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Iridium nanoparticles are important catalysts for several chemical and energy conversion reactions. Studies of iridium nanoparticles have also been a key for the development of kinetic models of nanomaterial formation. However, compared to other metals such as gold or platinum, knowledge on the nature of prenucleation species and structural insights into the resultant nanoparticles are missing, especially for nanoparticles obtained from IrxCly precursors investigated here. We use in situ X-ray total scattering (TS) experiments with pair distribution function (PDF) analysis to study a simple, surfactant-free synthesis of colloidal iridium nanoparticles. The reaction is performed in methanol at 50 °C with only a base and an iridium salt as precursor. From different precursor salts─IrCl3, IrCl4, H2IrCl6, or Na2IrCl6─colloidal nanoparticles as small as Ir∼55 are obtained as the final product. The nanoparticles do not show the bulk iridium face-centered cubic (fcc) structure but show decahedral and icosahedral structures. The formation route is highly dependent on the precursor salt used. Using IrCl3 or IrCl4, metallic iridium nanoparticles form rapidly from IrxClyn- complexes, whereas using H2IrCl6 or Na2IrCl6, the iridium nanoparticle formation follows a sudden growth after an induction period and the brief appearance of a crystalline phase. With H2IrCl6, the formation of different Irn (n = 55, 55, 85, and 116) nanoparticles depends on the nature of the cation in the base (LiOH, NaOH, KOH, or CsOH, respectively) and larger particles are obtained with larger cations. As the particles grow, the nanoparticle structure changes from partly icosahedral to decahedral. The results show that the synthesis of iridium nanoparticles from IrxCly is a valuable iridium nanoparticle model system, which can provide new compositional and structural insights into iridium nanoparticle formation and growth.

The dynamic atomic-scale behaviour of metallic mono- and bimetallic nanocrystals under reactive gas environments show the direct effect of alloying and Cu mobility on the corresponding restructuring processes.

Compositionally complex materials such as high-entropy alloys and oxides have the potential to be efficient platforms for catalyst discovery because of the vast chemical space spanned by these novel materials. Identifying the composition of the most active catalyst materials, however, requires unraveling the descriptor-activity relationship, as experimental screening the multitude of possible element ratios quickly becomes a daunting task. In this work, we show that inferred adsorption energy distributions of *OH and *O on complex solid solution surfaces within the space spanned by the system Ag-Pd-Pt-Ru are coupled to the experimentally observed electrocatalytic performance for the oxygen reduction reaction. In total, the catalytic activity of 1582 alloy compositions is predicted with a cross-validated mean absolute error of 0.042 mA/cm2 by applying a theory-derived model with only two adjustable parameters. Trends in the discrepancies between predicted electrochemical performance values of the model and the measured values on thin film surfaces subsequently provide insight into the alloys’ surface compositions during reaction conditions. Bridging this gap between computationally modeled and experimentally observed catalytic activities, not only reveals insight into the underlying theory of catalysis but also takes a step closer to realizing exploration and exploitation of high-entropy materials.

Understanding what controls the reaction rate on iridium-based catalysts is central to designing more active and stable electrocatalysts for the water oxidation reaction in proton exchange membrane (PEM) electrolysers. Here, we quantify the densities of redox active centres and probe their binding strengths on amorphous IrOx and rutile IrO2 using a combination of operando time-resolved optical spectroscopy, X-ray absorption spectroscopy (XAS) and time of flight secondary ion mass spectrometry (TOF-SIMs). Firstly, our results show that although IrOx exhibits an order of magnitude higher geometry current density compared to IrO2, the intrinsic rates of reaction per active state, on IrOx and IrO2 are comparable at a given potential. Secondly, we establish a quantitative experimental correlation between the intrinsic rate of water oxidation and the energetics of the active states. We use density functional theory (DFT) based models to provide a molecular scale interpretation of our data. We find that the *O species formed at water oxidation potentials have repulsive adsorbate-adsorbate interactions, and thus increasing their coverage weakens their binding and promotes the rate-determining O-O bond formation. Finally, we provide insights into how the intrinsic water oxidation kinetics can be increased by optimising both the binding energy and the interaction strength of the catalytically active states.

The main challenge for acidic water electrolysis is the lack of active and stable oxygen evolution catalysts based on abundant materials, which are globally scalable. Iridium oxide is the only material which is active and stable. However, Ir is extremely rare. While both active materials and stable materials exist, those that are active are usually not stable and vice versa. In this work, we present a new design strategy for activating stable materials originally deemed unsuitable due to a semiconducting nature and wide band gap energy. These stable semiconductors cannot change oxidation state under the relevant reaction conditions. Based on DFT calculations, we find that adding an n-type dopant facilitates oxygen binding on semiconductor surfaces. The binding is, however, strong and prevents further binding or desorption of oxygen. By combining both n-type and p-type dopants, the reactivity can be tuned so that oxygen can be adsorbed and desorbed under reaction conditions. The tuning results from the electrostatic interactions between the dopants as well as between the dopants and the binding site. This concept is experimentally verified on TiO2 by co-substituting with different pairs of n- and p-type dopants. Our findings suggest that the co-substitution approach can be used to activate stable materials, with no intrinsic oxygen evolution activity, to design new catalysts for acid water electrolysis.

