Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak

Ye, Jia-Jia; Lin, Chin E.; Huang, Xing‐Jiu

doi:10.1016/j.jelechem.2020.113934

Cited by 29 publications

(26 citation statements)

References 17 publications

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“…Other chemistry disciplines, such as materials, physical, and organic chemistry, were early adopters, but modern machine learning techniques have been underutilized in electrochemistry, and specifically voltammetry [132,133]. While advanced techniques, such as deep learning, have been used for classification of voltammograms [134,135], its counterpart (regression) is less often reported [88,119]. The development of this novel voltammetric technique (RPV) coupled with fit-for-purpose machine learning (ML) pipelines (broadly defined as RPV-ML) represents a new paradigm for electroanalytical classification and quantitation of multiplexed neurochemical responses across timescales.…”

Section: Study Limitations and Future Directionsmentioning

confidence: 99%

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

et al. 2021

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Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis. Graphical abstract

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Section: Study Limitations and Future Directionsmentioning

confidence: 99%

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

et al. 2021

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“…Accordingly, the detection method of coupling SWASV with SVR, which required lower cost and simpler electrode modification, could accurately detect the Pb(II) concentration under the interference of Cu(II). 1 GC/GQDs-NF/GCE: Graphene quantum dots-nafion modified glassy carbon electrode; 2 (BiO) 2 CO 3 @SWCNT-Nafion/GCE: (BiO) 2 CO 3 @single-walled carbon nanotube nanocomposite/nafion composition modified glassy carbon electrode; 3 : SWCNTs-Nafion/IL/SPE: Bi/single-walled carbon nanotubes-nafion/ionic liquid nanocomposite modified screen-printed electrode; 4 Bi/SPE: Bismuth film modified screen-printed carbon electrode; 5 Bi/p-Tyr/GC: rod-like poly-tyrosine/Bi modified glassy carbon electrode; 6 Bi 2 O 3 /GCE: Bismuth oxide modified glassy carbon electrode; 7 Bi-film/GCE: Bismuth film modified glassy carbon electrode.…”

Section: Analysis Of the Svr Model Results For Pb(ii) Concentration Dementioning

confidence: 99%

“…Square wave anodic stripping voltammetry (SWASV), as an electrochemical analysis technology, was widely applied for the detection of HMs [6,7]. SWASV, possessing many advantages of low cost, high sensitivity, excellent selectivity, and fast detection, was a good alternative to spectroscopic analysis methods [8][9][10], such as atomic absorption spectroscopy (AAS) and inductively coupled plasma-atomic emission spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Liu

Zhao

Liu

2020

Sensors

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In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals of Pb2+ was investigated, and a nonlinear relationship between Pb2+ concentration and the peak currents of Pb2+ and Cu2+ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb2+ and Cu2+) and one output (i.e., Pb2+ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R2) and root-mean-square error (RMSE). Results showed that the SVR model with R2 and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb2+ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.

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“…While the coupled reaction-transport phenomena that govern voltammetric responses are well-known, 5,19 these processes are not yet widely considered in automated voltammetric labeling methodologies, which may partially explain why prior protocols are challenged when evaluated at different analyte and supporting salt concentrations. 31,32 By incorporating physical phenomena into model formulations, voltammograms may be accurately simulated across a range of species concentrations using multiple experimental techniques with a single set of electrochemical and transport descriptors. Further, the number of possible compositions for a multicomponent solution scales combinatorically with the total number of species, potentially rendering exhaustive evaluation infeasible for larger sets.…”

Section: Introductionmentioning

confidence: 99%

Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions

Fenton

Brushett

2021

Preprint

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Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in solutions and as such has been used in numerous fields of study. Recently, automation has been introduced to extend the capabilities of voltammetric analysis through approaches such as Bayesian parameter estimation and compound identification. However, opportunities exist to enable more versatile methods across a wider range of solution compositions and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of analytes in multicomponent solutions across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled solutions each containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating the ability to identify redox-active constituents of a multicomponent solution. Finally, we identify areas of further improvement-such as achieving greater detection accuracy-and future applications to potentially enhance in situ or operando diagnostic workflows.

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Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak

Cited by 29 publications

References 17 publications

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions

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