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<p>The increasing presence of herbicides in water sources poses significant environmental and health risks, necessitating efficient removal strategies. This study explores the adsorption performance of NH<sub>2</sub>-UiO-66(Zr), a metal–organic framework, for removing 2-methyl-4-chlorophenoxyacetic acid (MCPA) and 3,6-dichloro-2-methoxy benzoic acid (Dicamba) from aqueous solutions. Response surface methodology was employed to optimize adsorption conditions, identifying optimal parameters for MCPA (25&#xa0;°C, 25&#xa0;min, 30&#xa0;mg NH<sub>2</sub>-UiO-66(Zr), 30&#xa0;mg/L MCPA) and Dicamba (30&#xa0;°C, 15&#xa0;min, 20&#xa0;mg NH<sub>2</sub>-UiO-66(Zr), 40&#xa0;mg/L Dicamba), achieving removal efficiencies of 98.3% and 98.1%, respectively. Artificial neural network models (4-6-1 for MCPA, 4-7-1 for Dicamba) validated these results, demonstrating high predictive accuracy. Molecular docking analysis revealed a slightly stronger binding affinity of NH<sub>2</sub>-UiO-66(Zr) for Dicamba, though the difference in binding energy was minimal. Adsorption isotherm studies indicated that the Freundlich model best described the process, suggesting a heterogeneous, reversible, and multilayer adsorption mechanism. These findings highlight NH<sub>2</sub>-UiO-66(Zr) as a promising adsorbent for herbicide removal, offering a sustainable approach to mitigating water contamination.</p>

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Experimental design and modelling of adsorption process for organic pollutants removal by NH2-UiO-66(Zr) metal–organic framework

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • K. Jumbri,
  • N. F. Hamidon,
  • Hayati M. Mukhair,
  • N. N. Lizman,
  • N. A. F. Abdullah,
  • M. S. Hossain,
  • H. F. Mohd Zaid,
  • H. A. Isiyaka,
  • R. A. Wahab,
  • M. B. Abdul Rahman

摘要

The increasing presence of herbicides in water sources poses significant environmental and health risks, necessitating efficient removal strategies. This study explores the adsorption performance of NH2-UiO-66(Zr), a metal–organic framework, for removing 2-methyl-4-chlorophenoxyacetic acid (MCPA) and 3,6-dichloro-2-methoxy benzoic acid (Dicamba) from aqueous solutions. Response surface methodology was employed to optimize adsorption conditions, identifying optimal parameters for MCPA (25 °C, 25 min, 30 mg NH2-UiO-66(Zr), 30 mg/L MCPA) and Dicamba (30 °C, 15 min, 20 mg NH2-UiO-66(Zr), 40 mg/L Dicamba), achieving removal efficiencies of 98.3% and 98.1%, respectively. Artificial neural network models (4-6-1 for MCPA, 4-7-1 for Dicamba) validated these results, demonstrating high predictive accuracy. Molecular docking analysis revealed a slightly stronger binding affinity of NH2-UiO-66(Zr) for Dicamba, though the difference in binding energy was minimal. Adsorption isotherm studies indicated that the Freundlich model best described the process, suggesting a heterogeneous, reversible, and multilayer adsorption mechanism. These findings highlight NH2-UiO-66(Zr) as a promising adsorbent for herbicide removal, offering a sustainable approach to mitigating water contamination.