Introduction
Industrial advancement has played a vital role in improving human life while presenting environmental challenges. One such challenge faced by our societies and the sustainability of our planet is the ongoing release of toxic heavy metals and metalloids into the aquatic environment. The toxic metalloids have been infiltrating our environments through industrial discharge, mining activities, and domestic discharge into surrounding water bodies, thus polluting the aquatic ecosystem [1]. Through bioaccumulation and bio-amplification across food chains, these heavy metal species have significantly influenced biodiversity and life on Earth [2]. Chromium (Cr), present in the effluents from several industrial activities, such as chemical synthesis, chrome plating, leather tanning, production of dyes, metallurgy, mining, and manufacturing of various alloys, has become one of the most toxic heavy metals in our environments. Because of their high toxicity, chromium and its compounds, commonly utilized in industrial chemical processes, have become environmental contaminants of genuine concern [3]. A massive influx of chromium pollution in soil, groundwater, and air due to industrial activities has been associated with an elevated risk of carcinogenicity, mutagenicity, chromosomal breakage, DNA damage, and genotoxicity, which are responsible for a range of debilitating diseases [4]. The Cr(VI) contamination was observed in higher concentrations in Changhua County compared to Taichung City, resulting in 60 times more gastric cancer incidence in the former compared to Taichung City. Overexposure to Cr(VI) was observed in China, where upper stretches of rivers such as the Luo and Sandao exceeded Cr(VI) permissible levels, whereas lower and middle stretches did not [5]. Similar reports of chromium contamination of water supplies have been registered globally from USA [6], Australia, India [7], Bangladesh [8], Greece [9], Japan [10], and Iraq [11], and many resulting in successful lawsuits.
Chromium has two stable oxidation states viz. Cr(III) and Cr(VI), of which Cr(VI) is more soluble, mobile, and bioavailable in alkaline to slightly acidic conditions and, as a result, has a higher level of toxicity [12]. Cr(VI) is also regularly found in drinking and public water systems. In 1991, as a part of the Safe Water Drinking Act (SFDA), the United States Environmental Protection Agency placed chromium under the maximum contaminant level goal to have a maximum contaminant level of 100 ppb total chromium. However, even though it is recognized that Cr(VI) is significantly toxic while Cr(III) is not, the US EPA has not yet been able to establish MCL specifically for Cr(VI). However, according to research from the US National Toxicology Program, California could establish a state-wide drinking water standard of 10 ppb MCL for Cr(VI), under further review [13]. In addition, Japan and China also regulate Cr(VI). The chromium is detected by traditional laboratory tests using AAS [14] and ICP-MS [15]. For instance, for regulatory compliance, the total chromium in water is currently measured by ICP-MS using the US EPA Method 200.8 [16]. These tests are sensitive and reliable but require expensive equipment with a large footprint and trained technicians, making on-site detection challenging and delaying decision-making. Importantly, these assays do not allow specific detection of toxic Cr(VI). A rather complex anion-exchange chromatography-based US EPA Method 218.7 can be followed [6]. These limitations have led to an emerging interest in developing new methods for specific Cr(VI) detection in a point-of-care (POC). Notably, very few reported methods demonstrated the ability to detect Cr(VI) selectively. These methods have utilized various transducer/detection platforms, including fluorescence spectrometry [17] electrochemistry [18], and others. Among different transducers, colorimetric detection offers the key advantage of more practical deployment due to the ease of analyte detection by an unaided eye, as is evident from the success of COVID rapid antigen test (RAT) kits, and other standard POC devices such as pregnancy strips. Thus, colorimetric methods offer the opportunity to develop low-cost portable devices for rapid onsite monitoring of important target analytes, both qualitatively and semi-quantitatively. Chromium monitoring using the colorimetric method based on 1, 5-diphenylcarbazide dye is a well-known process [19], but over time it has faced many interference issues, and the better chromogenic substrate is well sought.
The conventional analysis of Cr(VI) in water requires highly skilled staff, time-consuming sample pre-treatment, and sophisticated equipment, including AAS, ICP-MS, UV-Vis spectrometry, surface-enhanced Raman scattering spectroscopy, fluorescence, electrochemistry, and X-ray Fluorescence, etc. which are more specific and sensitive towards minute concentrations of Cr(VI) [20,21]. But, detection through conventional systems is costly, requires skilled human resources for device and software handling, is time-consuming, and lacks field portability. These drawbacks of the conventional system have encouraged the development of inexpensive sensor-based rapid detection systems with high field portability and sensitivity. The precise detection of toxic metalloids using suitable sensing techniques may also flourish their separation and elimination techniques towards increasing the safety status of the usable water [22]. Moreover, the sensor development study may also be integrated with novel nanomaterials and quantum dots [23] to increase the stability and sensitivity of the developed sensor. Not only that, but such sensors can also be used in conventional adsorption studies towards developing novel adsorption beds in water treatment plants, as mentioned by Ismail et al. [24]. In the present study, a portable field device has been developed by exploiting TMB as a chromogenic substance. The first part of this endeavor depicts the choice of material through UV-Vis Spectroscopy. Furthermore, after establishing chemosensor selectivity and specificity towards Cr (VI), UIISScan1.1, an advanced image array technology-based field-portable high-throughput sensory system, was [25] exploited towards a simple, rapid and on-site analysis of Cr(VI). The tailored system has been upgraded and trained with an image-processing algorithm. A software named ‘Cr-Detector’ was developed for this purpose (Fig 1).
