机器学习辅助解析共价有机框架材料吸附铀酰的结构-机制关系★

doi:10.6023/A26060214

摘要/Abstract

共价有机框架（COFs）因其结构可设计、孔道有序和官能团可调等特点,在铀酰离子（UO₂²⁺）吸附与分离中展现出重要应用潜力。然而,现有研究多集中于单一材料的性能提升,对COFs结构特征与铀酰吸附机制之间的关联规律仍缺乏系统认识。本文基于文献数据构建了包含105组有效样本的COFs-铀酰吸附数据集,将文献报道的吸附机制归纳为配位作用、氧化还原作用、配位-氧化还原耦合作用、配位-静电耦合作用和弱相互作用五类,并结合统计分析、机器学习分类和SHAP可解释性分析,系统考察了COFs结构参数与铀酰吸附机制之间的关联。结果表明,配位相关作用是COFs吸附铀酰的主导路径,占比超过80%,表明含N/O配位位点及孔道可接近性是影响铀酰富集的关键因素。SHAP分析进一步表明,带隙、孔径和比表面积是模型进行机制判别时贡献最高的变量,其中低带隙样本更倾向于对应氧化还原相关机制,适中孔径和较高比表面积与铀酰离子的孔道可达性及配位位点暴露程度密切相关,而骨架有序性可能通过影响孔道连通性和界面电荷分布,参与静电辅助富集过程。本文从数据驱动角度揭示了COFs结构特征与铀酰吸附机制之间的关联规律,为COFs铀酰吸附材料的机制识别和理性设计提供了参考。

关键词: 机器学习, 共价有机框架, 铀酰离子, 吸附材料

Covalent organic frameworks (COFs) have shown significant potential in the adsorption and separation of uranyl ions (UO₂²⁺) due to their structural designability, ordered pore architectures, and tunable functional groups. However, existing research has largely focused on improving the performance of individual materials, while a systematic understanding of the relationship between COF structural features and uranyl adsorption mechanisms remains limited. In this study, we constructed a COF-uranyl adsorption dataset comprising 105 valid samples based on literature data. We standardized the adsorption mechanisms reported in the literature into five types: coordination, redox, coordination-redox coupling, coordination-electrostatic coupling, and weak interactions. By integrating statistical analysis, machine learning classification, and SHAP analysis, we systematically investigated the associations between COF structural parameters and uranyl adsorption mechanisms. The results showed that coordination-related interactions were dominant in uranyl adsorption by COFs, accounting for over 80% of the samples, indicating the importance of nitrogen- and oxygen-containing coordination sites and pore accessibility in uranyl enrichment. SHAP analysis further showed that band gap, pore size, and specific surface area made substantial contributions to model-based mechanism classification. Samples with lower band gaps were more likely to be classified as involving redox-related mechanisms, while moderate pore sizes and higher specific surface areas were closely associated with pore accessibility and the exposure of coordination sites. Framework ordering may also contribute to electrostatically assisted enrichment by affecting pore connectivity and interfacial charge distribution. This work reveals data-driven associations between COF structural features and uranyl adsorption mechanisms, providing valuable insights into mechanism identification and the rational design of COF-based uranyl adsorbents.

Key words: machine learning, covalent organic frameworks, uranyl ion, adsorption material

引用此文

孙士博, 王胜, 王翰洋, 蓝晨栝, 马静, 王其召, 孙振丽, 王祥科. 机器学习辅助解析共价有机框架材料吸附铀酰的结构-机制关系^★[J]. 化学学报, doi: 10.6023/A26060214.

Sun Shibo, Wang Sheng, Wang Hanyang, Lan Chenkuo, Ma Jing, Wang Qizhao, Sun Zhenli, Wang Xiangke. Machine Learning-assisted Elucidation of the Adsorption Structure-mechanism Relationship of Uranyl on Covalent Organic Frameworks[J]. Acta Chimica Sinica, doi: 10.6023/A26060214.

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