Zhang Zhiming, Palakkal Athulya Surendran, Wu Xiaoyu, Jiang Jianwen, Jiang Zhongyi
Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, PR China.
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore.
Environ Sci Technol. 2025 May 13;59(18):9123-9133. doi: 10.1021/acs.est.5c00768. Epub 2025 May 2.
The rapid increase in atmospheric CO, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO emissions from flue gases. However, many MOFs suffer from structural degradation and performance deterioration upon exposure to water in flue gases. Aiming to discover stable and efficient MOFs for CO capture from a wet flue gas, we propose a hierarchical high-throughput computational screening (HTCS) strategy. Machine learning (ML)-assisted stability analysis is incorporated within the HTCS, leveraging prior experimental experience to predict ultrastable (including water-, thermal-, and activation-stable) MOFs from ∼280,000 candidates in the ab initio REPEAT charge MOF (ARC-MOF) database. Among 9755 shortlisted MOFs, molecular simulations identify 1000 top-performing MOFs. Remarkably, several vanadium-based MOFs are revealed to be ultrastable, exhibiting high CO capture capability of 3-7 mmol/g and CO/N selectivity of 95-401. Subsequently, ML regressors are developed to derive design principles for MOFs capable of overcoming the trade-off effect. Furthermore, an ML classifier is developed to analyze the impact of water on CO capture by comparing dry and wet conditions. The proposed hierarchical HTCS and developed ML models lay a solid foundation for the potential transition of MOFs into practical applications.
人为源导致的大气中二氧化碳迅速增加，对全球气候构成了严重威胁，并引发了广泛的环境关注。金属有机框架材料（MOFs）是很有前景的吸附剂，有望减少烟道气中的二氧化碳排放。然而，许多MOFs在接触烟道气中的水后会出现结构降解和性能恶化。为了发现用于从湿烟道气中捕获二氧化碳的稳定且高效的MOFs，我们提出了一种分层高通量计算筛选（HTCS）策略。机器学习（ML）辅助的稳定性分析被纳入HTCS中，利用先前的实验经验从从头算重复电荷MOF（ARC-MOF）数据库中的约280,000个候选材料中预测超稳定（包括水稳定、热稳定和活化稳定）的MOFs。在9755个入围的MOFs中，分子模拟确定了1000个性能最佳的MOFs。值得注意的是，几种钒基金属有机框架材料被发现是超稳定的，表现出3 - 7 mmol/g的高二氧化碳捕获能力和95 - 401的二氧化碳/氮气选择性。随后，开发了ML回归模型来推导能够克服权衡效应的MOFs的设计原则。此外，还开发了一个ML分类器，通过比较干燥和潮湿条件来分析水对二氧化碳捕获的影响。所提出的分层HTCS和开发的ML模型为MOFs向实际应用的潜在转变奠定了坚实的基础。