[Figure omitted. See PDF.]
Materials and methods
This section outlines experimental design details performed experiments and data analysis techniques. At first, suitable chromogenic compounds are selected based on the observable and measurable color change with different concentrations of Cr(VI). Next, different image analysis algorithms have been explored, and suitable color analysis techniques have been finalized for data collection. Furthermore, experimentations of different concentrations of Cr(VI) with suitable chromogen have been performed, and optimized reaction parameters. Next, repetitive experimentation with all optimized reaction parameters and selected concentrations of Cr(VI) have been executed towards cumulative data collection and standard curve generation. Different image parameters have been extracted from the images captured through the portable field device and evaluated towards selective output signal generation and finalization of the algorithm towards accurate sample analysis and validation. Further selectivity study of the developed sensor with different positive and negative ions has also been performed. Furthermore, recovery analysis and accurate sample analysis have also been executed.
Reagents
3,3′,5,5′-tetramethylbenzidine (TMB), o-Phenyldiamine (OPD), 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) hydrogen peroxide (H2O2, 30%), and DMSO, were obtained from Sigma-Aldrich. In addition, potassium dichromate (K2Cr2O7), Chromium chloride hexahydrate (CrCl3.6H2O), Zinc sulphate (ZnSO4), Lead acetate trihydrate (Pb(C2H3O2)2.3(H2O)), Sodium sulfate (Na2SO4), Potassium carbonate (K2CO3), Sodium hydrogen carbonate (NaHCO3), Barium nitrate (Ba(NO3)2), Cadmium nitrate tetrahydrate (Cd (NO3)2 · 4H2O), Mercury chloride (HgCl2) were purchased from HiMedia. Na2SO4 and ZnSO4 were used to obtain SO42- ions which were further used to check the specificity/cross-reactivity of the developed sensor intended to detect Cr(VI). In this article, water and other aqueous solutions with ultrapure water were purified with a Millipore system (18.2 MΩ·cm).
Instruments and apparatus
Previously, an uniform illumination imaging system (UIIS) integrated with a portable scanner was reported that, allows the detection of an analyte through Mahanalobis Distance calculations [25]. The UIIS system can capture images of 96 well plates in 600 dpi resolution and operate at a wide temperature range, making it effective for spot experimentations in fields. The present study used a similar UIIS system with further modification. For capturing an image of the reaction colour, a different image analysis technique was explored to develop the algorithm for chromium detection. Here we have collected the ‘R’, ‘G’, and ‘B’ values of the reaction colour that signifies the ‘RED’, ‘GREEN’, and ‘BLUE’ parameters. Then we tried to find a predictive pattern with different colour parameters toward developing the standard curve. 96-well plates used in the study were obtained from Costar®, Corning Incorporated. For comparison with commercial spectrophotometer absorbance at a specific wavelength was measured by CLARIOstarplus, BMG Labtech.
Reaction parameter optimisation
Different parameters regarding the Cr(VI) mediated oxidation of TMB were optimised initially. The optimisation of TMB concentration and H2O2 concentration was done by single variable method and using various concentrations of the reagents. The reaction time optimisation was performed in the Cr-Detector system.
TMB concentration optimization
The TMB concentrations ranging from 0.1 to 1.5 mM were used for the above experiment. A fixed concentration of H2O2 and Cr(VI) was then added to the reaction mixture. Finally, the volume was adjusted to 200 μL with ultrapure deionised water. The absorbance maxima values at 650 nm of the concentrations were recorded.
H2O2 concentration optimisation
The different concentrations of H2O2 (10 to 300 mM) were used for the optimisation experiment. At first, a fixed concentration of TMB was added to the wells, followed by different concentrations of H2O2 and a fixed concentration of Cr(VI). Next, the reaction volume was adjusted to 200 μL with ultrapure deionised water. Finally, the absorbance maxima values at 650 nm were recorded.
Reaction time optimization
The reaction time was optimised with fixed TMB, H2O2 concentration, and different Cr(VI) concentration ranges from 0 to 1000 ppb. In addition, a kinetics study of the reaction was performed for 25 minutes with a time interval of 5 minutes. The image parameter values were recorded in the Cr-Detector system every 5 minutes.
Colorimetric determination of Cr(VI)
In the present study, different parameters have been optimized to get a proper signal for detecting Cr(VI) up to the permissible limit by EPA. Solutions of Cr(VI) with concentrations ranging from 1ppb to 1000ppb were formed by serial dilution to obtain the detectable linear range of the Cr(VI). Concentrations of 1 ppb, 25 ppb, 50 ppb, 75 ppb, 100 ppb, 200 ppb, 400 ppb, 600 ppb, 800 ppb and 1000 ppb were used for the test. The different concentrations of Cr(VI) were added to the reaction mixture containing optimum concentrations of TMB and H2O2. The reaction volume was then adjusted to 200 μL with ultrapure deionised water, and absorbance was recorded at 650 nm after the optimized incubation. Further, the mentioned colorimetric experiment for Cr(VI) detection was repeated 500 times in the developed Cr-Detector towards the development of the calibration curve.
