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Author

Rachel B. Getman

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Aug 2026

Electrocatalytic Hydrogenation with Nanoparticles Derived from a Cobalt Metal-Organic Framework.

Electrocatalytic hydrogenation (e -H) provides a sustainable route for converting unsaturated organic substrates under mild conditions using renewable electricity as the driving force. Here, we report an MOF-derived cobalt catalyst for the e -H of acetone and pyridine. A new two-dimensional cobalt metal-organic framework, Co-L0-NS, composed of Co(II) nodes and polyaromatic carboxylate linkers, was synthesized as nanosheets and used as a precursor to generate the active catalyst under cathodic bias. Electrochemical pretreatment induces controlled framework reconstruction to form MD-Cat, a highly dispersed, structurally disordered, Co(OH)2-rich nanocluster material. MD-Cat catalyzes the e -H of acetone to isopropanol with nearly quantitative Faradaic efficiency at optimized potentials and promotes pyridine hydrogenation to piperidine with up to 50% Faradaic efficiency. Comparative studies with electrodeposited cobalt, commercial cobalt nanoparticles, and bulk Co(OH)2 show that the MOF-derived catalyst exhibits superior current densities and product selectivity, which we attribute to its nanoscale morphology and hydroxylated cobalt environment. In situ Co K-edge XAS, XPS, PXRD, ATR-SEIRAS, and STEM analyses indicate that Co remains predominantly in the +2 oxidation state during catalysis while undergoing structural reorganization. Tafel analysis supports a PCET-type mechanism for acetone hydrogenation; while DFT calculations suggest that the Co/Co(OH)2 interface suppresses HER by weakening H* binding while preserving organic-substrate activation. These results highlight MOF-templated electrochemical reconstruction as a promising approach for designing selective e -H catalysts, not only by increasing catalyst accessibility through nanostructuring but also by enabling the formation of unique catalytic motifs that would otherwise be difficult to access using traditional methods.

B. K. Behera, Xin Zheng, Haomiao Xie et al. · 0 citations
Open access Jul 2026

Experimentally Tuned Protein-RNA Rosetta Score Function using Bayesian Optimization

Protein-RNA complexes drive fundamental cellular processes such as transcription and translation. Despite the prevalence and importance of protein-RNA interactions, the field lacks reliable and accessible methods to quantify the energetic favorability of these interactions. We propose an experimentally tuned protein-RNA score function that can be directly implemented into ROSETTA. Fine-tuning these score functions for predictive tasks requires repeated evaluations on a set of protein-RNA complexes, which can be computationally expensive given the number of parameters to tune. We used Bayesian Optimization to efficiently improve the energetic agreement between ROSETTA and experimentation. We observe significant interactions for specific RNA subclasses, serving as further confirmation of the physical validity of the score function. Beyond protein-RNA interaction prediction, we establish a framework to efficiently fine-tune ROSETTA score functions for any protein-class interaction using Bayesian Optimization. TOC FIGURE

Joe Bailey, Nathan Phan, Søren C. Spina et al. · 0 citations