Rational design of hydrophobic MOFs for membrane-based C2H6-selective separation under high humidity condition

The development of ethane-selective permeation materials is essential for energy-efficient C₂H₆/C₂H₄ separation. However, designing high-performance C₂H₆-selective MOFs remains highly challenging, particularly when used under humid conditions. In this work, hydrophobic MOFs were initially identified using the Widom insertion method. Their features and separation properties calculated through molecular simulations were subsequently utilized as inputs for the development of machine learning (ML) models to identify the key factors that influence the performance of membrane-based C₂H₆/C₂H₄ separation. Based on ML insights, 11,973 hypothetical MOFs (hMOFs) were constructed, and 1229 hydrophobic hMOFs were further identified through a preliminary screening. The four high-performing hMOF membranes were further identified through precision screening, and hMOF-1 demonstrates effective C₂H₆/C₂H₄ separation under high humidity conditions, with performance comparable to that observed under dry conditions. Additionally, the gas separation mechanisms for high-performing hMOFs were also elucidated under both dry and humidity conditions. Consequently, the ML-guided design of hydrophobic MOF membranes demonstrates significant potential for membrane-based C₂H₆-selective separation, especially when considering the impact of humidity conditions on practical C₂H₆/C₂H₄ separation applications.