Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)

Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics

This work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Differential Pulse Voltammetry (DPV). Preliminary electrochemical analysis was performed using cyclic voltammetry, and the sensor morphology was studied by scanning electronic microscopy and electrochemical impedance spectroscopy. The sample set ranging from 0.5 to 80 µmol L^-1 was prepared using a complete factorial design (3⁴) and considering some interferent species such as ascorbic acid, glucose, and sodium dodecyl sulfate to build the response model and an external randomly subset of samples within the experimental domain. A data compression strategy based on discrete wavelet transform was applied to handle voltammograms' complexity and high dimensionality. Afterward, Partial Least Square Regression (PLS) and Artificial Neural Networks (ANN) predicted the drug concentrations in the mixtures. PLS-adjusted models (n = 12) successfully predicted the concentration of paracetamol and diclofenac, achieving correlation values of R ≥ 0.9 (testing set). Meanwhile, the ANN model (four layers) obtained good prediction results, exhibiting R ≥ 0.968 for the four analyzed drugs (testing stage). Thus, an MWCNT-CPE electrode can be successfully used as a potential sensor for voltammetric determination and NSAID analysis.

Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm

The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO₃, and the concentration and reduction time of NaBH₄ are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices.

Electrochemical Deposition of Gold Nanoparticles on Reduced Graphene Oxide by Fast Scan Cyclic Voltammetry for the Sensitive Determination of As(III)

In this study, a stable, sensitive electrochemical sensor was fabricated by the electrochemical codeposition of reduced graphene oxide (rGO) and gold nanoparticles on a glassy carbon electrode (rGO-Au_nano/GCE) using cyclic voltammetry (CV), which enabled a simple and controllable electrode modification strategy for the determination of trace As(III) by square wave anodic stripping voltammetry (SWASV). SWASV, CV, electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the electrochemical properties and morphology of the proposed sensing platform. The number of sweep segments, the deposition potential and the deposition time were optimized to obtain ideal sensitivity. The presence of rGO from the electroreduction of graphene oxide on the sensing interface effectively enlarged the specific surface area and consequently improved the preconcentration capacity for As(III). The rGO-Au_nano/GCE sensor exhibited outstanding detection performance for As(III) due to the combined effect of Au_nano and rGO formed during the electroreduction process. Under the optimized conditions, a linear range from 13.375 × 10⁻⁹ to 668.75 × 10⁻⁹ mol/L (1.0 to 50.0 μg/L) was obtained with a detection limit of 1.07 × 10⁻⁹ mol/L (0.08 μg/L) (S/N = 3). The reproducibility and reliability of the rGO-Au_nano/GCE sensor were also verified by performing 8 repetitive measurements. Finally, the rGO-Au_nano/GCE sensor was used for the analysis of real samples with satisfactory results.

Sensitive determination of trace Cd(ii) and Pb(ii) in soil by an improved stripping voltammetry method using two different in situ plated bismuth-film electrodes based on a novel electrochemical measurement system

In this study, a simple but effective electrochemical method was developed to minimize the interference from real soil samples and increase the sensitivity of Pb(ii) and Cd(ii) detection by square-wave anodic stripping voltammetry (SWASV) using a novel electrochemical measurement system, which can be used for the on-site determination of trace Cd(ii) and Pb(ii) in real soil samples. The method involved performing SWASV following double deposition and stripping steps at two in situ plated bismuth-film electrodes with drastically different surface properties. Pb(ii) and Cd(ii) were first deposited on an in situ plated bismuth-film graphite carbon paste electrode (Bi/GCPE). When the first deposition was finished, the GCPE was moved to a micro-electrolytic cell to perform the first stripping step. The following measurements were performed with the other deposition and stripping steps using a highly sensitive in situ plated bismuth-film multiwalled carbon nanotube–Nafion composite modified glassy carbon electrode (Bi/MWCNT–Nafion/GCE) as the working electrode. Pb(ii), Cd(ii) and Bi(iii) stripped from the GCPE in the micro-electrolytic cell were partially deposited on the MWCNT–Nafion/GCE, and the stripping current signals were obtained from their oxidation during the second stripping step. Considering the small volume of the micro-electrolytic cell, the concentrations of Cd(ii) and Pb(ii) were drastically higher than those in the bulk solution, and therefore, the detection limits were reduced. Under the optimized conditions, the concentrations in the linear range spanned from 1.0 to 45.0 μg L⁻¹ for both Pb(ii) and Cd(ii), with a detection limit of 0.03 μg L⁻¹ for Pb(ii) and 0.02 μg L⁻¹ for Cd(ii) (S/N = 3). Finally, analyses of real samples were performed to detect trace levels of Pb(ii) and Cd(ii) in soil with satisfactory results.

A double-stripping voltammetry method was designed and developed to improve the sensitivity and anti-interference ability for detection of heavy metals.

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

Identification of Load Categories in Rotor System Based on Vibration Analysis

Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications.