivity (29) . Increased deposition potential can lead to higher sensitivity because a higher potential drives the deposition reaction more strongly, resulting in a thicker or more densely packed deposit. However, there is a limit to this effect, as a potential that is too high can lead to unwanted side reactions or damage to the sensor. Decreased deposition potential can lead to lower sensitivity because a lower potential drives the deposition reaction less strongly, resulting in a thinner or less densely packed deposit. In the experiment, we studied the behaviour of deposition potential -0.1 to -1V, (Figure 2), and the highest signal peak was obtained at - 0.3V, so finalised.
3.2.3 Optimisation of SWV Parameter- Deposition Time
Deposition time affects the sensitivity of the analyte to be detected, so sufficient time must be spent adsorption of targeted species to the electrode’s surface.
As the deposition time increases, more analyte ions can migrate to the electrode and be reduced/oxidized (depending on the experiment). This leads to more accumulated analyte on the electrode surface, resulting in a progressively higher current signal during the stripping stage (30). This is typically observed as a linear increase in peak current with increasing deposition time. Eventually, the analyte occupies all available active sites on the electrode surface. Further increasing the deposition time won't significantly increase the current signal, as no more analyte can be accumulated. This is the saturation point, and the current may reach a plateau or even start to decrease slightly. To optimise deposition time for arsenic ions, we examined 30 to 300 sec, and 60sec was observed as sufficient time (Figure 3) for the arsenic ions to adsorb on the electrode surface (31) .
3.2.4 Electrochemical Effect of Scan Rate on the As3+ Signals
The scan rate in square wave voltammetry (SWV) significantly impacts the analyte's current signal, influencing both its magnitude and signal/noise ratio. As the scan rate increases, the peak current of the analyte generally increases because faster potential switching leads to a steeper concentration gradient near the electrode surface, enhancing the diffusion flux of the analyte and boosting the faradaic current. Also, increasing the scan rate can improve the signal-to-noise ratio (SNR) because the background current (primarily arising from double-layer charging) increases linearly with the scan rate, while the faradaic current increases proportionally to the square root of the scan rate. Therefore, the faradaic signal becomes more prominent at higher scan rates relative to the background noise.
The current signal peak is proportional to the square root of the scan rate according to the Randles–Ševčík equation (a), (32) .
2.69 × 105 n1.5 AC √Dv………………. (a)
- ip = current
- n = number of electrons transferred
- A = electrode area in cm2
- D = diffusion coefficient in cm2/s
- C = concentration in mol/cm3
- ν = scan rate in V/s
In this work, we have recorded the current signal (Figure 4) at different scan rates (50 to 350mV/sec), and the highest current peak was reached at a scan rate of 200 mV/Sec; therefore, 200 mV/Sec was selected as the optimal scan rate, ensuring the fastest sensor response time and the least limit of quantitation (LoQ).
3.2.5 Supporting Electrolytes and the effect of pH
The type and pH of the supporting electrolyte greatly influence arsenic's behaviour during square-wave anodic stripping voltammetry. As a result, selecting an electrolyte takes careful consideration. Figure 5 shows how different electrolytes affect the measurement of Arsenic. HNO3 at 1.08 V was found to have the highest detection sensitivity, followed by potassium chloride (pH 2), acetate buffer (pH 4.5), and hydrochloric acid (pH 1). Hydrochloric acid did not produce a sensitivity or an arsenic peak. (25) Chlorine ions from the hydrochloric acid electrolyte solution may be adsorbed on the Gold and cover the electrode surface due to the electrode's wide surface area, which eventually limits the number of active sites for arsenic adsorption. (33) Mohammad et al., (2010) mentioned in their research that Cl1- may serve as an ionic bridge to help reduce Arsenic by forming complex Arsenic to create AsCl3, leading to quicker electron kinetics. Meanwhile, the origin of this arsenic peak couldn't be identified and was subject to wide variation (34) . In 0.1 M KCl, a broad peak was observed, and no detectable arsenic signals were found, so this investigation did not consider arsenic detection. Since the development of the extra peaks in the 0.1M acetate buffer was not fully understood, it was not considered for arsenic detection in this investigation. The low sensitivity in detection under high pH conditions may be due to the valency change, hydrolysis of Arsenic, and presence of other interfering ions of electrolytes, which may interact with the electrode surface and make the Arsenic interact with the electrode at its ultralow concentration quite tricky (25) (35) . 0.1 M nitric acid solution produced results that were quite sensitive. So, from the analysis, it was observed that the supporting electrolyte nitric acid plays an exciting role in increasing the arsenic solution's conductivity, providing optimum sensitivity and sharp stripping peak current toward As3+ compared with other supporting electrolytes (20) . Therefore, pH 1 was chosen as the optimum pH for all subsequent experiments.
3.2.6 Interference Study
The selectivity toward As3+ was verified by measuring multicomponent potential competing for anions (Fluoride, phosphate, and Bromide, respectively). In the potential range of -0.5 to 1V, little stripping peak current signal could be observed for the Anions mentioned above, even though they are at high concentrations (100ppb) compared with that of 50ppb As3+ refer to Figure 6. Therefore, the proposed sensor is selective, i.e., it does not influence the potential interfering ions.
3.3 Voltammogram and Linearity
The ASV response in terms of voltage versus current plot (Voltammogram) was obtained (Figure 7) for a varying arsenic concentration (5-50 ppb). An anodic current peak arises at a 1.078 V stripping window (oxidising potential), which is uniquely obtained through the above-stated optimisations (Table 2) and provides the sensor specificity for the targeted analyte. The Calibration curve for Arsenic ions sensing is a plot between concentration and current values (Figure 8). The Calibration curve's slope value is 2.3512, indicating the higher sensitivity, and the regression coefficient R2 is 0.9987, proving strong linearity between the current value and arsenic concentration.
