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<p>Fluoride contamination in groundwater poses severe health risks, necessitating innovative solutions for its removal. This study explores the synthesis of cerium oxide nanoparticles using betel leaf extract i.e. Betel Nano Ceria (BNC), leveraging its phytochemicals, such as hydroxychavicol and chavibetol, as natural reducing and capping agents. The fluoride removal efficiency of nanoparticle was evaluated through fixed-bed column studies under varying operational conditions: flow rate, initial fluoride concentration, and bed height. Key performance indicators, including adsorption capacity and removal efficiency, were optimized using mathematical models (Thomas, Yoon-Nelson, and Adams-Bohart) and Artificial Neural Network (ANN) modeling. The ANN model validated the experimental findings with high accuracy (R<sup>2</sup> &gt; 0.99), offering predictive insights for scalable water treatment. Comprehensive characterization of BNC using techniques like GC–MS, SEM–EDS, XRD, FTIR, TGA, BET and Zeta potential elucidated their structural and functional attributes, critical for adsorption mechanisms. This research underscores the potential of BNC as eco-friendly adsorbent for maximum fluoride removal efficiency of 89.1% with 37.16&#xa0;mg/g capacity in fixed-bed column study. The nonlinear Langmuir isotherm exhibits excellent performance with maximum adsorption capacity of 148.89&#xa0;mg/g. It integrates green synthesis and advanced modeling, paving the way for sustainable and efficient water purification technologies adaptable to real-world applications.</p> Graphical Abstract <p></p>

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Betel Leaf Nano Ceria for Fluoride Removal from Water: A Synergistic Approach Involving Fixed-Bed Column Adsorption and Artificial Neural Network Optimization

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Sunil Kumar Gochhi,
  • Anil Kumar Giri,
  • Surjendu Kumar Dey

摘要

Fluoride contamination in groundwater poses severe health risks, necessitating innovative solutions for its removal. This study explores the synthesis of cerium oxide nanoparticles using betel leaf extract i.e. Betel Nano Ceria (BNC), leveraging its phytochemicals, such as hydroxychavicol and chavibetol, as natural reducing and capping agents. The fluoride removal efficiency of nanoparticle was evaluated through fixed-bed column studies under varying operational conditions: flow rate, initial fluoride concentration, and bed height. Key performance indicators, including adsorption capacity and removal efficiency, were optimized using mathematical models (Thomas, Yoon-Nelson, and Adams-Bohart) and Artificial Neural Network (ANN) modeling. The ANN model validated the experimental findings with high accuracy (R2 > 0.99), offering predictive insights for scalable water treatment. Comprehensive characterization of BNC using techniques like GC–MS, SEM–EDS, XRD, FTIR, TGA, BET and Zeta potential elucidated their structural and functional attributes, critical for adsorption mechanisms. This research underscores the potential of BNC as eco-friendly adsorbent for maximum fluoride removal efficiency of 89.1% with 37.16 mg/g capacity in fixed-bed column study. The nonlinear Langmuir isotherm exhibits excellent performance with maximum adsorption capacity of 148.89 mg/g. It integrates green synthesis and advanced modeling, paving the way for sustainable and efficient water purification technologies adaptable to real-world applications.

Graphical Abstract