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Abstract Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivitykin MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm⁻³), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m⁻¹ K⁻¹), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m⁻¹ K⁻¹), the structures of which we describe in additional detail. 

The stability of metal–organic frameworks (MOFs) in water affects their ability to function as chemical catalysts, their capacity as adsorbents for separations in water vapor presence, and their usefulness as recyclable water harvesters. Here, we have examined water stability of four node-modified variants of the mesoporous MOF, NU-1000, namely formate-, Acac-, TFacac-, and Facac-NU-1000, comparing these with node-accessible NU-1000. These NU-1000 variants present ligands grafted to NU-1000's hexa-Zr( iv )-oxy nodes by displacing terminal aqua and hydroxo ligands. Facac-NU-1000, containing the most hydrophobic ligands, showed the greatest water stability, being able to undergo at least 20 water adsorption/desorption cycles without loss of water uptake capacity. Computational studies revealed dual salutary functions of installed Facac ligands: (1) enhancement of framework mechanical stability due to electrostatic interactions; and (2) transformation and shielding of the otherwise highly hydrophilic nodes from H-bonding interactions with free water, presumably leading to weaker channel-stressing capillary forces during water evacuation – consistent with trends in free energies of dehydration across the NU-1000 variants. Water harvesting and hydrolysis of chemical warfare agent simulants were examined to gauge the functional consequences of modification and mechanical stabilization of NU-1000 by Facac ligands. The studies revealed a harvesting capacity of ∼1.1 L of water vapor per gram of Facac-NU-1000 per sorption cycle. They also revealed retention of catalytic MOF activity following 20 water uptake and release cycles. This study provides insights into the basis for node-ligand-engendered stabilization of wide-channel MOFs against collapse during water removal. 

Building a knowledge graph is a time-consuming and costly process which often applies complex natural language processing (NLP) methods for extracting knowledge graph triples from text corpora. Pre-trained large Language Models (PLM) have emerged as a crucial type of approach that provides readily available knowledge for a range of AI applications. However, it is unclear whether it is feasible to construct domain-specific knowledge graphs from PLMs. Motivated by the capacity of knowledge graphs to accelerate data-driven materials discovery, we explored a set of state-of-the-art pre-trained general-purpose and domain-specific language models to extract knowledge triples for metal-organic frameworks (MOFs). We created a knowledge graph benchmark with 7 relations for 1248 published MOF synonyms. Our experimental results showed that domain-specific PLMs consistently outperformed the general-purpose PLMs for predicting MOF related triples. The overall benchmarking results, however, show that using the present PLMs to create domain-specific knowledge graphs is still far from being practical, motivating the need to develop more capable and knowledgeable pre-trained language models for particular applications in materials science.