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Precise atom mapping is crucial for data-driven reaction prediction, but currently lacks the required accuracy. Here, authors introduce a human-in-the-loop machine learning scheme for that purpose, and achieve high accuracy on a wide spectrum of reaction datasets.

To understand reactions in organic chemistry, ideally simple rules would help us predict the outcome of new reactions, but in reality such rules are not easily identified. Chen and Jung extract generalized reaction templates from data and show that they can be used in graph neural networks to predict the outcome of reactions and, despite simplification, still represent a high percentage of existing reactions.

Mesoporous metal–organic frameworks containing unsaturated Cr(III) sites are able to thermodynamically and selectively capture nitrogen from mixtures with oxygen and methane.

The stability of polymer electrolyte membrane fuel cells is limited by the degradation of the cathode catalyst during repetitive start-up/shut-down events — a parasitic oxygen reduction reaction on the anode causes an instantaneous potential jump at the cathode. The issue is now addressed by selectively suppressing the oxygen reduction reaction on the anode by exploiting the metal–insulator transition behaviour of Pt/H_xWO₃ catalysts.

Chemical interaction between metal and oxide supports is an important molecular-level factor that influences the catalytic selectivity of a desirable reaction. Here, using Pt nanowires/TiO₂ catalytic nanodiodes, the authors investigate an enhancement of both selectivity and hot electron generation on metal-oxide interfacial sites.

The real-time quantitative detection of hot electrons provides critical clues to understand the origin of the enhanced catalytic performance of bimetallic nanoparticles (NPs). Here, the authors investigate hot electrons generated on bimetallic PtCo NPs during H₂ oxidation by measuring the chemicurrent on a catalytic nanodiode.

Porous materials are well studied for gas capture and separation technologies. Here, the authors report nitrogen-rich, nanoporous polymers, which display very high CO₂/N₂ selectivity with increasing temperature, which may be attributable to an entropically driven N₂-phobicity effect.

Suppressing the movement of dislocations is critical in enhancing the strength of crystalline metals. Kim et al. demonstrate in Cu– and Ni–graphene nanolayered systems that single-layer graphene is effective in suppressing dislocation movement, resulting in a maximum strength of 4.0 GPa.

The discovery of heterogeneous catalysts for large molecule conversion has been lagging due to the combinatorial inventory of intermediates. Here, the author presents an automated framework to explore the chemical space of reaction intermediates.

Practical electrochemical N₂ reduction reaction is challenged by competing side reactions. Here a combination of DFT and mikrokinetic modelling reveals the potential-dependent competition between electrochemical ammonia production and hydrogen evolution on a single-site iron catalyst embedded in N-doped graphene.

Reliable unzipping of the basal plane of mono-elemental graphene and phosphorene has only been reported thus far. Here, the authors demonstrate the unzipping of bi-elemental 2D MX₂ transition metal chalcogenides as a general route to synthesize 1D nanoribbon structures.

Production of affordable, clean hydrogen relies on efficient oxygen evolution, but improving catalytic performance for the reaction in acidic media is challenging. Here the authors show how tuning the nanoporous morphology of iridium/iridium oxide leads to an improvement in activity/stability, compared with conventional iridium-based oxides.

While lithium-oxygen batteries offer a green method to power vehicles, the sluggish decomposition of lithium peroxide limits device performance. Here, the authors direct lithium peroxide formation into amorphous nanostructures to enable its facile decomposition and improve charging efficiency.