Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data.

Journal: Environmental science & technology
Published Date: May 28, 2025

Abstract

The development of new adsorbents for water treatment often involves complex adsorption mechanisms, whose individual contributions are unclear, thereby limiting the understanding of adsorption driving forces, making it difficult to achieve precise design of adsorbents. Machine learning (ML) has been used to uncover the impacts of these mechanisms through feature engineering, but progress is limited by the data quality for training. Herein, we developed a universal ML strategy for precisely predicting the adsorption capacity of polymers for oxyanions and identifying the adsorption driving force based on the combination of experimental and published literature data. The adsorption mechanism was explored through classification of RDkit descriptors with different SHAP importance values, and electrostatic interaction was found to be the driving force in the oxyanion adsorption process, which was further verified by theoretical calculations, adsorption experiments, and effective targeted adsorbent design. In comparison, analysis relying on a separate literature data source led to decreased model performance, some biased conclusions, and invalid targeted adsorbent design. Overall, this study proposed a strategy for data set optimization as well as dominant mechanism identification, which could shed light on better treatment of oxyanions in wastewater.

Authors

  • Ling Yuan
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204, China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Hang Yu
  • Rongming Xu
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Weiming Zhang
    School of Clinical Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, China.
  • Yanyang Zhang
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Ming Hua
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Lu Lv
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Bingcai Pan
    State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China; Research Center for Environmental Nanotechnology (ReCENT), Nanjing University, Nanjing 210023, China.

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