Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.绝大多数新兴关注污染物（contaminants of emerging concern）均为极性及/或可离子化有机化合物，此类物质在人工工程系统与自然环境中的去除难度极高。碳基吸附剂（carbonaceous sorbents）涵盖活性炭、生物炭、富勒烯与碳纳米管等品类，其应用场景包括饮用水过滤、污水处理及污染物修复。由于现有吸附模型均针对中性化合物开发，当前缺乏可预测多数新兴关注污染物在这类吸附剂上吸附行为的工具，因此研究人员与工程实践者均亟需一种可基于吸附性能预测能力，为特定污染物筛选适配吸附剂的方法。本研究提出一种普适性极强的深度学习神经网络方法，可精准预测常规使用的弗伦德里希吸附等温线（Freundlich isotherm）拟合参数log KF与n（log KF的决定系数R²＞0.98，n的决定系数R²＞0.91）。该神经网络模型所采用的参数，既包含碳基吸附剂的通用常规参数，也包含可从在线数据库免费获取的公开参数。本研究同时提供了一款可免费访问的图形用户界面。