Smart electrochemical sensing of xylitol using a combined machine learning and simulation approach

A novel sensor was proposed for the detection of xylitol in sugar free chewing gum using Au nanoparticles (NPs) derived from Callistemon viminalis leaf extract coupled with multiwalled carbon nanotubes (MWCNTs) doped onto glassy carbon electrode (GCE). In comparison to the bare GCE, the modified GCE/MWCNT/AuNPs sensor showed about 45-fold better electrochemical response to xylitol. Under the optimal conditions, the designed sensor achieved a detection limit of 9.8 × 10 6 pM for concentrations ranging from 9.9 × 10 6 to 2.9 × 10 5 pM. The practicability was tested on sugar-free sample yielding recoveries of 97–100% with RSDs of 2.83–3.33%. Machine learning (ML) was used to predict changes in voltammetric signal with changing potential over time demonstrating the fundamental knowledge of the electrochemical reaction. The performance of the Artificial Neural Network (ANN) provides good accuracy and precision in predicting the intensity (I) along with repeated ANN runs, with a mean square error (MSE) of 0.007 (± 0.002) and a determination coefficient (R2) of 0.9992 ± 0.0006. Additionally, the interaction of xylitol on the electrode surfaces were investigated using Monte Carlo adsorption studies and 1000 ps Molecular Dynamics simulations under NVT conditions. According to the frontier molecular orbitals obtained through Density Functional Theory calculations, the reactive sites of xylitol occur at the hydroxyl group on the second carbon. Using complementary measurement techniques, this new strategy exhibits a great potential for rapid detection of xylitol in food and dental products

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact

Novel Dithiocarbamates for electrochemical detection of Nickel (II) in environmental samples

Ammonium 4-phenylpiperazine-1-dithiocarbamate (Amm 4-PP-DTC) and ammonium 4-benzylpiperidine-1-dithiocarbamate (Amm 4-BP-DTC) were synthesized for the determination of nickel(II) using catalytic hydrogen currents (CHC’s) technique with DC Polarography. The method was based on the chelation of nickel(II) with Amm 4-PP-DTC/ Amm 4-BP-DTC in presence of NH 4 OH at pH 6.8 to produce catalytic hydrogen current at -1.50V and -1.41 V vs. SCE respectively. Optimized polarographic conditions were established by studying effect of pH, supporting electrolyte (NH4Cl), ligands and metal ion concentration and effect of adverse ions on peak height to improve the sensitivity, selectivity and detection limits of the present method. This technique is successfully applied for the analysis of nickel(II) in different matrices with recoveries ranging from 96.0-99.0 % and the results obtained were comparable with the atomic absorption spectroscopy

Preparation, Spectrochemical, and Computational Analysis of L-Carnosine (2-[(3-Aminopropanoyl)amino]-3-(1H-imidazol-5-yl)propanoic Acid) and Its Ruthenium (II) Coordination Complexes in Aqueous Solution

This study reports the synthesis and characterization of novel ruthenium (II) complexes with the polydentate dipeptide, L-carnosine (2-[(3-aminopropanoyl)amino]-3-(1H-imidazol-5-yl)propanoic acid). Mixed-ligand complexes with the general composition [MLp(Cl)q(H2O)r]·xH2O (M = Ru(II); L = L-carnosine; p = 3 − q; r = 0–1; and x = 1–3) were prepared by refluxing aqueous solutions of the ligand with equimolar amounts of ruthenium chloride (black-alpha form) at 60 °C for 36 h. Physical properties of the complexes were characterized by elemental analysis, DSC/TGA, and cyclic voltammetry. The molecular structures of the complexes were elucidated using UV-Vis, ATR-IR, and heteronuclear NMR spectroscopy, then confirmed by density function theory (DFT) calculations at the B3LYP/LANL2DZ level. Two-dimensional NMR experiments (1H COSY, 13C gHMBC, and 15N gHMBC) were also conducted for the assignment of chemical shifts and calculation of relative coordination-induced shifts (RCIS) by the complex formed. According to our results, the most probable coordination geometries of ruthenium in these compounds involve nitrogen (N1) from the imidazole ring and an oxygen atom from the carboxylic acid group of the ligand as donor atoms. Additional thermogravimetric and electrochemical data suggest that while the tetrahedral-monomer or octahedral-dimer are both possible structures of the formed complexes, the metal in either structure occurs in the (2+) oxidation state. Resulting RCIS values indicate that the amide-carbonyl, and the amino-terminus of the dipeptide are not involved in chelation and these observations correlate well with theoretical shift predictions by DFT

Insights into the Design of An Enzyme Free Sustainable Sensing Platform for Efavirenz

In this study, a new hybrid sensor was developed using titanium oxide nanoparticles (TiO2-NPs) and nafion as an anchor agent on a glassy carbon electrode (GCE/TiO2-NPs-nafion) to detect efavirenz (EFV), an anti-HIV medication. TiO2-NPs was synthesized using Eucalyptus globulus leaf extract and characterized using ultraviolet–visible spectroscopy (UV–VIS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive spectroscopy (EDS). The electrochemical and sensing properties of the developed sensor for EFV were assessed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The current response of GCE/TiO2-NPs-nafion electrode towards the oxidation of EFV was greater compared to the bare GCE and GCE/TiO2-NPs electrodes. A linear dynamic range of 4.5 to 18.7 µM with 0.01 µM limit of detection was recorded on the electrode using differential pulse voltammetry (DPV). The electrochemical sensor demonstrated good selectivity and practicality for detecting EFV in pharmaceuticals (EFV drugs) with excellent recovery rates, ranging from 92.0–103.9%. The reactive sites of EFV have been analyzed using quantum chemical calculations based on density functional theory (DFT). Monte Carlo (MC) simulations revealed a strong electrostatic interaction on the substrate-adsorbate (GCE/TiO2-NPs-nafion-EFV) system. Results show good agreement between the MC computed adsorption energies and the experimental CV results for EFV. The stronger adsorption energy of nafion onto the GCE/TiO2-NPs substrate contributed to the catalytic role in the signal amplification for sensing of EFV. Our results provide an effective way to explore the design of new 2D materials for sensing of EFV, which is highly significant in medicinal and materials chemistry

Constructing probabilistic ATMS using extended incidence calculus

ATMSs - assumption truth maintenance systemsAvailable from British Library Document Supply Centre-DSC:3511.638(EU-DAI-RP--813) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo