Selectivity Map for the Late Stages of CO and CO2 Reduction to C2 Species on Copper Electrodes.

The electrochemical CO and CO2 reduction reactions (CORR and CO2 RR) using copper catalysts and renewable electricity hold promise as a carbon-neutral route to produce commodity chemicals and fuels. However, the exact mechanisms and structure sensitivity of Cu electrodes toward C2 products are still under debate. Herein, we investigate ethylene oxide reduction (EOR) as a proxy to the late stages of CORR to ethylene, and the results are compared to those of acetaldehyde reduction to ethanol. Density functional theory (DFT) calculations show that ethylene oxide undergoes ring opening before exclusively reducing to ethylene via *OH formation. Based on generalized coordination numbers (CN), a selectivity map for the late stages of CORR and CO2 RR shows that sites with moderate coordination (5.9 < CN < 7.5) are efficient for ethylene production, with pristine Cu(100) being more active than defective surfaces such as Cu(311). In contrast, kinks and edges are more active for ethanol production, while (111) terraces are relatively inert.

Designing water splitting catalysts using rules of thumb: advantages, dangers and alternatives.

Thermodynamic analysis of the oxygen evolution reaction (OER) hints toward an intrinsic overpotential caused by the nonoptimal adsorption-energy scaling relation between OH and OOH. Consequently, nowadays it is a widely accepted yet unverified rule of thumb that breaking such a scaling relation results in enhanced catalytic activity. In this perspective, we show that breaking the OH-OOH scaling relation does not per se lower the OER overpotential. Instead, electrocatalytic symmetry and ease of optimization are shown to be key factors when screening for enhanced OER catalysts. The essence of electrocatalytic symmetry is captured by a descriptor called the electrochemical-step symmetry index (ESSI). In turn, the ease of optimization and whether it should be scaling-based or scaling-free is provided by a procedure called δ-ε optimization. Finally, taking the search for bifunctional catalysts for oxygen electrocatalysis as an example, we show that the alternative analysis can be straightforwardly extended to other electrocatalytic reactions.

Subsurface Carbon: A General Feature of Noble Metals.

Carbon moieties on late transition metals are regarded as poisoning agents in heterogeneous catalysis. Recent studies show the promoting catalytic role of subsurface C atoms in Pd surfaces and their existence in Ni and Pt surfaces. Here energetic and kinetic evidence obtained by accurate simulations on surface and nanoparticle models shows that such subsurface C species are a general issue to consider even in coinage noble-metal systems. Subsurface C is the most stable situation in densely packed (111) surfaces of Cu and Ag, with sinking barriers low enough to be overcome at catalytic working temperatures. Low-coordinated sites at nanoparticle edges and corners further stabilize them, even in Au, with negligible subsurface sinking barriers. The malleability of low-coordinated sites is key in the subsurface C accommodation. The incorporation of C species decreases the electron density of the surrounding metal atoms, thus affecting their chemical and catalytic activity.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Mechanistic insight into electrocatalytic glyoxal reduction on copper and its relation to CO2 reduction.

Copper electrodes produce several industrially relevant chemicals and fuels during the electrochemical CO2 reduction reaction (CO2RR). Knowledge about the reaction pathways can help tune the reaction selectivity toward higher-value products. To probe the uncertain role of the C2 molecule glyoxal, we electrochemically reduced it on polycrystalline Cu and quantified its liquid-phase products, namely, ethanol, ethylene glycol, and acetaldehyde. The gas phase contained hydrogen and traces of ethylene. In contrast with previous hypothesis, a one-to-one comparison with CO2RR on Cu indicates that glyoxal is neither a major intermediate in the pathway toward ethylene nor in the pathway toward ethanol. In addition, great possibilities for the selective, low-temperature production of ethylene glycol are open, as computational modelling shows that ethylene glycol and ethanol are produced on different active sites. Thus, apart from the mechanistic insight into CO2RR, this study gives new directions to facilitate the electrification of chemical processes at refineries.

Charting the Atomic C Interaction with Transition Metal Surfaces.

Carbon interaction with transition metal (TM) surfaces is a relevant topic in heterogeneous catalysis, either for its poisoning capability, for the recently attributed promoter role when incorporated in the subsurface, or for the formation of early TM carbides, which are increasingly used in catalysis. Herein, we present a high-throughput systematic study, adjoining thermodynamic plus kinetic evidence obtained by extensive density functional calculations on surface models (324 diffusion barriers located on 81 TM surfaces in total), which provides a navigation map of these interactions in a holistic fashion. Correlation between previously proposed electronic descriptors and ad/absorption energies has been tested, with the d-band center being found the most suitable one, although machine learning protocols also underscore the importance of the surface energy and the site coordination number. Descriptors have also been tested for diffusion barriers, with ad/absorption energies and the difference in energy between minima being the most appropriate ones. Furthermore, multivariable, polynomial, and random forest regressions show that both thermodynamic and kinetic data are better described when using a combination of different descriptors. Therefore, looking for a single perfect descriptor may not be the best quest, while combining different ones may be a better path to follow.

Revealing the nature of active sites in electrocatalysis.

Heterogeneous electrocatalysis plays a central role in the development of sustainable, carbon-neutral pathways for energy provision and the production of various chemicals. It determines the overall efficiency of electrochemical devices that involve catalysis at the electrode/electrolyte interface. In this perspective, we discuss key aspects for the identification of active centers at the surface of electrocatalysts and important factors that influence them. The role of the surface structure, nanoparticle shape/size and the electrolyte composition in the resulting catalytic performance is of particular interest in this work. We highlight challenges that from our point of view need to be tackled, and provide guidelines for the design of "real life" electrocatalysts for renewable energy provision systems as well as for the production of industrially important compounds.