Specificity test
Solution of other metals and ions like Hg2+, Pb2+, Ba2+, Cr3+, As3+, Cd, SO42− CO32−, and HCO3− were prepared from their respective salts. A fixed concentration of these ions, along with Cr(VI), was added to optimum concentrations of TMB and H2O2. The reaction volume was adjusted to 200 μL with ultrapure deionised water followed by measuring absorbance at 650 nm. Further anti-interference tests were performed using a mixture of different metal ions in the same reaction mixture. All the mentioned experiments were performed in triplicates and repeated more than once.
Results and discussion
Choice of materials
Cr(VI) and Cr(III) redox conversion is ubiquitous in the environment. In other words, Cr(III) can easily be oxidised to Cr(VI) by an oxidant and Cr(VI) can quickly be reduced to Cr(III) by a reductant [26]. Several attempts have been envisaged toward colorimetric Cr(VI) detection. One such testimony is 1,5-diphenylcarbazide (DPC), which can produce a strong colour change for quantitative or semi-quantitative analysis [27,28]. The DPC-based sensor is one the favourites among several other chromogens for environment monitoring. Despite its advantages, the use of DPC is limited due to its unstable nature, reduced sensitivity, and narrow linear range [29]. Research works have been conducted to increase the stability of DPC by coating various polymers, which, moreover, increases the sensor fabrication cost. Therefore, in this study, other different chromogenic substances such as 3,3’,5,5’-tetramethylbenzidine (TMB), o-Phenyldiamine (OPD), 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) are envisaged towards cost effective colorimetric detection of Cr(VI). 3,3′,5,5′- tetramethylbenzidine (TMB) is one of the most used chromogenic substrates among peroxidase probes. In the presence of a catalyst, TMB can be oxidised by H2O2 to oxidised TMB forming intense blue colour, which can be visualised through the naked eye and spectroscopy. The absorbance peak can be recorded at 650 nm [30]. On the other hand, o-phenylenediamine (OPD) is a chromogenic substrate that generates yellow colour upon oxidation. The oxidation by H2O2 results in the formation of 2, 3-phenazinediamin, generating bright yellow colour and yellow fluorescence. The absorbance maxima of the colour change were measured at 420 nm [31]. Similarly, ABTS is a chemically stable and highly water-soluble peroxidase substrate. When oxidised by H2O2, it serves as a peroxidase substrate and creates a metastable cation by forming ABTS4+. The oxidation produces blue-green colour, which can be quantified by recording absorbance maxima at 734 nm [32]. This work chose TMB as the chromogenic substrate because of its superior sensitivity over OPD and ABTS comparatively. For example, a fixed concentration of the OPD and ABTS failed to produce a measurable colour change for different Cr(VI) concentrations in the presence of 50 mM H2O2, while the same concentration of TMB produced excellent measurable colour changes in the presence of 50mM H2O2 (Fig 2). Also, TMB was found as the chosen substrate in almost every literature regarding peroxidise, nanozyme and DNAzyme activities [33].
[Figure omitted. See PDF.]
Reaction parameter optimisation
TMB concentration optimization.
The absorbance maxima values at 650 nm of the TMB concentrations of 0.1 mM to 1.5 mM were found to be in a range of 0.0047 to 0.0685. It was observed that, from 0.9 mM concentration, the absorbance values got saturated. Therefore, as per this study, 0.9 mM concentration of TMB was found to be optimum for further experiments. The graph for TMB concentration optimisation is represented in S1 Fig in S1 File.
H2O2 concentration optimisation
The absorbance maxima values at 650 nm of the H2O2 concentrations of 10 mM to 300 mM were found in a range of 0.0243 to 0.1523. From the absorbance maxima values, it was observed that, the absorbance values get saturated from H2O2 concentration of 50 mM to 300 mM. Thus, 50 mM H2O2 was finalized as the optimum H2O2 concentration for further experimentations. The graph for H2O2 concentration optimisation is represented in S2 Fig in S1 File.
Colorimetric detection of Cr(VI) through spectrophotometer
In this work, the enzyme-linked immunosorbent assay (ELISA) substrate 3,3′,5,5′-Tetramethylbenzidine (TMB) is utilized as a probe for the colorimetric detection of Cr(VI). The sensing mechanism of this colorimetry is based on the oxidation of TMB by Cr(VI) in an acid media with the assistance of H+ ions produced by H2O2. TMB is converted to oxidized TMB when Cr(VI) is reduced to Cr(III) in an acidic environment. When TMB is oxidized from its colorless form, it becomes blue. Maximum absorption for this blue color occurs at 650 nm wavelength. As a result, by measuring absorbance at 650 nm, the intensity of the generated color is assessed spectroscopically. The absorbance maxima values at 650 nm of different Cr(VI) concentrations of 1 ppb to 1000 ppb were found to be in the range of 0 to 0.2435. Furthermore, it was found that, as the concentration of Cr(VI) increased, the absorbance at 650 nm increased steadily. The inset of Fig 3 provides images of the TMB-H2O2 solutions mediated by various Cr(VI) concentrations for naked-eye examination. The linear fit analysis for the standard curve depicted excellent goodness of fit value of 0.994 (Fig 3). The equation for the standard curve is as follows:
[Figure omitted. See PDF.]
