Reviews & Analysis

This Perspective discusses ways to probe the presence or absence of molecular-scale interactions between actives sites on heterogeneous electrodes and how they affect the mechanism of (photo)electrocatalytic reactions.

The advances in artificial intelligence are permeating most scientific domains, and heterogeneous catalysis is no exception. This Perspective discusses the current state and future prospects of AI in heterogeneous catalysis, from the development of an AI-ready data ecosystem to multimodal foundation models and autonomous labs.

The rise of artificial intelligence together with advances in robotics is leading a surge of interest in self-driving laboratories. This Perspective discusses self-driving laboratories for catalysis while arguing that, to achieve their full potential, human oversight is required.

The structure and properties of the electric double layer that forms at the electrode–electrolyte interface is crucial in determining the performance of electrocatalytic reactions. This Perspective puts forward and discusses three major schools of thought on electrolyte effects and electrocatalyst design.

Quantum computing is a promising technology to solve complex challenges that would take classical computers an impractical amount of time. This Perspective discusses the current state of quantum computing and possible applications in enzyme engineering and biocatalysis.

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.

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.

The absolute position of band edges is widely considered an indispensable design principle for selection of appropriate semiconductors for a given photo(electro)catalytic reaction. In this Perspective, the authors re-examine this idea from a viewpoint of semiconductor physics and make the case that alignment of band edges with chemical redox potentials is of limited importance.

Material–microbe hybrids represent an interesting class of catalyst with potential for high energy efficiency and product selectivity. In this Perspective the authors discuss some of the difficulties in understanding these interdisciplinary systems and the attempts to unify the approaches taken by different research communities to further the field.

Biomass is a renewable source of carbon that can be exploited to produce valuable chemicals and fuels. This Perspective discusses the electrochemical valorization of biomass, identifying specific chemical transformations in which the approach can excel.

Electrocatalytic NO_x reduction (NO_xR) to ammonia has recently become an increasingly popular alternative to the more challenging N₂ reduction. This Perspective critically assesses the possible ways NO_xR could contribute to the ammonia economy and clarifies the necessary steps for a rigorous experimental protocol.

Direct CO₂-to-C₂₊ and tandem CO₂-to-CO and CO-to-C₂₊ electrocatalytic systems have been proposed as strategies for sustainable fuel and chemical synthesis. This Perspective considers the role of acidic CO₂ gas on the cathodic microenvironment and local pH and draws connections between this and product selectivity in the electrochemical CO₂ reduction reaction and the electrochemical CO reduction reaction, focusing on the competition between two major pathways: ethylene/ethanol and acetate.

Thanks to a unique set of properties, liquid metal catalysts provide advantages compared to traditional solid systems, yet their potential in heterogeneous catalysis has not been fully explored. This Perspective identifies some of the key advances in the field of liquid metal catalysis, discussing areas where progress is expected through further fundamental understanding as well as reactor engineering.

Understanding the structure–performance relationships of heterogeneous catalysts is of fundamental importance for their deployment in industry. However, gaps exist between the conditions and catalytic materials commonly employed in laboratory studies and those encountered in practical reactors. This Perspective highlights the importance of recognizing such gaps, with the goal to inform the planning of academic research and maximize its impact.

Progress in the field of photocatalytic CO₂ reduction has been constrained by a lack of comparability between studies. This Perspective provides recommendations for best practices in the undertaking and reporting of experimental data in this promising research area.

Catalytic pyrolysis is a promising process for the valorization of biomass and plastic waste, although several aspects related to its practical utilization remain unexplored. This Perspective revisits the salient features of catalytic pyrolysis, identifying a roadmap to advance the application of this technology at commercial scale.

Reaction networks provide complete mechanistic understanding of catalytic processes, although they can be highly complex and thus very challenging to obtain. This Perspective discusses the use of machine learning for the exploration of reaction networks in heterogeneous catalysis.

Combining computational and experimental methods is a powerful approach, but these are not always directly comparable. This Perspective discusses the relationship between experimental measurements and theoretical calculations in electrocatalysis and aims to enhance the connections between the two.

Reliable testing of fuel cell and electrolyser catalysts is crucial for comparison between studies. This Perspective discusses the differences between rotating disk electrode (RDE) and membrane electrode assembly (MEA) testing of electrocatalysts, and identifies where RDE can be useful and when MEA is more appropriate to study activity and stability under realistic conditions.

Most applications of machine learning in catalysis use black-box models to predict physical properties, but extracting meaningful physical insights from them is challenging. This Perspective discusses machine learning approaches for heterogeneous catalysis and classifies them in terms of their interpretability.