Simultaneous Voltammetric Determination of Heavy Metals by Use of Crown Ether-modified Electrodes and Chemometrics

Machine learning toward high-performance electrochemical sensors

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

Building Tailored Interfaces through Covalent Coupling Reactions at Layers Grafted from Aryldiazonium Salts

Aryldiazonium ions are widely used reagents for surface modification. Attractive aspects of their use include wide substrate compatibility (ranging from plastics to carbons to metals and metal oxides), formation of stable covalent bonding to the substrate, simplicity of modification methods that are compatible with organic and aqueous solvents, and the commercial availability of many aniline precursors with a straightforward conversion to the active reagent. Importantly, the strong bonding of the modifying layer to the surface makes the method ideally suited to further on-surface (postfunctionalization) chemistry. After an initial grafting from a suitable aryldiazonium ion to give an anchor layer, a target species can be coupled to the layer, hugely expanding the range of species that can be immobilized. This strategy has been widely employed to prepare materials for numerous applications including chemical sensors, biosensors, catalysis, optoelectronics, composite materials, and energy conversion and storage. In this Review our goal is first to summarize how a target species with a particular functional group may be covalently coupled to an appropriate anchor layer. We then review applications of the resulting materials.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration

In this study, square wave anodic stripping voltammetry using two different types of electrodes (carbon nanotube electrode and graphite electrode) was combined with chemometric methods - partial least squares (PLS) and artificial neural networks (ANN) for determining copper, zinc, cadmium and lead in cachaça. The objectives were comparison of methods developed and the verification of the quality of artisanal cachaças in terms of metal content. For the development of the methodology, inductively coupled plasma optical emission spectrometry (ICP OES) was used as reference technique. The performance of multivariate models obtained was evaluated by the coefficient of determination (R²) and root mean square error of prediction (RMSEP). F test was utilized for comparing methods at confidence level of 95%. Better results were observed by using carbon nanotube electrode regardless of the multivariate method proposed. The methodology is simple, fast, and inexpensive and it can be used in quality control laboratories.

New insight for simultaneous determination of hazardous di-hydroxybenzene isomers at crown ether modified polymer/carbon nanotubes composite sensor

A new insight is presented in the fabrication of a reliable electrochemical sensor for di-hydroxybenzene isomers; hydroquinone (HQ), catechol (CC), and resorcinol (RC) which have been considered as common pollutants in environment and water samples. The sensor is based on modifying the glassy carbon electrode (GC) with successive layers, multi-walled carbon nanotubes (CNT), poly-hydroquinone (PHQ) and benzo-12-crown-4 (CE); GC/CNT/PHQ/CE. CE is introduced for the first time as a receptor for the di-hydroxybenzene isomers based on host-guest size matching. Other cycling compound with different cavity diameter as β-cyclodextrin (β-CD) (6.0-6.5 Å) was examined displaying lower current responses. CE exhibited "fit" cavity size (1.20-1.50 Å). Thus, the inclusion complexes formed between β-CD and di-hydroxybenzene isomers are less stable. The layered sensor showed highly electro-catalytic activity for simultaneous determination of isomers; HQ, CC and RC in the concentration ranges of 0.03-100 μM, 0.01-100 μM and 0.05-100 μM with low detection limit values of 0.156 nM, 0.118 nM and 0.427 nM, respectively. The practical impact of the sensor was illustrated for determination of di-hydroxybenzene isomers in real water matrices from two different sources. Moreover, anti-interference ability of the layered sensor for determination of di-hydroxybenzene isomers was successfully achieved in presence of common interfering ions and organic pollutants.

Single-Atom Enzyme-Functionalized Solution-Gated Graphene Transistor for Real-Time Detection of Mercury Ion

Mercury ion (Hg²⁺), a bioaccumulating and toxic heavy metal, can cause severe damages to the environment and human health. Therefore, development of high-performance Hg²⁺ sensors is highly desirable. Herein, we construct a uniform dodecahedral shaped N-doped carbon decorated by single Fe site enzyme (Fe-N-C SAE), which exhibits good performance for Hg²⁺ detection. The N atom on Fe-N-C SAE can specifically recognize Hg²⁺ through chelation between Hg²⁺ and N atom, while the catalytic site on the single-atom enzyme acts as a signal amplifier. The Fe-N-C SAE-functionalized solution-gated graphene transistor exhibits a dramatic improvement in the selectivity and sensitivity of the devices. The sensor can rapidly detect Hg²⁺ down to 1 nM within 2 s. Besides, a relatively good repeatability and reproducibility for the detection of Hg²⁺ have also been found in our sensor platform. Our findings expand the application of single-atom catalysts in the field of food safety and environmental monitoring.

A new multivariate standard addition strategy for stripping voltammetric electronic tongues: Application to the determination of Tl(I) and In(III) in samples with complex matrices

A new multivariate standard addition strategy applicable to stripping methods was proposed as an extention of the classical univariate standard addition method for the resolution of complex samples involving overlapped peaks and complex matrices. The proposed strategy consists in alternate additions of the considered analytes and the further extrapolation to a simulated blank solution measured by skipping the preconcentration step (deposition time = 0). This calibration approach was successfully tested in tonic water samples spiked with Tl(I) and In(III) using a sensor array based on a SeCyst-SPCNFE and an ex-situ-BiSPCE, providing good concordance between replicates and much better accuracy than the usual multivariate external calibration method.