Where ‘Y1’ is the absorbance value at 650 nm, ‘m1’ is the slope, i.e., 2.55744 x 10−4 ± 6.25491 x 10−6, ‘X1’ is the Cr(VI) concentration and ‘c1’ is the intercept value, i.e., -0.00178 ± 0.00281.
Effects of oxidized ions on specificity and anti-interference
To confirm the selective/specific response for detecting Cr(VI), numerous oxidized ions, including cations and anions (Cr3+, Pb2+, As3+, Hg2+, Cd2+, Ba2+, SO42-, HCO3-, PO43-) were supplemented in the reaction medium in presence and absence of Cr6+. The absorbance maxima values at 650 nm were recorded for several other ions in the same TMB-H2O2 reaction system. S3 Fig in S1 File. shows naked-eye color formation only in the wells where Cr(VI) was added. Also, there is a significantly high signal output in the case of Cr(VI), unlike other ions when the absorbance values were studied.
To further investigate the viability of the new colorimetric chemosensor for Cr(VI), an anti-interference experiment was conducted. Under the optimized experimental conditions, equal amounts of other metals and other ions were added to the reaction system, and the absorbance maxima values were then measured at 650 nm. As shown in S3 Fig in S1 File, the presence of interfering ions has no discernible effect on the absorbance intensity at 650 nm of TMB solution after adding Cr(VI). These results demonstrated the novel visual chemosensor’s excellent specificity and anti-interference for a qualitative Cr(VI) study.
Development of UIIS sensing system for Cr(VI) detection
Image parameter selection.
In this work, different image parameter values, such as ‘RED’ ®, ‘GREEN’ (G), and ‘BLUE’ (B), are extracted from the Cr-Detector system for further data analysis. When the G and B values are plotted against the Cr(VI) concentrations, the different Cr(VI) concentrations cannot be clearly distinguished based on either G or B values, as both the G and B values for different concentrations appeared to be similar and thus no distinguishable pattern is observed. The linear fit equations for the plot of Cr(VI) concentration vs. G value and Cr(VI) concentration vs. B value provide poor goodness of fit values of 0.50 and 0.26. In contrast, the R-value and R+G+B value expressed a distinguishable pattern. The R-value for each concentration of Cr(VI) appeared to be much more distinguished; the linear regression analysis provided an excellent adjusted-R2 value of 0.94. A similar pattern is observed for the R+G+B value vs. Cr(VI) concentration plot, where the linear fit with adjusted-R2 value is 0.84. From this, R-value was targeted among other image parameters for all the experiments of colorimetric detection of Cr(VI) due to its highest linearity and adjusted-R2 value. The image parameter selection is represented in S4 Fig in S1 File.
Reaction time optimization.
The reaction time was optimised with fixed TMB, H2O2 concentration, and different Cr(VI) concentration ranges from 0 to 1000 ppb. Then, a kinetics study of the reaction is performed for 25 minutes with a minimum interval of 5 minutes. We considered the ‘RED’ ® value among the other image parameters for further analysis. The data generated every 5 minutes are plotted against the Cr(VI) concentration ranges from 0 ppb to 1000 ppb. Visual colour changes for the concentrations appeared from 15 minutes of the kinetics study. Before 15 minutes, the visual colour changes were negligible. The plot explained that the difference between the ‘R’ values of all the concentrations before 15 minutes of the kinetic study was significantly less and thus produced flat patterns with minimum slope. Whereas distinguishable differences appeared for the different concentrations from 15 to 25 minutes of the kinetic study, thus producing patterns with a higher slope. The linear regression analysis of the patterns observed at 15 minutes, 20 minutes and 25 minutes of the experiments showed adjusted goodness of fit values (R2) as 0.93, 0.90 and 0.86, respectively. From this, it was concluded that the reading could be taken in the Cr-Detector after 15 minutes of incubation of the reaction mixture to get the best possible output. The reaction time optimization through Cr-Detector is represented in S5 Fig in S1 File.
Colorimetric detection of Cr(VI) through Cr-Detector.
Further, the mentioned protocol for colorimetric detection of Cr(VI) was repeated 500 times in Cr-Detector to generate the R-values of the image acquired through scanning the 96 well plates towards developing the calibration curve (Fig 4).
[Figure omitted. See PDF.]
The average R-values of the concentrations range from 0 ppb to 1000 ppb and are plotted against the concentrations towards the development of the calibration curve (Represented in the inset of Fig 4). The plot demonstrates the calibration curve with the linearity found from 25 ppb to 600 ppb of Cr(VI).
The equation of the calibration curve is found to be:
Where ‘Y2’ is R-value generated in Cr-Detector, ‘m2’ is the slope, i.e., -0.06134 ± 0.00222, ‘X2’ is the Cr(VI) concentration, and ‘c2’ is the intercept value, i.e., 100.49757 ± 0.63949. The adjusted-R2 value is found to be 0.99216. The calculated theoretical Limit of Detection (LOD) and Limit of Quantification (LOQ) values from the calibration curve is 0.12 ppb and 0.36 ppb, respectively, with a linear detection range 25 to 600 ppb.
Recovery analysis.