Table 2. Optimised Parameters for Arsenic Quantification
Optimised parameters for the As3+ Sensing
|
Deposition Potential
|
-0.3V
|
Deposition Time
|
60 sec
|
Quiet Time
|
20 sec
|
Amplitude
|
25mV
|
Step Size
|
4mV
|
Frequency
|
50Hz
|
Scan Rate
|
200mV/sec
|
Stripping Potential
|
1.078V
|
3.4 Detection and Quantitation Limit
The detection limit is a critical parameter in analytical contexts, representing the smallest quantity of a substance that can be reliably distinguished from its absence. In the context of detecting genetically modified grains, LOD is defined in terms of the probability of detection (POD), which is influenced by sampling uncertainty. The LOD & LOQ values were calculated from the linear curve (36) using equations (1,2) are given below:
The repeatability test is measured by the relative standard deviation (RSD) of multiple measurements (standard trial experiments in triplicates) made under the same conditions. The values for detection limits were calculated using equations (1) and (2), given in Table 3.
Table 3. Detection and Quantification limit for As3+ ions
Analyte
|
Sensitivity (µA/ppb)
|
R2
|
SD
|
LoD (ppb)
|
LoQ (ppb)
|
As3+
|
2.3512
|
0.9987
|
0.258
|
0.33
|
1.09
|
4.2 Statistical Modelling and Chemometric Analysis
In this study, the data analysis was done using the software “The Unscramble (version 10.4)” to explore the trends in the varying Arsenic Samples of concentration range (5 to 50 ppb) and extract optimised data relevant to variables, including oxidising potential, scan rate, deposition potential and deposition time, which are associated/responsible for maximum peak current values without noise or disturbance, and provided linear sensing trend (37) .
3.5.1 Principal Component Analysis
Principal Component Analysis, or multivariate technique, analyses a data set in which observations/behaviours are described through several inter-correlated quantitative dependent variables (38) . Using PCA, large oxidising potential scan data is extracted into smaller data sets, which becomes easier to explore and visualise, making analysing data much more manageable. After applying data reduction, it was found that PC1 and PC2 carry 99% and 1% of the data variance, respectively, i.e. PC1 indicated maximum variance in the dataset. From the weighted regression coefficient (BW) analysis (Figure 9, BW-Plot), it was seen that at 1.078 V stripping potential value (close to PC1 as seen in Figure 9, correlation loading plot) suggests that at this particular oxidising potential value (refer to Figure 9, correlation loading plot), linear behaviour of Arsenic samples distributed based on the current values as seen in Scores plot (Figure 9).
3.5.2 Optimisation of Prediction Model Parameters/Design Variables
In this analysis, different parameters were studied, including deposition potential, deposition time, and scan rate affecting the current response for Arsenic samples. The data analysis found (refer to Figure 11, BW-Box Plot) that -0.3V deposition potential, 60 sec & 300sec deposition time, the scan rate of 75mV/s & 200mV/s shows maximum weighted regression coefficients (BW), indicating that these are the most relevant features/design variables that can be used to predict the Arsenic concentration. A similar analysis was confirmed by the Correlation loading plot and Scores plot (Figure 10). The design/optimised variables (Dp, Dt, SR) are used in the correlation loading plot to develop a regression model that lies on the highest explained variance eclipse and is closest to the PC1 axis. The closeness to the PC1 axis indicates the least angle with the origin and the direct proportionality to the concentration of the samples, i.e., increasing from left to right direction. Also, the external eclipse variables (DP3, DT2, and SR4) inform 100 % of the variance explained in the information in the model.
3.5.3 PLSR Regression Model
Based on the optimised design variables using weighted regression coefficient (BW), a regression model was developed to predict the Arsenic concentration with RMSE (cal. & val.) and R-square (cal. & val.) values 0.596 &1.10 and 0.998 & 0.996, respectively (Figure 12). The slope value obtained for the regression plot is 0.998&0.984 (cal. & val.). In Table 5, the fitness measures (R-square, RMSE, RMSEP and Slope) indicate robustness and reliability to predict (39) . The reliability of the prediction was tested using the full cross-validation method. The model was fitted using Factor 1 only.
Table 4. Prediction Model Fitness Measures
Analyte
|
Values
|
Slope
|
Offset
|
RMSE
|
R2
|
RMSEP
|
As3+
|
Calibrated
|
0.998
|
0.036
|
0.596
|
0.998
|
0.596
|
Validated
|
0.984
|
0.243
|
1.103
|
0.996
|
3.5.4 Predicted with Deviation Interpretation
A prediction vs deviation interpretation can be made (Figure 13) to know the model's reliability and how well it can predict the unknown samples whose reference measurements are unknown. In Figure 13, the horizontal red line shows an expected response for the As3+ samples. The blue box around the predicted value (that spans the deviation in both directions) estimates the prediction uncertainty. From the X-variable values for the samples, deviations from the calibration samples can be seen in Figure 14 and Table 6. Also, Figure 13 shows the Predicted vs Reference plot for test set validation, where the Root Mean Square Error of Prediction (RMSEP) is a dispersion of the validation samples around the regression line.
Table 5. Prediction with Deviation Values
Reference (ppb)
|
Predicted (ppb)
|
Deviation
|
5
|
4.8201
|
0.7144
|
10
|
9.9785
|
0.8137
|
20
|
19.7297
|
0.5499
|
30
|
30.4928
|
0.7151
|
40
|
40.9350
|
0.9352
|
50
|
49.0440
|
1.1050
|