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<p>Adsorption remains a robust and cost-effective technique for mitigating dye contamination in aqueous environments. This study investigates a novel zinc oxide (ZnO)-reinforced carboxymethyl cellulose/<i>β</i>-cyclodextrin-epichlorohydrin-tetrafluoroterephthalonitrile polymer nanocomposite (CMC-<i>β</i>-CD-E-T/ZnO) for the adsorption of malachite green (MG) dye from water. Morphological characterization via field emission scanning electron microscopy (FE-SEM) revealed a wrinkled surface topology, while N₂ adsorption–desorption isotherm analysis indicated a low Brunauer–Emmett–Teller (BET) specific surface area (0.49 m<sup>2</sup>/g). Thermogravimetric analysis demonstrated that the incorporation of ZnO enhanced the thermal stability of the composite, with activation energy (<i>E</i><sub><i>k</i></sub>) values in the range of 181–211&#xa0;kJ&#xa0;mol<sup>−1</sup>, compared to 154–183&#xa0;kJ&#xa0;mol<sup>−1</sup> for CMC-<i>β</i>-CD-E-T alone. Adsorption studies revealed a maximum Langmuir adsorption capacity (<i>q</i><sub><i>m</i></sub>) of 654 ± 41&#xa0;mg&#xa0;g<sup>−1</sup> at 30&#xa0;°C, with adsorption kinetics conforming to a pseudo first order (PFO) model at high MG concentrations. Rapid adsorption kinetics (60–90&#xa0;min) is attributed to the negatively charged surface of the composite, which facilitated electrostatic interactions with the cationic MG dye molecules. Thermodynamic parameters suggested a spontaneous and endothermic adsorption process, with an enthalpy change (<i>∆H</i>°) of 25.8 ± 3.66&#xa0;kJ&#xa0;mol<sup>−1</sup>. However, competitive ion studies revealed a substantial decrease in adsorption capacity in the presence of divalent cations such as Ca<sup>2</sup>⁺ and Mg<sup>2</sup>⁺, likely due to site blocking and reduced electrostatic attraction. Predictive modeling using artificial neural networks (ANN) demonstrated high accuracy in forecasting adsorption capacities under varying operational conditions. Despite its initial high performance, the adsorption capacity of the composite decreased significantly after repeated regeneration-reuse cycles, primarily due to the progressive blocking and saturation of active sites at elevated MG concentrations.</p> Graphical abstract <p></p>

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ZnO reinforced carboxymethyl cellulose/β-cyclodextrin nanocomposite for malachite green adsorption: isotherm, kinetics, and thermodynamic evaluation

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Ruksana Sirach,
  • Pragnesh N. Dave

摘要

Adsorption remains a robust and cost-effective technique for mitigating dye contamination in aqueous environments. This study investigates a novel zinc oxide (ZnO)-reinforced carboxymethyl cellulose/β-cyclodextrin-epichlorohydrin-tetrafluoroterephthalonitrile polymer nanocomposite (CMC-β-CD-E-T/ZnO) for the adsorption of malachite green (MG) dye from water. Morphological characterization via field emission scanning electron microscopy (FE-SEM) revealed a wrinkled surface topology, while N₂ adsorption–desorption isotherm analysis indicated a low Brunauer–Emmett–Teller (BET) specific surface area (0.49 m2/g). Thermogravimetric analysis demonstrated that the incorporation of ZnO enhanced the thermal stability of the composite, with activation energy (Ek) values in the range of 181–211 kJ mol−1, compared to 154–183 kJ mol−1 for CMC-β-CD-E-T alone. Adsorption studies revealed a maximum Langmuir adsorption capacity (qm) of 654 ± 41 mg g−1 at 30 °C, with adsorption kinetics conforming to a pseudo first order (PFO) model at high MG concentrations. Rapid adsorption kinetics (60–90 min) is attributed to the negatively charged surface of the composite, which facilitated electrostatic interactions with the cationic MG dye molecules. Thermodynamic parameters suggested a spontaneous and endothermic adsorption process, with an enthalpy change (∆H°) of 25.8 ± 3.66 kJ mol−1. However, competitive ion studies revealed a substantial decrease in adsorption capacity in the presence of divalent cations such as Ca2⁺ and Mg2⁺, likely due to site blocking and reduced electrostatic attraction. Predictive modeling using artificial neural networks (ANN) demonstrated high accuracy in forecasting adsorption capacities under varying operational conditions. Despite its initial high performance, the adsorption capacity of the composite decreased significantly after repeated regeneration-reuse cycles, primarily due to the progressive blocking and saturation of active sites at elevated MG concentrations.

Graphical abstract