A recovery analysis has been carried out to check the device’s applicability. 50, 100, 250 and 500 ppb of Cr(VI) were spiked in Milli-Q water, and the recovery percentages of the spiked concentrations are 101.056%, 100.026%, 100.0324% and 99.9716%, respectively. The recovery error (RE) percentages for 50, 100, 250 and 500 ppb are -1.056, -0.026, -0.3024 and 0.0284, respectively (S1 Table in S1 File).
Real sample analysis.
Further, 27 samples from the southern belt of river Hooghly of West Bengal, India, were collected where most industries are located. The samples were analyzed per the above reaction protocol, and the data were taken with the spectrophotometer and Cr-Detector. The data are represented in Table 1. Out of 27 samples, except 3, the rest of the samples’ detection was comparable with the conventional Spectrophotometric analysis. The correlation coefficient of the UIIS data with Spectrophotometer data was 0.99018.
[Figure omitted. See PDF.]
Comparison of ‘Cr-Detector’ with other sensors for detection of Cr(VI)
Lots of similar work on sensor development for the detection of Cr(VI) have been reported in earlier studies. The result obtained from ‘Cr-Detector’ was compared with other sensors and it correlates well with other reported literature in Table 2.
[Figure omitted. See PDF.]
Conclusion
Herein we report the development of a chemo-sensor, TMB, for detecting Cr(VI) in water using the spectrophotometric method. The sensor was tested against various interfering parameters. In the presence of those interfering substances, the chemo-sensor exhibited excellent specificity and selectivity towards Cr(VI). Using the spectrophotometric method, the linear range was 1 to 1000 ppb with an adjusted-R2 value of 0.994. Furthermore, the chemo-sensor was integrated with Cr-Detector to demonstrate a lab-to-field application that, extended the sensing system’s applicability. Using a uniform illumination-based system, the linear range was found to be 25 to 600 ppb of Cr(VI) with LOD 0.12 ppb, which is well comparable with other reported literature concerning the linear range and detection limit, as presented in Table 1.
Furthermore, the exhibited wider linear range covers the prescribed permissible limit by USEPA and the Food Safety and Standards Authority of India (FSSAI). Real sample analysis revealed a good correlation with spectrophotometric data. Integrating TMB as a chemo-sensor and Cr-Detector, a selective, rapid, portable, user-friendly device for measuring Cr(VI) is first reported in this endeavor, to our knowledge. Image analysis-based field portable sensor and sensing system development for low-cost rapid onsite monitoring of environmental toxicants will encourage the future development of array-based sensor integrated devices that will enable cumulative detection and multi-parametric analysis of more than one analyte at a time. Moreover, the advancement in soft computing has revolutionized data analytics for sensor development. The rapid emergence of deep learning has been successfully applied to improve the analytical performance of colorimetric determination (e.g., UV-Vis spectrophotometer and colorimetric test paper), including denoising, recognition, and summary of every small characteristic change from each image [49–52]. One example is Deep Convolutional Neural Networks (DCNNs) [51]. DCNNs have excellent potential to increase the sensitivity and selectivity of any biosensor by rapid and effective decision generation. In comparison to human methods, the automated aspect of DCNNs enables quick and effective analysis. The rapid onsite one-click picture processing can be achieved through DCNNs. Moreover, Users with different degrees of image analysis or colorimetric sensing experience can utilize this one-click method since it is user-friendly and accessible. Again, Few-Shot Learning (FSL) and Generative Adversarial networks (GAN) can increase the accuracy of DCNNs. These soft computing technologies may be explored to develop effective decision support systems with superior sensitivity and selectivity in future.
Supporting information
S1 File. Supporting information contains supporting figures and table.
https://doi.org/10.1371/journal.pone.0295687.s001
(DOCX)
Acknowledgments
The authors acknowledge ICAR-NASF. The authors also thank the Ministry of Electronics and Information Technology (MeitY), Government of India, for their continuous support.
Citation: Dei J, Mondal S, Biswas A, Sarkar DJ, Bhattacharyya S, Pal S, et al. (2024) Cr-Detector: A simple chemosensing system for onsite Cr (VI) detection in water. PLoS ONE 19(1): e0295687. https://doi.org/10.1371/journal.pone.0295687
About the Authors:
Jyotsna Dei
Roles: Data curation, Formal analysis, Methodology, Resources, Software, Validation, Writing – original draft
Affiliations: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India, Department of Instrumentation and Electronics Engineering, Jadavpur University Salt Lake Campus, Kolkata, India
Shirsak Mondal
Roles: Data curation, Formal analysis, Software, Writing – original draft
Affiliation: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India
Ayan Biswas
Roles: Formal analysis, Software, Validation, Writing – original draft
Affiliation: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India
Dhruba Jyoti Sarkar
Roles: Supervision, Writing – review & editing
Affiliation: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India
Soumyadeb Bhattacharyya
Roles: Formal analysis, Validation
Affiliation: Agri and Environmental Electronics (AEE) Group, Centre for Development of Advanced Computing (C-DAC), Kolkata, West Bengal, India
Souvik Pal
Roles: Conceptualization, Investigation, Methodology
Affiliation: Agri and Environmental Electronics (AEE) Group, Centre for Development of Advanced Computing (C-DAC), Kolkata, West Bengal, India
Subhankar Mukherjee
Roles: Resources, Validation, Writing – review & editing
Affiliation: Agri and Environmental Electronics (AEE) Group, Centre for Development of Advanced Computing (C-DAC), Kolkata, West Bengal, India
Subrata Sarkar
Roles: Data curation, Formal analysis
Affiliation: Agri and Environmental Electronics (AEE) Group, Centre for Development of Advanced Computing (C-DAC), Kolkata, West Bengal, India
Alokesh Ghosh
Roles: Conceptualization, Resources, Software
Affiliation: Agri and Environmental Electronics (AEE) Group, Centre for Development of Advanced Computing (C-DAC), Kolkata, West Bengal, India
Vipul Bansal
Roles: Conceptualization, Writing – review & editing
Affiliation: Ian Potter NanoBioSensing Facility, NanoBiotechnology Research Laboratory, School of Science, RMIT University, Melbourne, Victoria, Australia
ORICD: https://orcid.org/0000-0002-3354-4317
Rajib Bandhyopadhyay
Roles: Conceptualization, Supervision, Writing – review & editing
Affiliation: Department of Instrumentation and Electronics Engineering, Jadavpur University Salt Lake Campus, Kolkata, India
Basanta Kumar Das
Roles: Supervision, Writing – review & editing
Affiliation: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India
Bijay Kumar Behera
Roles: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing
E-mail: [email protected]
Affiliations: Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland, Fisheries Research Institute, Kolkata, West Bengal, India, College of Fisheries, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India
ORICD: https://orcid.org/0000-0003-4960-9768
1. Gautam RK, Sharma SK, Mahiya S, Chattopadhyaya MC. Contamination of Heavy Metals in Aquatic Media: Transport, Toxicity and Technologies for Remediation. Heavy Metals In Water. 2014; 1–24.
