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Abstract <p>The electrochemical synthesis of silver nanoparticles using Polyvinylpyrrolidone-k30 as a stabilizer garnered significant interest due to its effectiveness as an antibacterial agent with minimal toxicity, environmental benefits, and commercial viability. Optimizing the concentration of PVP K30-AgNPs was critical because their characteristics depended on concentration and size. This research investigated the antimicrobial efficacy of silver nanoparticles (AgNPs) against a panel of common food-borne pathogens, including <i>Escherichia coli</i> (ATCC 25922), <i>Salmonella enterica</i> (ATCC 13076), <i>Staphylococcus aureus</i> (ATCC 25923), and <i>Bacillus subtilis</i> (ATCC 6633P), by measuring their concentration efficiency. An Artificial Neural Network model was employed to optimize and evaluate the enhancement of PVP K30-AgNPs concentration. Response Surface Methodology (RSM) was utilized to statistically analyse the influence of process parameters on the concentration of PVP K30-AgNPs. The morphology of the generated silver nanoparticles was examined using a combination of dynamic light scattering measurements (DLS), Zeta Potential, atomic force microscopy (AFM), Fourier Transform spectroscopy, X-ray diffraction analysis (DRX), and Ultraviolet–Visible spectroscopy. Two rods of silver (99.99% purity) were utilized for the electrochemical synthesis of PVP K30-AgNPs. The studies used three significant process parameters: Time, Temperature, and Voltage. The experimental data were then used to train an Artificial Neural Network model using the Levenberg–Marquardt (LM) backpropagation technique to improve the accuracy of predicting the minimum concentration of PVP K30-AgNPs. The optimum version of the Artificial Neural Network adopted a feed-forward multilayer Perceptron (MLP) configuration with three input nodes, a hidden layer of ten neurons, and one output node. The model demonstrated superior predictive capabilities, achieving an <i>R</i><sup>2</sup> of 0.99999 and a mean-squared error (MSE) of 0.00198 on the experimental data. This regression analysis confirmed the ability of the Artificial Neural Network to estimate the concentration of PVP K30-AgNPs precisely.</p> Graphical abstract <p></p>

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Electrochemical synthesis of PVP K30-AgNPs modelling based on ANN-LMP: Box-Behnken optimization for antioxidant and antibacterial activities

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Faouzi Lasmi,
  • Houria Hamitouche,
  • Hassiba Laribi-Habchi,
  • Amel Boudechicha,
  • Anis Lasmi,
  • Rima Benbekai,
  • Sid Ahmed Bennoui,
  • Yacine Benguerba,
  • Nadjib Chafai

摘要

Abstract

The electrochemical synthesis of silver nanoparticles using Polyvinylpyrrolidone-k30 as a stabilizer garnered significant interest due to its effectiveness as an antibacterial agent with minimal toxicity, environmental benefits, and commercial viability. Optimizing the concentration of PVP K30-AgNPs was critical because their characteristics depended on concentration and size. This research investigated the antimicrobial efficacy of silver nanoparticles (AgNPs) against a panel of common food-borne pathogens, including Escherichia coli (ATCC 25922), Salmonella enterica (ATCC 13076), Staphylococcus aureus (ATCC 25923), and Bacillus subtilis (ATCC 6633P), by measuring their concentration efficiency. An Artificial Neural Network model was employed to optimize and evaluate the enhancement of PVP K30-AgNPs concentration. Response Surface Methodology (RSM) was utilized to statistically analyse the influence of process parameters on the concentration of PVP K30-AgNPs. The morphology of the generated silver nanoparticles was examined using a combination of dynamic light scattering measurements (DLS), Zeta Potential, atomic force microscopy (AFM), Fourier Transform spectroscopy, X-ray diffraction analysis (DRX), and Ultraviolet–Visible spectroscopy. Two rods of silver (99.99% purity) were utilized for the electrochemical synthesis of PVP K30-AgNPs. The studies used three significant process parameters: Time, Temperature, and Voltage. The experimental data were then used to train an Artificial Neural Network model using the Levenberg–Marquardt (LM) backpropagation technique to improve the accuracy of predicting the minimum concentration of PVP K30-AgNPs. The optimum version of the Artificial Neural Network adopted a feed-forward multilayer Perceptron (MLP) configuration with three input nodes, a hidden layer of ten neurons, and one output node. The model demonstrated superior predictive capabilities, achieving an R2 of 0.99999 and a mean-squared error (MSE) of 0.00198 on the experimental data. This regression analysis confirmed the ability of the Artificial Neural Network to estimate the concentration of PVP K30-AgNPs precisely.

Graphical abstract