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<p>This study investigates NaClO/FeSO<sub>4</sub>-based Fenton-like oxidation for Amoxicillin (AMX) degradation in aqueous systems. Process optimization using Response Surface Methodology coupled with Central Composite Design (RSM-CCD) identified optimal conditions of [NaClO] = 800&#xa0;µM, [AMX] = 35&#xa0;mg L<sup>−1</sup>, [FeSO<sub>4</sub>] = 5&#xa0;mg L<sup>−1</sup>, and pH = 3, yielding 91.8% removal. A hybrid RSM, Artificial Neural Networks (ANN), and Genetic Algorithm (GA) modeling approach further enhanced performance, predicting 99% efficiency (R<sup>2</sup> = 0.991) and achieving 97% removal under refined conditions (AMX concentration of 34.85&#xa0;mg L<sup>−1</sup>, [NaClO] = 508.4 µM, [FeSO<sub>4</sub>] = 7.83 mg L<sup>−1</sup>, and a pH of 3.14). Green-synthesized IONPs nanoparticles supplemented the homogeneous system for by-products formation and toxicity assessment experiments, achieving 99.9% AMX removal within 60&#xa0;min (<i>k</i> = 0.114&#xa0;min<sup>−1</sup>) when combined with hydroxylamine. Trace and ultra-trace analysis using QuEChERS extraction coupled with Direct Infusion-High Resolution Mass Spectrometry (DI-HRMS) enabled quantification of the parent compound and seven transformation by-products. Ecotoxicity testing with <i>Chlorella vulgaris</i> demonstrated transient inhibition followed by substantial detoxification.</p>

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Eco-friendly AOP for Amoxicillin removal: hybrid optimization, by-products identification via QUECHERS-HRMS technique, and toxicity assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Mouna Imene Ousaadi,
  • Mohammed Berkani,
  • Anfel Smaali,
  • Abdelatif Ben Kaida,
  • Gianluca Viscusi,
  • Nada Abbas,
  • Yassine Kadmi

摘要

This study investigates NaClO/FeSO4-based Fenton-like oxidation for Amoxicillin (AMX) degradation in aqueous systems. Process optimization using Response Surface Methodology coupled with Central Composite Design (RSM-CCD) identified optimal conditions of [NaClO] = 800 µM, [AMX] = 35 mg L−1, [FeSO4] = 5 mg L−1, and pH = 3, yielding 91.8% removal. A hybrid RSM, Artificial Neural Networks (ANN), and Genetic Algorithm (GA) modeling approach further enhanced performance, predicting 99% efficiency (R2 = 0.991) and achieving 97% removal under refined conditions (AMX concentration of 34.85 mg L−1, [NaClO] = 508.4 µM, [FeSO4] = 7.83 mg L−1, and a pH of 3.14). Green-synthesized IONPs nanoparticles supplemented the homogeneous system for by-products formation and toxicity assessment experiments, achieving 99.9% AMX removal within 60 min (k = 0.114 min−1) when combined with hydroxylamine. Trace and ultra-trace analysis using QuEChERS extraction coupled with Direct Infusion-High Resolution Mass Spectrometry (DI-HRMS) enabled quantification of the parent compound and seven transformation by-products. Ecotoxicity testing with Chlorella vulgaris demonstrated transient inhibition followed by substantial detoxification.