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In this work, we show how the combination of soluble non-polar and polar links allows for the preparation of multivariate metal–organic frameworks (MTV MOFs) that exhibit dipolar solid-solution behavior. 

In this contribution we demonstrate that metal–organic frameworks (MOFs) with suitable underlying topological structure are amenable for the preparation of MOF-based substitutional solid-solutions (SSS) that follow Vegard's law. 

Understanding the origin of enhanced catalytic activity is critical to heterogeneous catalyst design. This is especially important for non-noble metal-based catalysts, notably metal oxides, which have recently emerged as viable candidates for numerous thermal catalytic processes. For thermal catalytic reduction/hydrogenation using metal oxide nanoparticles, enhanced catalytic performance is typically attributed to an increased surface area and the presence of oxygen vacancies. Concomitantly, the treatments that induce oxygen vacancies also impact other material properties, such as the microstrain, crystallinity, oxidation state, and particle shape. Herein, multivariate statistical analysis is used to disentangle the impact of material properties of CuO nanoparticles on catalytic rates for nitroaromatic and methylene blue reduction. The impact of the microstrain, shape, and Cu(0) atomic percent is demonstrated for these reactions; furthermore, a protocol for correlating material property parameters to catalytic efficiency is presented, and the importance of catalyst design for these broadly utilized probe reactions is highlighted. 

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.