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.
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, 0
: 26060214
-26060214
.
DOI: 10.6023/A26060214
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