2. Su C. A review on heavy metal contamination in the soil worldwide: Situation, impact and remediation techniques. Environmental Skeptics and Critics. 2014;3: 24.
3. Zhang T, Zhang S, Liu J, Li J, Lu X. Efficient Visual Chemosensor for Hexavalent Chromium via a Controlled Strategy for Signal Amplification in Water. Anal Chem. 2020;92: 3426–3433. pmid:31964141
4. Zhitkovich A. Chromium in drinking water: sources, metabolism, and cancer risks. Chem Res Toxicol. 2011;24: 1617–1629. pmid:21766833
5. He X, Li P. Surface Water Pollution in the Middle Chinese Loess Plateau with Special Focus on Hexavalent Chromium (Cr6+): Occurrence, Sources and Health Risks. Expo Health. 2020;12: 385–401.
6. Coyte RM, McKinley KL, Jiang S, Karr J, Dwyer GS, Keyworth AJ, et al. Occurrence and distribution of hexavalent chromium in groundwater from North Carolina, USA. Sci Total Environ. 2020;711. pmid:32000345
7. Shankar B. Chromium Pollution in the groundwaters of an industrial area in Bangalore, India. Environ Eng Sci. 2009;26: 305–310.
8. Rahman H, Hoque N, Sarker PK, Safa A. Assessment of Hexavalent Chromium Pollution in Buriganga and Dhaleshwari River Waterbodies Adjacent to Tannery Estates in Bangladesh. MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY. 2020;8: 11–15. ).
9. Vasilatos C, Megremi I, Economou-Eliopoulos M, Mitsis I. Hexavalent chromium and other toxic elements in natural waters in the Thiva- Tanagra- Malakasa Basin, Greece. Hellenic Journal of Geosciences. 2008;43: 57–66.
10. Hori M, Shozugawa K, Matsuo M. Hexavalent chromium pollution caused by dumped chromium slag at the urban park in Tokyo. J Mater Cycles Waste Manag. 2015;17: 201–205.
11. Abdul Jabar MAB, Thabit JA. Chemical Pollution Risks for Many Drinking Water Sources in Baghdad City, Iraq. Pol J Environ Stud. 2021;30: 1203–1214.
12. Paul D, Choudhary B, Gupta T, Jose MT. Spatial distribution and the extent of heavy metal and hexavalent chromium pollution in agricultural soils from Jajmau, India. Environ Earth Sci. 2015;73: 3565–3577.
13. Program NT. Toxicology and carcinogenesis studies of sodium dichromate dihydrate (Cas No. 7789-12-0) in F344/N rats and B6C3F1 mice (drinking water studies). Natl Toxicol Program Tech Rep Ser. 2008; 1–192.
14. Sperling M, Xu S, Welz B. Determination of Chromium(III) and Chromium(VI) in Water Using Flow Injection On-Line Preconcentration with Selective Adsorption on Activated Alumina and Flame Atomic Absorption Spectrometric Detection. Anal Chem. 1992;64: 3101–3108.
15. Parks JL, McNeill L, Frey M, Eaton AD, Haghani A, Ramirez L, et al. Determination of total chromium in environmental water samples. Water Res. 2004;38: 2827–2838. pmid:15223276
16. Longbottom JE, Martin TD, Edgell KW, Long SE, Plantz MR, Warden BE. Determination of Trace Elements in Water by Inductively Coupled Plasma–Mass Spectrometry: Collaborative Study. J AOAC Int. 1994;77: 1004–1023.
17. Bahadir Z, Bulut VN, Hidalgo M, Soylak M, Marguí E. Determination of trace amounts of hexavalent chromium in drinking waters by dispersive microsolid-phase extraction using modified multiwalled carbon nanotubes combined with total reflection X-ray fluorescence spectrometry. Spectrochim Acta Part B At Spectrosc. 2015;107: 170–177.
