Lifeng Ding

The separation of hydrogen isotopes for applications such as nuclear fusion is a major challenge. Current technologies are energy intensive and inefficient. Nanoporous materials have the potential to separate hydrogen isotopes by kinetic quantum sieving, but high separation selectivity tends to correlate with low adsorption capacity, which can prohibit process scale-up. In this study, we use organic synthesis to modify the internal cavities of cage molecules to produce hybrid materials that are excellent quantum sieves. By combining small-pore and large-pore cages together in a single solid, we produce a material with optimal separation performance that combines an excellent deuterium/hydrogen selectivity (8.0) with a high deuterium uptake (4.7 millimoles per gram).

https://www.science.org/doi/full/10.1126/science.aax7427

The use of computational screening for discovering functional materials has become crucial. However, handling the large amount of data generated by such screening studies is still a challenge. In this study, 1087 metal-organic frameworks (MOFs) were computationally screened for capturing trace amounts of methyl iodide. The researchers developed a simple method to analyze the complex data obtained from the screening. They used unsupervised learning to create 2D maps of the high-dimensional data, making it easier to interpret and identify top-performing MOFs. These maps also allowed for the clustering of similar MOFs, removing the need for laborious visual inspection. Additionally, the researchers found that the structure-function maps could be used to predict the performance of MOFs for capturing trace amounts of methyl iodide, achieving high accuracies when applied to a different set of MOFs.

https://pubs.acs.org/doi/full/10.1021/acsami.2c10861

Metal-organic frameworks (MOFs) are highly versatile due to their rich chemical information, leading to successful applications. Identifying the best MOFs for specific tasks requires a thorough assessment of their chemical attributes. A new deep learning model, called the MOF-GRU model, uses a text representation of MOFs to predict gas separation performance. It shows superior predictive accuracy and can handle large data sets effectively. This model can uncover performance relationships without detailed 3D structural information, speeding up the discovery of high-performance materials for gas separation applications.

https://pubs.acs.org/doi/full/10.1021/acsami.3c11790

In recent years, the issue of climate change, with carbon dioxide (CO₂) emissions being a significant contributor, has gathered widespread attention. To address this increasingly urgent issue, efforts being made focus on reducing CO₂ emissions through advanced technologies like metal-organic frameworks (MOFs) based approach. These frameworks have high surface area and adaptable properties that can facilitate CO₂ capture. However, to broaden their potential applications, it is essential to enhance their functionalities, such as introducing new molecular units to create innovative frameworks. There's also interest in creating light-responsive frameworks capable of capturing CO₂ using visible light, rather than harmful UV light, to increase efficiency and sustainability. This research aims to explore the potential of MOFs as a platform for CO₂ capture and develop light-responsive frameworks for energy-efficient CO₂ capture approaches.

https://www.sciencedirect.com/science/article/pii/S1385894724017418