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<p>This study presents a novel bimetallic iron–silver phosphate (FeAg(PO<sub>4</sub>)<sub>2</sub>) nanocatalyst. We detail a one-pot hydrothermal synthesis of FeAg(PO<sub>4</sub>)<sub>2</sub>, a hetero-structured composite dominated by silver phosphate (Ag<sub>3</sub>PO<sub>4</sub>) and iron (III) phosphate (FePO<sub>4</sub>). Characterization techniques include X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectroscopy, UV–Vis diffuse reflectance, X-ray photoelectron spectroscopy, Brunauer–Emmett–Teller surface area analysis, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray mapping, revealing a mesoporous structure with a surface area of 55.60&#xa0;m<sup>2</sup>/g and a pore size of 2&#xa0;nm. Machine learning, particularly the Random Forest Tree model, predicted energy per atom of − 7.17&#xa0;eV, and a band gap of 1.87&#xa0;eV, indicating high stability and suitability for catalysis. The nano-catalyst showed high efficiency in synthesizing benzimidazole derivatives (up to 95% yield in 10&#xa0;min at 60&#xa0;°C in ethanol) and was reusable for five cycles. It also demonstrated selective fluorescence quenching for L-cysteine detection, with sulfur-containing amino acids exhibiting higher fluorescence. The study concludes with potential applications in pharmaceuticals and sensing, encouraging further research.</p>

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Artificial intelligence driven design of FeAg(PO4)2 nanocatalyst for benzimidazole synthesis and L-cysteine detection

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Zohra Hamiani,
  • Mohammed Beldjilali,
  • Amina Berrichi,
  • Abdelkader Ech-Chergui Nebatti,
  • Ridha Hassaine,
  • Nihel Dib,
  • Ginesa Blanco,
  • Redouane Bachir

摘要

This study presents a novel bimetallic iron–silver phosphate (FeAg(PO4)2) nanocatalyst. We detail a one-pot hydrothermal synthesis of FeAg(PO4)2, a hetero-structured composite dominated by silver phosphate (Ag3PO4) and iron (III) phosphate (FePO4). Characterization techniques include X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectroscopy, UV–Vis diffuse reflectance, X-ray photoelectron spectroscopy, Brunauer–Emmett–Teller surface area analysis, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray mapping, revealing a mesoporous structure with a surface area of 55.60 m2/g and a pore size of 2 nm. Machine learning, particularly the Random Forest Tree model, predicted energy per atom of − 7.17 eV, and a band gap of 1.87 eV, indicating high stability and suitability for catalysis. The nano-catalyst showed high efficiency in synthesizing benzimidazole derivatives (up to 95% yield in 10 min at 60 °C in ethanol) and was reusable for five cycles. It also demonstrated selective fluorescence quenching for L-cysteine detection, with sulfur-containing amino acids exhibiting higher fluorescence. The study concludes with potential applications in pharmaceuticals and sensing, encouraging further research.