18. Hilali N, Mohammadi H, Amine A, Zine N, Errachid A. Recent Advances in Electrochemical Monitoring of Chromium. Sensors 2020, Vol 20, Page 5153. 2020;20: 5153. pmid:32917045
19. Lace A, Ryan D, Bowkett M, Cleary J. Chromium Monitoring in Water by Colorimetry Using Optimised 1,5-Diphenylcarbazide Method. International Journal of Environmental Research and Public Health 2019, Vol 16, Page 1803. 2019;16: 1803. pmid:31117215
20. Zhuang YT, Chen S, Jiang R, Yu YL, Wang JH. Ultrasensitive Colorimetric Chromium Chemosensor Based on Dye Color Switching under the Cr(VI)-Stimulated Au NPs Catalytic Activity. Anal Chem. 2019;91: 5346–5353. pmid:30912435
21. Zheng F, Lin X, Yu H, Li S, Huang X. Visible-light photoreduction, adsorption, matrix conversion and membrane separation for ultrasensitive chromium determination in natural water by X-ray fluorescence. Sens Actuators B Chem. 2016;226: 500–505.
22. Al-Rawashdeh M, Alzoubi A, Hanandeh S, Yousef I, Al-Nawaiseh M. Performance of Mortar Incorporating Heat-Treated Drinking Water Treatment Sludge as a Silica-Sand Replacement. Civil Engineering Journal. 2022;8: 1639–1652.
23. Janet Priscilla S, Andria Judi V, Daniel R, Sivaji K. Effects of Chromium Doping on the Electrical Properties of ZnO Nanoparticles. Emerging Science Journal. 2020;4: 82–88.
24. Ismail WNW, Syah MIAI, Muhet NHA, Bakar NHA, Yusop HM, Samah NA. Adsorption Behavior of Heavy Metal Ions by Hybrid Inulin-TEOS for Water Treatment. Civil Engineering Journal. 2022;8: 1787–1798.
25. Mukherjee S, Pal S, Pal A, Ghosh D, Sarkar S, Bhand S, et al. UIISScan 1.1: A Field portable high-throughput platform tool for biomedical and agricultural applications. J Pharm Biomed Anal. 2019;174: 70–80. pmid:31158608
26. Zhang X, Liu W, Li X, Zhang Z, Shan D, Xia H, et al. Ultrahigh Selective Colorimetric Quantification of Chromium(VI) Ions Based on Gold Amalgam Catalyst Oxidoreductase-like Activity in Water. Anal Chem. 2018;90: 14309–14315. pmid:30474963
27. Kim BM, Kim B, Nam SE, Eom HJ, Lee S, Kim K, et al. Reductive Transformation of Hexavalent Chromium in Ice Decreases Chromium Toxicity in Aquatic Animals. Environ Sci Technol. 2022;56: 3503–3513. pmid:35245034
28. Alahmad W, Sahragard A, Varanusupakul P. An overview of the recent developments of microfluidic paper-based analytical devices for the detection of chromium species. Microchemical Journal. 2021;170: 106699.
29. Huang ZJ, Li H, Luo JY, Li S, Liu F. Few-Shot Learning-Based, Long-Term Stable, Sensitive Chemosensor for On-Site Colorimetric Detection of Cr(VI). Anal Chem. 2023;95: 6156–6162. pmid:36992572
30. Li M, Su H, Tu Y, Shang Y, Liu Y, Peng C, et al. Development and Application of an Efficient Medium for Chromogenic Catalysis of Tetramethylbenzidine with Horseradish Peroxidase. ACS Omega. 2019;4: 5459–5470.
31. Zhou Y, Ma Z. Fluorescent and colorimetric dual detection of mercury (II) by H2O2 oxidation of o-phenylenediamine using Pt nanoparticles as the catalyst. Sens Actuators B Chem. 2017;C: 53–58.
32. Kumar S, Chaitanya RK, Preedy VR. Assessment of antioxidant potential of dietary components. HIV/AIDS. Elsevier; 2018. pp. 239–253.
33. Zhang X, Yang Q, Lang Y, Jiang X, Wu P. Rationale of 3,3’,5,5’-Tetramethylbenzidine as the Chromogenic Substrate in Colorimetric Analysis. Anal Chem. 2020;92: 12400–12406. pmid:32794705
34. Alahmad W, Tungkijanansin N, Kaneta T, Varanusupakul P. A colorimetric paper-based analytical device coupled with hollow fiber membrane liquid phase microextraction (HF-LPME) for highly sensitive detection of hexavalent chromium in water samples. Talanta. 2018;190: 78–84. pmid:30172544
35. Santra D, Mandal S, Santra A, Ghorai UK. Cost-Effective, Wireless, Portable Device for Estimation of Hexavalent Chromium, Fluoride, and Iron in Drinking Water. Anal Chem. 2018;90: 12815–12823. pmid:30281277
36. Kumar N, Madhwal D, Jain VK. A POC device for on-the-spot detection of hexavalent chromium in wastewater. J Environ Chem Eng. 2020;8: 104342.
