Non-thermal plasma offers a remarkable alternative to traditional catalysis methods, meeting the rising demands for essential chemicals like fertilizers and fuels. This Comment explores how this approach can support sustainability goals by promoting economic growth, and decentralizing chemical production processes.

Uses of machine learning and automation are increasing and these techniques are becoming popular in catalysis research. This Perspective discusses how active learning workflows and human intervention should be optimized to ensure the most efficient progress for emerging self-driving laboratories performing heterogeneous catalysis research.

Intermediate-temperature water splitting based on protonic ceramic electrolysis cells is a promising strategy that is far less explored than its low- and high-temperature counterparts. This Perspective explores its challenges and offers guidelines on the design of anode materials and catalysts.

Isotope labelling with ¹³C-carbon has become a popular technique used to understand carbon dioxide electroreduction mechanisms. However, isotopes of carbon exhibit complex selectivity effects, which need to be carefully considered in the experimental design to avoid misinterpretations.

Prime editing is a CRISPR methodology whose efficiency declines with distance from the target sequence. Here the authors demonstrate prime editing with prolonged editing window, proPE, which extends the editing distance, enabling the use of prime editing for therapeutic interventions.

Delocalized radical systems are appealing for controlling stereoselectivity in organic synthesis. Here the authors report on a Co(II)-based enantioselective radical system for the dearomative 1,7-conjugate amination of readily available 4-vinylphenols with aryl azides.

Tandem electro-biocatalytic systems present a versatile platform for producing a variety of synthetic products using CO₂ as a starting material. Here direct ocean carbon capture is incorporated into an electrolysis scheme to produce formic acid from CO₂ dissolved in seawater that is subsequently converted to succinate in a bioreactor.

The activity and stability of supported metal catalysts is in large part influenced by their interaction with the support. Now, neural network molecular dynamics simulations are combined with interpretable machine learning to reveal the governing factors of metal–support interactions for Pt nanoparticles on various oxide supports, identifying key features and proposing sinter-resistant supports.

Hexopyranose cleavage is a crucial step in carbon metabolism. Here the authors report the discovery and characterization of metalloenzyme Art22, which is involved in the sugar moiety modification of aurantinin B, an antibacterial agent from Bacillus.

It is challenging to design machine learning potentials for heterogeneous catalysis that are universal, reactive and have high accuracy. Now, an element-based machine learning potential relying on a random exploration via an imaginary chemicals optimization sampling strategy is put forward, and is successfully demonstrated for a range of applications.

This Editorial introduces the roll-out of transparent peer review at Nature Catalysis.