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<p>Anion-exchange resins are widely used for removing anionic micropollutants from aqueous environments, yet predictive understanding across structurally diverse pollutants remains limited. To address this gap, we present the first combined experimental–computational QSAR framework for anion-exchange resins that explicitly incorporates a concentration-dependent descriptor, namely activity degree of the ion (log&#xa0;<i>α</i>). Adsorption isotherms of 26 anionic compounds were systematically measured on Amberjet™ 4200 at multiple initial concentrations, providing a robust experimental dataset. Two complementary descriptor sets were employed for model development: (i) empirically derived linear free energy relationship (LFER) parameters and (ii) in silico-calculated COSMOtherm descriptors. Incorporating log α into both frameworks substantially improved accuracy, reflecting the critical role of ionic strength and activity effects in adsorption processes. The optimized models achieved excellent predictive power, with training <i>R</i><sup>2</sup> values &gt; 0.93 and external validation using test set yielding <i>R</i><sup>2</sup> = 0.938 (SE = 0.193 log units) for the LFER-based model and <i>R</i><sup>2</sup> = 0.953 (SE = 0.150 log units) for the COSMOtherm-based model. Analysis of LFER descriptor contributions revealed that the excess molar refractivity term had a negative coefficient—suggesting that electronic lone pairs, hydrogen-bond acidity, and volume-related lipophilic effects exert a repulsive influence on adsorption—whereas polar interaction and hydrogen-bond basicity terms showed positive coefficients, indicating that these interactions enhance adsorption affinity. Comparative analysis further indicated that COSMOtherm descriptors more effectively captured electronic and solvation effects, whereas LFER descriptors provided clearer mechanistic interpretability. This study establishes a versatile framework for predictive evaluation of anionic micropollutant adsorption, providing mechanistic insights and supporting the preliminary assessment of adsorbent suitability for structurally novel or data-scarce pollutants.</p>

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Anionic Micropollutant Removal by Ion-Exchange Resin Amberjet4200: Experimental Evaluation and QSAR Modeling

  • 期刊论文

  • 发表日期：

    2025年9月20日

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  • Min-Kyeong Seon,
  • Si-Hyeon Park,
  • Jeong-Min Cheon,
  • Chul-Woong Cho

摘要

Anion-exchange resins are widely used for removing anionic micropollutants from aqueous environments, yet predictive understanding across structurally diverse pollutants remains limited. To address this gap, we present the first combined experimental–computational QSAR framework for anion-exchange resins that explicitly incorporates a concentration-dependent descriptor, namely activity degree of the ion (log α). Adsorption isotherms of 26 anionic compounds were systematically measured on Amberjet™ 4200 at multiple initial concentrations, providing a robust experimental dataset. Two complementary descriptor sets were employed for model development: (i) empirically derived linear free energy relationship (LFER) parameters and (ii) in silico-calculated COSMOtherm descriptors. Incorporating log α into both frameworks substantially improved accuracy, reflecting the critical role of ionic strength and activity effects in adsorption processes. The optimized models achieved excellent predictive power, with training R2 values > 0.93 and external validation using test set yielding R2 = 0.938 (SE = 0.193 log units) for the LFER-based model and R2 = 0.953 (SE = 0.150 log units) for the COSMOtherm-based model. Analysis of LFER descriptor contributions revealed that the excess molar refractivity term had a negative coefficient—suggesting that electronic lone pairs, hydrogen-bond acidity, and volume-related lipophilic effects exert a repulsive influence on adsorption—whereas polar interaction and hydrogen-bond basicity terms showed positive coefficients, indicating that these interactions enhance adsorption affinity. Comparative analysis further indicated that COSMOtherm descriptors more effectively captured electronic and solvation effects, whereas LFER descriptors provided clearer mechanistic interpretability. This study establishes a versatile framework for predictive evaluation of anionic micropollutant adsorption, providing mechanistic insights and supporting the preliminary assessment of adsorbent suitability for structurally novel or data-scarce pollutants.