Kagalkar Abhishek, Dharaskar Swapnil, Chaudhari Nitin, Vakharia Vinay, Karri Rama Rao
Department of Chemical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382426, India.
Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382426, India.
Sci Rep. 2025 May 4;15(1):15563. doi: 10.1038/s41598-025-00324-8.
The efficacy of ZnO-MXene nanocomposites as extremely effective adsorbents for the removal of metal ions from wastewater is investigated in this work. The two-step chemical method used to create composites showed how temperature affected their shape. In the adsorption studies, a high removal efficiency of 97% for chromium, 91% for cadmium, 97% for lead, and 96% for arsenic were observed. While isotherm studies showed a stronger fit with the Freundlich model, indicating heterogeneous adsorption, adsorption kinetics followed a pseudo-second-order model. The spontaneity and viability of adsorption, which is dominated by chemisorption mechanisms, were validated by thermodynamic studies. Furthermore, adsorption performance was well predicted by machine learning models such as Random Forest (RF) and Support Vector Machine (SVM), with RF showing the highest accuracy. These results demonstrate that ZnO-MXene is a promising and reasonably priced nano adsorbent that can satisfy WHO water quality requirements. A sustainable wastewater treatment solution is provided by the combination of both experimental and predictive modelling techniques, which yield important insights into adsorption mechanisms.
本文研究了ZnO-MXene纳米复合材料作为从废水中去除金属离子的高效吸附剂的效能。用于制备复合材料的两步化学方法显示了温度如何影响其形状。在吸附研究中，观察到对铬的去除效率高达97%，对镉的去除效率为91%，对铅的去除效率为97%，对砷的去除效率为96%。虽然等温线研究表明与Freundlich模型拟合度更高，表明为非均相吸附，但吸附动力学遵循准二级模型。热力学研究验证了以化学吸附机制为主导的吸附的自发性和可行性。此外，机器学习模型如随机森林（RF）和支持向量机（SVM）能很好地预测吸附性能，其中RF显示出最高的准确性。这些结果表明，ZnO-MXene是一种有前景且价格合理的纳米吸附剂，能够满足世界卫生组织的水质要求。实验和预测建模技术相结合提供了一种可持续的废水处理解决方案，这对吸附机制产生了重要的见解。
