Reporting in the Journal Applied Materials & Interfaces (2018, 10, 4, 3668–3679; https://pubs.acs.org/doi/10.1021/acsami.7b18037), Seda Keskin and team at the Koc University and Ozyegin University, Istanbul, Turkey, used computational methods to overcome the challenge presented by the impracticality of experimentally identifying the ever-increasing number of potential MOF materials exhibiting high gas separation potential.
High-Throughput Computational Screening to Identify Candidates for Target Gas Separation
The researchers identified the best candidates for target gas separation (CH4/H2 separation) using high-throughput screening of MOFs in the Cambridge Structural Database (CSD) to focus future experimental investigations. Using the correlations observed from this analysis, they were also able to propose a model to predict future MOFs for the same application; again allowing almost endless experimental options to be prioritized.
Results showed that MOFs, due to their higher selectivities and working capacities, can outperform traditional adsorbent materials such as zeolites, activated alumina, silica gel, and carbon-based materials in CH4/H2 separations.
Materials informatics can greatly aid MOF research and discovery, identifying many potential candidates from the plethora of predicted MOF structures for further investigation in wide ranging applications.
By focusing experimental work, predicting results, and searching the known landscape, materials informatics will increasingly allow these materials to reach their full potential.
Download our e-book on the applications of informatics to MOFs research and development.
Read more case studies about CCDC data and software being used in industry and academia.
Learn more about the Cambridge Structural Database (CSD) and how the validated, curated data of 1.1M+ crystal structures can aid your research.