37. Liu Q, Kim J, Chemical TC-S and AB. A highly sensitive photoelectrochemical sensor with polarity-switchable photocurrent for detection of trace hexavalent chromium. Elsevier. Sens. Actuators B Chem. 2020; 317, p.128181
38. Roy S, Bardhan S, Mondal D, Saha I, Roy J, Das S, et al. Polymeric carbon dot/boehmite nanocomposite made portable sensing device (Kavach) for non-invasive and selective detection of Cr(VI) in wastewater and living cells. Sens Actuators B Chem. 2021;348.
39. Liu G, Lin YY, Wu H, Lin Y. Voltammetric detection of Cr(VI) with disposable screen-printed electrode modified with gold nanoparticles. Environ Sci Technol. 2007;41: 8129–8134. pmid:18186348
40. Zhao K, Ge L, Wong TI, Zhou X, Lisak G. Gold-silver nanoparticles modified electrochemical sensor array for simultaneous determination of chromium(III) and chromium(VI) in wastewater samples. Chemosphere. 2021;281. pmid:34029966
41. Karthika A, Nikhil S, Suganthi A, Rajarajan M. A facile sonochemical approach based on graphene carbon nitride doped silver molybdate immobilized nafion for selective and sensitive electrochemical detection of chromium (VI) in real sample. Advanced Powder Technology. 2020;31: 1879–1890.
42. Ouyang R, Bragg SA, Chambers JQ, Xue ZL. Flower-like self-assembly of gold nanoparticles for highly sensitive electrochemical detection of chromium(VI). Anal Chim Acta. 2012;722: 1–7. pmid:22444528
43. Kowsalya B, Anusha Thampi V V., Sivakumar V, Subramanian B. Electrochemical detection of Chromium(VI) using NiO nanoparticles. Journal of Materials Science: Materials in Electronics. 2019;30: 14755–14761.
44. Jin W, Wu G, Chen A. Sensitive and selective electrochemical detection of chromium (vi) based on gold nanoparticle-decorated titania nanotube arrays. Analyst. 2013;139: 235–241. pmid:24191278
45. Jena BK, Raj CR. Highly sensitive and selective electrochemical detection of sub-ppb level chromium(VI) using nano-sized gold particle. Talanta. 2008;76: 161–165. pmid:18585258
46. Tu J, Gan Y, Liang T, Wan H, Wang P. A miniaturized electrochemical system for high sensitive determination of chromium(VI) by screen-printed carbon electrode with gold nanoparticles modification. Sens Actuators B Chem. 2018;C: 582–588.
47. Liu Y, Gao G, Hu J, Zou X. ELECTROCHEMICAL SCIENCE Electrodeposited AuNPs/rGO Nanocomposite as sensor for Cr(VI) Determination in Water. Int J Electrochem Sci. 2018;13: 11853–11866.
48. Wu S, Dai X, Cheng T, Li S. Highly sensitive and selective ion-imprinted polymers based on one-step electrodeposition of chitosan-graphene nanocomposites for the determination of Cr(VI). Carbohydr Polym. 2018;195: 199–206. pmid:29804969
49. Huang ZJ, Luo J yi, Zheng FY, Li SX, Liu FJ, Lin LX, et al. Long-term stable, high accuracy, and visual detection platform for In-field analysis of nitrite in food based on colorimetric test paper and deep convolutional neural networks. Food Chem. 2022;373. pmid:34838401
50. Luo J, Huang Z, Li S, Zheng F, Liu F, Huang Q, et al. Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation. Anal Chem. 2022;94: 14801–14809. pmid:36239120
51. Huang ZJ, Li H, Luo JY, Li S, Liu F. Few-Shot Learning-Based, Long-Term Stable, Sensitive Chemosensor for On-Site Colorimetric Detection of Cr(VI). Anal Chem. 2023;95: 6156–6162. pmid:36992572
52. Xue X, Sun H, Yang M, Liu X, Hu HY, Deng Y, et al. Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective. Anal Chem. 2023;95: 13733–13745. pmid:37688541
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2024 Dei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Due to the increase in urbanization and industrialization, the load of toxicants in the environment is alarming. The most common toxicants, including heavy metals and metalloids such as hexavalent Chromium, have severe pathophysiological impacts on humans and other aquatic biotas. Therefore, developing a portable rapid detection device for such toxicants in the aquatic environment is necessary. This work portrays the development of a field-portable image analysis device coupled with 3,3’,5,5’-tetramethylbenzidine (TMB) as a sensing probe for chromium (VI) detection in the aquatic ecosystem. Sensor parameters, such as reagent concentration, reaction time, etc., were optimized for the sensor development and validation using a commercial UV-Vis spectrophotometer. The chemoreceptor integrated with a uniform illumination imaging system (UIIS) revealed the system’s applicability toward Cr(VI) detection. The calibration curve using the R-value of image parameters allows Cr(VI) detection in the linear range of 25 to 600 ppb, which covers the prescribed permissible limit by various regulatory authorities. Furthermore, the adjusted R2 = 0.992 of the linear fit and correlation coefficients of 0.99018 against the spectrophotometric method signifies the suitability of the developed system. This TMB-coupled field-portable sensing system is the first-ever reported image analysis-based technology for detecting a wide range of Cr(VI) in aquatic ecosystems to our knowledge.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Bandhyopadhyay, Rajib; Das, Basanta Kumar; Behera, Bijay Kumar