Splitting of Hydrogen Atoms into Proton-Electron Pairs at BaO-Ru Interfaces for Promoting Ammonia Synthesis under Mild Conditions.

Ru catalysts promoted with alkali and alkaline earth have shown superior ammonia (NH3) synthesis activities under mild conditions. Although these promoters play a vital role in enhancing catalytic activity, their function has not been clearly understood. Here, we synthesize a series of Ba-Ru/MgO catalysts with an optimal Ru particle size (∼2.3 nm) and tailored BaO-Ru interfacial structures. We discover that the promoting effect is created through the separate storage of H+/e- pairs at the BaO-Ru interface. Chemisorbed H atoms on Ru dissociate into H+/e- pairs at the BaO-Ru interface, where strongly basic, nonreducible BaO selectively captures H+ while leaving e- on Ru. The resulting electron accumulation in Ru facilitates N2 activation via enhanced π-backdonation and inhibits hydrogen poisoning during NH3 synthesis. Consequently, the formation of intimate BaO-Ru interface without an excessive loss of accessible Ru sites enables the synthesis of highly active catalysts for NH3 synthesis.

Hierarchical LTL Zeolite as an Efficient and Sustainable Solid Acid Catalyst for Replacing HCl in the Production of Polyurethane Intermediates.

In many industrially important reactions, caustic mineral acid catalysts have been successfully replaced with green solid acids such as zeolites. In this context, extensive efforts have been devoted to replacing HCl to produce methylenedianiline (MDA), a key intermediate in polyurethane production. Unfortunately, limited success has been achieved thus far due to low activity, selectivity towards the desired 4,4'-MDA, and rapid catalyst deactivation. Here we report that meso-/microporous hierarchical LTL zeolite exhibits unprecedentedly high activity, selectivity, and stability. The one-dimensional cage-like micropores of LTL promote the bimolecular reaction between two para-aminobenzylaniline intermediates to selectively produce 4,4'-MDA and inhibit the formation of undesired isomers and heavy oligomers. Meanwhile, the secondary mesopores alleviate mass transfer limitations, resulting in a 7.8-fold higher MDA formation rate compared to solely microporous LTL zeolite. Due to suppressed oligomer formation and fast mass transfer, the catalyst exhibits inappreciable deactivation in an industrially relevant continuous flow reactor.

Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation.

Biological experiments for developing efficient cancer therapeutics require significant resources of time and costs particularly in acquiring biological image data. Thanks to recent advances in AI technologies, there have been active researches in generating realistic images by adapting artificial neural networks. Along the same lines, this paper proposes a learning-based method to generate images inheriting biological characteristics. Through a statistical comparison of tumor penetration metrics between generated images and real images, we have shown that forged micrograph images contain vital characteristics to analyze tumor penetration performance of infecting agents, which opens up the promising possibilities for utilizing proposed methods for generating clinically meaningful image data.