Articles in 2023

Direct CO₂ electroreduction on Cu-based catalysts has been used to produce C₂ products but yields of C₃ products have remained low. Here a CO₂ supersaturation strategy is used to promote electrodeposition of a highly alloyed CuAg electrode and its subsequent selectivity towards 2-propanol.

Electrosynthesis of higher carbon products (C₄₊) in a high selectivity has not been achieved by the direct reduction of CO₂ or CO. Here, the authors use a cascade electrocatalysis–thermocatalysis approach to produce butane from CO with an overall selectivity of 43%.

Microporous zeolites have pores of molecular dimension that can stabilize desired chemical pathways but may also introduce mass-transfer limitations. Now, synthesis protocols allow for greater control of catalyst active-site location via elemental zoning, enabling an alternative strategy to reduce mass-transfer limitations and consequently improve catalyst performance for methanol-to-hydrocarbon reactions.

Electron transfer processes are almost ubiquitous, yet hard to understand thoroughly due to the variability of catalytic species involved. Now, a detailed mechanistic picture of the electron transfer associated with polypyridine nickel systems has been reported, offering an answer to the electron transfer puzzle of these complexes.

The ability to maximize electron utilization in electrosynthesis has been a long-standing goal, with research typically focusing on catalyst design or pairing disparate reactions. Now, electrocatalytic hydrogenation is performed with Faradaic efficiencies approaching 200% by producing hydrogen atoms from both the reduction and oxidation reactions simultaneously.

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.

Computational chemistry has the potential to aid in the design of heterogeneous catalysts; however, there is currently a large gap between the complexity of real systems and what can be readily computed at scale. This Review discusses the ways in which machine learning can assist in closing this gap to facilitate rapid advances in catalyst discovery.