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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. 

Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. The sheer number of synthesized (plus potentially synthesizable) MOF structures requires researchers pursue computational techniques to screen and isolate MOF candidates. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis. We present challenges and case studies about (1) construction of a MOF knowledge graph (MOF-KG) from structured and unstructured sources and (2) leveraging the MOF-KG for discovery of new or missing knowledge. 

J-dimer emission is an emergent property that occurs when pairs of ground state fluorophores associate, typically in a dilute solution medium. The resulting fluorescence is shifted with respect to the monomer. J-dimer emission, however, has never been observed in concentrated dispersions or in the solid state. We posited that multivariate (MTV) MOFs with double interwoven structures would help to isolate these dimers within their crystalline matrix. Using this strategy, J-aggregate density was controlled during crystallization by following a substitutional solid solution approach. Here, we identified the presence of J-dimers over the entire composition range for interwoven PIZOF-2 / NNU-28 structures with variable amounts of a diethynyl-anthracene aggregate-forming link. We produced bulk crystals that systematically shifted their fluorescence from green to red with lifetimes (up to 13 ns) and quantum yields (up to 76%) characteristic of π–π stacked aggregates. Photophysical studies also revealed an equilibrium constant of dimerization, K D = 1.5 ± 0.3 M −1 , enabling the first thermodynamic quantification of link–link interactions that occur during MOF assembly. Our findings elucidate the role that supramolecular effects play during crystallization of MTV MOFs, opening pathways for the preparation of solid-state materials with solution-like properties by design. 

The hydrogen-bonding environments at the COOH moiety in eight polycrystalline polymorphs of palmitic acid are explored using solid-state NMR. Although most phases have no previously reported crystal structure, measured 13 C chemical shift tensors for COOH moieties, combined with DFT modeling establish that all phases crystallize with a cyclic dimer ( R 22(8)) hydrogen bonding arrangement. Phases A 2 , B m and E m have localized OH hydrogens while phase C has a dynamically disordered OH hydrogen. The phase designated A s is a mix of five forms, including 27.4% of B m and four novel phases not fully characterized here due to insufficient sample mass. For phases A 2 , B m , E m , and C the anisotropic uncertainties in the COOH hydrogen atom positions are established using a Monte Carlo sampling scheme. Sampled points are retained or rejected at the ±1 σ level based upon agreement of DFT computed 13 COOH tensors with experimental values. The collection of retained hydrogen positions bear a remarkable resemblance to the anisotropic displacement parameters ( i.e. thermal ellipsoids) from diffraction studies. We posit that this similarity is no mere coincidence and that the two are fundamentally related. The volumes of NMR-derived anisotropic displacement ellipsoids for phases with localized OH hydrogens are 4.1 times smaller than those derived from single crystal X-ray diffraction and 1.8 times smaller than the volume of benchmark single crystal neutron diffraction values.