School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
J Am Chem Soc. 2022 Mar 9;144(9):4071-4079. doi: 10.1021/jacs.1c13049. Epub 2022 Feb 16.
Type II porous liquids, comprising intrinsically porous molecules dissolved in a liquid solvent, potentially combine the adsorption properties of porous adsorbents with the handling advantages of liquids. Previously, discovery of appropriate solvents to make porous liquids had been limited to direct experimental tests. We demonstrate an efficient screening approach for this task that uses COSMO-RS calculations, predictions of solvent p values from a machine-learning model, and several other features and apply this approach to select solvents from a library of more than 11,000 compounds. This method is shown to give qualitative agreement with experimental observations for two molecular cages, CC13 and TG-TFB-CHEDA, identifying solvents with higher solubility for these molecules than had previously been known. Ultimately, the algorithm streamlines the downselection of suitable solvents for porous organic cages to enable more rapid discovery of Type II porous liquids.
II 型多孔液体由固有多孔分子溶解在液体溶剂中组成，可能将多孔吸附剂的吸附性能与液体的处理优势结合起来。以前，发现合适的溶剂来制备多孔液体仅限于直接实验测试。我们展示了一种针对此任务的有效筛选方法，该方法使用 COSMO-RS 计算、机器学习模型对溶剂 p 值的预测以及其他几个特性，并将该方法应用于从超过 11000 种化合物的库中选择溶剂。该方法被证明与两个分子笼 CC13 和 TG-TFB-CHEDA 的实验观察结果具有定性一致性，确定了对这些分子具有更高溶解度的溶剂，这超过了以前已知的溶剂。最终，该算法简化了多孔有机笼合适溶剂的选择过程，从而能够更快地发现 II 型多孔液体。
