Photo: Part of the research team involved in this study. Left to right: Dr. Haiyan Mao, Dr. Sophia Fricke (PMRC post-doctoral fellow), Dr. Tamires Menezes, Dr. Katilla Costa Santos.
Nanoporous materials are of significant interest for applications such as catalysis, chemical separations, and energy storage. The performance of these materials is highly dependent on their pore sizes, which are challenging to determine efficiently using conventional gas adsorption isotherms. In this study, the authors leverage established machine learning techniques to directly correlate time-domain NMR signals with Brunauer–Emmett–Teller (BET) surface areas for a set of metal-organic frameworks (MOFs) imbibed with solvents at varying concentrations. This approach dramatically accelerates the characterization of MOF porosity, a key property for the development of materials for carbon capture and other advanced applications.
By integrating machine learning with benchtop NMR relaxometry, researchers from the Reimer group introduced a rapid and accurate screening method that is approximately 1,440 times faster than traditional gas adsorption measurements. This technique provides a high-throughput, non-destructive method for assessing porosity in as little as one minute. Moreover, the NMR-machine learning approach is straightforward to perform and can be seamlessly integrated into synthetic workflows within standard laboratory settings. Future advancements will expand this method to characterize pore shapes, chemical affinities, and the presence of multiple pore types, further enhancing its utility in materials science.