Stability is a key property for any catalyst. The description of stability, however, varies in the literature depending on the subfield. In this Review the authors present a systematic literature analysis aimed at identifying generalized deactivation modes and their prevalence in different areas of catalysis to offer a comprehensive descriptive framework of catalyst deactivation.

Metabolic engineering of microbes constitutes a promising strategy to make the industrial production of chemicals more sustainable. This Review discusses recent advances of targeted high-throughput genome editing to construct next-generation cell factories for bioproduction.

Single-atom, small cluster and nanoparticle catalysts feature intriguing reactivity for a variety of transformations, which is often attributed to the properties of specific atomic species. This Review critically revisits the reactivity of such catalysts in term of the ensemble effects that arise from the interaction of multiple metal atoms or single atomic species with neighbouring atoms from supports, additives or surface ligands.

The 27th United Nations Climate Change Conference placed the risks of greenwashing under the spotlight. In this Editorial, we reflect on the implications of this phenomenon for science and peer review.

The dynamic transformation of Cu ions during the selective catalytic reduction of NO_x on Cu zeolites is well documented, although the function of the zeolite framework has not been fully understood. Here the authors unravel the role of anionic Al sites in the zeolite framework in regulating the mobility and reactivity of Cu cations during catalysis.

To overcome mass transport limitations in zeolite-catalysed reactions, scientists must often resort to hierarchical or nanosized zeolites; however, the synthesis of such materials remains challenging. Here the authors disclose a one-pot method for the preparation of Si-zoned MFI-type catalysts with improved diffusion properties for the methanol-to hydrocarbon reaction.

Although the Tetrahymena group I intron was the first RNA catalyst discovered, important mechanistic details remain ambiguous. Now six different conformational states of Tetrahymena group I intron self-splicing and an unexpected pseudoknotted structure are resolved by cryogenic electron microscopy.

Oxygen reduction to hydrogen peroxide is a promising alternative to replace the energy-intensive anthraquinone process in industry. Now, the hydrogen peroxide electrosynthesis performance of a carbon-supported cobalt phthalocyanine catalyst is tuned via the introduction of oxygen functional groups to the support, which optimize the electronic structure of cobalt active sites.

Polypyridine-ligated nickel complexes can mediate a variety of cross-coupling reactions. However, some mechanistic details remain poorly understood. Now, it is demonstrated that the nature of the anionic ligand strongly influences key electron-transfer events in these complexes during elementary reaction steps.

The catalytic cycle of formate dehydrogenase is generally assumed to involve an apoenzyme state according to the Theorell–Chance mechanism. Now, based on single-molecule experiments and multiscale simulations of formate dehydrogenase from Candida boidinii, an alternative mechanism that bypasses the apoenzyme state is proposed.

Computational chemistry has become an increasingly common part of catalysis research. More recently, data-based methods such as machine learning have been suggested as a means to speed up discovery. This Focus issue features a collection of content dedicated to machine learning as pertaining to its potential impact on the field of catalysis.

Data science and machine learning have the potential to accelerate the discovery of effective catalysts; however, these approaches are currently held back by the issue of negative results. This Comment highlights the value of negative data by assessing the bottlenecks in data-driven catalysis research and presents a vision for a way forwards.