Combining theory and experiment in electrocatalysis: Insights into materials design

Better living through water-splitting Chemists have known how to use electricity to split water into hydrogen and oxygen for more than 200 years. Nonetheless, because the electrochemical route is inefficient, most of the hydrogen made nowadays comes from natural gas. Seh et al. review recent progress in electrocatalyst development to accelerate water-splitting, the reverse reactions that underlie fuel cells, and related oxygen, nitrogen, and carbon dioxide reductions. A unified theoretical framework highlights the need for catalyst design strategies that selectively stabilize distinct reaction intermediates relative to each other. Science , this issue p. 10.1126/science.aad4998 </jats:p

Two-Dimensional Titanium and Molybdenum Carbide MXenes as Electrocatalysts for CO2 Reduction

Electrocatalytic CO2 reduction reaction (CO2RR) is an attractive way to produce renewable fuel and chemical feedstock, especially when coupled with efficient CO2 capture and clean energy sources. On the fundamental side, research on improving CO2RR activity still revolves around late transition metal-based catalysts, which are limited by unfavorable scaling relations despite intense investigation. Here, we report a combined experimental and theoretical investigation into electrocatalytic CO2RR on Ti- and Mo-based MXene catalysts. Formic acid is found as the main product on Ti2CTx and Mo2CTx MXenes, with peak Faradaic efficiency of over 56% on Ti2CTx and partial current density of up to −2.5 mA cm−2 on Mo2CTx. Furthermore, simulations reveal the critical role of the Tx group: a smaller overpotential is found to occur at lower amounts of –F termination. This work represents an important step toward experimental demonstration of MXenes for more complex electrocatalytic reactions in the future

Rational Design of Two-Dimensional Transition Metal Carbide/Nitride (MXene) Hybrids and Nanocomposites for Catalytic Energy Storage and Conversion

Electro-, photo-, and photoelectrocatalysis play a critical role toward the realization of a sustainable energy economy. They facilitate numerous redox reactions in energy storage and conversion systems, enabling the production of chemical feedstock and clean fuels from abundant resources like water, carbon dioxide, and nitrogen. One major obstacle for their large-scale implementation is the scarcity of cost-effective, durable, and efficient catalysts. A family of two-dimensional transition metal carbides, nitrides, and carbonitrides (MXenes) has recently emerged as promising earth-abundant candidates for large-area catalytic energy storage and conversion due to their unique properties of hydrophilicity, high metallic conductivity, and ease of production by solution processing. To take full advantage of these desirable properties, MXenes have been combined with other materials to form MXene hybrids with significantly enhanced catalytic performances beyond the sum of their individual components. MXene hybridization tunes the electronic structure toward optimal binding of redox active species to improve intrinsic activity while increasing the density and accessibility of active sites. This review outlines recent strategies in the design of MXene hybrids for industrially relevant electrocatalytic, photocatalytic, and photoelectrocatalytic applications such as water splitting, metal–air/sulfur batteries, carbon dioxide reduction, and nitrogen reduction. By clarifying the roles of individual material components in the MXene hybrids, we provide design strategies to synergistically couple MXenes with associated materials for highly efficient and durable catalytic applications. We conclude by highlighting key gaps in the current understanding of MXene hybrids to guide future MXene hybrid designs in catalytic energy storage and conversion applications

Engineering grain boundaries at the 2D limit for the hydrogen evolution reaction

Atom-thin transition metal dichalcogenides (TMDs) have emerged as fascinating materials and key structures for electrocatalysis. So far, their edges, dopant heteroatoms and defects have been intensively explored as active sites for the hydrogen evolution reaction (HER) to split water. However, grain boundaries (GBs), a key type of defects in TMDs, have been overlooked due to their low density and large structural variations. Here, we demonstrate the synthesis of wafer-size atom-thin TMD films with an ultra-high-density of GBs, up to ~1012 cm−2. We propose a climb and drive 0D/2D interaction to explain the underlying growth mechanism. The electrocatalytic activity of the nanograin film is comprehensively examined by micro-electrochemical measurements, showing an excellent hydrogen-evolution performance (onset potential: −25 mV and Tafel slope: 54 mV dec−1), thus indicating an intrinsically high activation of the TMD GBs

Understanding electrified interfaces

International audienceUnderstanding electrified interfaces is crucial to enabling a multitude of applications, including photo(electrocatalysis), super-and pseudocapacitors, batteries, etc. However, reaching an atomistic understanding of electrified interfaces remains challenging and will require the combination and development of refined computations and experiments

Theory-guided materials design: two-dimensional MXenes in electro- and photocatalysis

International audienceTwo-dimensional transition metal carbides and nitrides (MXenes) have made significant impact on sustainable energy research in the fields of energy storage and conversion. Unlike short-term energy storage strategies (e.g. batteries and supercapacitors), the catalytic conversion of simple molecules to value-added chemicals using renewable energy represents a more long-term solution to the world's energy crisis. Significant advances in density functional theory and low-cost computing in the past decade have enabled the generation of reliable materials data from fundamental physics equations. The paradigm shift towards theory-guided materials design is expected to enhance the catalyst discovery and development process by providing rational guidance to screen viable MXene catalysts more rapidly than an experimental-only approach. In this review, we aim to provide a critical appraisal of the latest theoretical and experimental work on MXenes in the fields of electro-and photocatalytic energy conversion, including relevant reactions involving hydrogen, oxygen, carbon dioxide and nitrogen molecules. In the process, we will also be pointing out current limitations in theoretical models, existing scientific gaps and future research directions for this field.

Artificial intelligence and machine learning in energy storage and conversion

Artificial intelligence (AI) and machine learning (ML) have been transforming the way we perform scientific research in recent years.1–4 This themed collection aims to showcase the implementation of AI and ML in energy storage and conversion research, including that on batteries, supercapacitors, electrocatalysis, and photocatalysis. The works covered range from materials, to devices, to systems, with an emphasis on how AI and ML have accelerated research and development in these fields

How machine learning can accelerate electrocatalysis discovery and optimization

International audienceAdvances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories

Correction: Theory-guided materials design: two-dimensional MXenes in electro- and photocatalysis

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