Electrolyte effects play an important role on the activity of the oxygen reduction reaction (ORR) of Pt-based electrodes. Herein, we combine a computational model and rotating disk electrode measurements to investigate the effects from phosphate anion poisoning for the ORR on well-defined extended Pt surfaces. We construct a model including the poisoning effect from phosphate species on Pt(111) and Cu/Pt(111) based on density functional theory simulations. We have investigated the effect of adsorbed phosphate species at low overpotentials when tuning *OH binding energies. Our work shows that, regardless of the surface site blockage from phosphate, the trend in catalytic oxygen reduction activity is predominately governed by the *OH binding.

Rational design of catalysts by tailoring specific surface sites with different elements could result in catalysts with high activity, selectivity and stability. In this work, we show that *CO on-top and O* on-top adsorption energies are good descriptors for catalysis of the CO oxidation reaction (COOR) on pure metals and binary alloys. The observed Brønsted-Evans-Polanyi (BEP) and scaling relations for COOR on different surfaces are incorporated into a predictive model that uses the binding strength of the four adjacent metal atoms making up the active site for COOR catalysis to estimate reaction and activation energies. The model is used to screen 234 multi-metallic catalyst candidates made by combining Ru, Pt, Pd, Cu and Au at these four sites. The screening and subsequent calculations suggest that Ru-Cu-Au and Ru-Pt-Cu alloys are good catalysts for COOR. Our study shows that it is possible to use information from pure metals and binary alloys to predict the catalytic behavior of more complex alloys, and hereby reduce the computational cost of identifying new catalyst candidates for COOR.

Theoretical descriptors differentiate the catalytic activity of materials for the oxygen evolution reaction by the strength of oxygen binding in the reactive intermediate created upon electron transfer. Recently, time-resolved spectroscopy of a photo-electrochemically driven oxygen evolution reaction followed the vibrational and optical spectra of this intermediate, denoted M-OH*. However, these inherently kinetic experiments have not been connected to the relevant thermodynamic quantities. Here we discover that picosecond optical spectra of the Ti-OH* population on lightly doped SrTiO3 are ordered by the surface hydroxylation. A Langmuir isotherm as a function of pH extracts an effective equilibrium constant relatable to the free energy difference of the first oxygen evolution reaction step. Thus, time-resolved spectroscopy of the catalytic surface reveals both kinetic and energetic information of elementary reaction steps, which provides a critical new connection between theory and experiment by which to tailor the pathway of water oxidation and other surface reactions.

The Mo/Fe nitrogenase enzyme is unique in its ability to efficiently reduce dinitrogen to ammonia at atmospheric pressures and room temperature. Should an artificial electrolytic device achieve the same feat, it would revolutionise fertilizers and even provide an energy dense, truly carbon-free fuel. This Review provides a coherent comparison of recent progress made in dinitrogen fixation on (i) solid electrodes, (ii) homogeneous catalysts and (iii) nitrogenases. Specific emphasis is placed on systems for which there is unequivocal evidence that dinitrogen reduction has taken place. By establishing the cross-cutting themes and synergies between these systems, we identify viable avenues for future research.

Entropy-driven formation of high entropy alloy (HEA) nanoparticles from metal precursors requires high temperatures and controlled cooling rates. However, several proposed HEA nanoparticle synthesis strategies avoid the high-temperature regime. In our work, we address the question of how single-phase HEA nanoparticles can form at low temperatures. Investigating a system of five noble metal single source precursors, we combine in situ X-ray powder diffraction with multi-edge X-ray absorption spectroscopy to demonstrate that the formation of single-phase nanoparticles is governed by stochastic principles and the inhibition of precursor mobility during the formation process. The proposed formation principle is supported by simulations of the nanoparticle formation in a randomized process, rationalizing the experimentally found differences between two-element and multi-element metal precursor mixtures.

Cu is currently the most effective monometallic catalyst for producing valuable multicarbon-based (C2+) products, such as ethylene and ethanol, from the CO2reduction reaction (CO2RR). One approach to optimize the activity and selectivity of the metal Cu catalyst is to functionalize the Cu electrode with a molecular modifier. We investigate from a data standpoint whether any reported functionalized Cu catalyst improves the intrinsic activity and/or multicarbon product selectivity compared to the performance of bare Cu foil and the best single crystal Cu facets. Our analysis shows that the reported increases in activity are due to increased surface roughness and disappear once normalized with respect to electrochemical surface area. The intrinsic activity generally falls below that of the bare Cu foil reference, both for total and product-specific current, which we attribute to nonselective blocking of active sites by the modifier on the surface. Instead, an analysis of various polymer diffusion coefficients indicates that the modifier allows for easier diffusion of CO2compared to H2O to the surface, leading to greater selectivity for CO2RR and C2+products. As such, our analysis finds no catalyst for CO2RR that intrinsically outperforms bare Cu.

The formation of a vast number of different multi-element active sites in compositionally complex solid solution materials, often more gene­rally termed high-entropy alloys, offers new and unique concepts in catalyst design, which are mitigating existing limitations and change the view on structure-activity relations. We discuss these concepts by summarising the presently existing fundamental knowledge and criti­cally assess the chances and limitations of this material class also highlighting design strategies. A roadmap is proposed, illustrating which of the characteristic concepts could be exploited using which strategy, and which breakthroughs might be possible to guide future research in this highly promising material class for (electro)catalysis.

Electrocatalytic conversion is a promising technology for storing renewable electricity in the chemical form. Substantial efforts have been made on the multi-carbon feedstock production,while producing nitrogen-containing chemicals like urea via C-N coupling little is known. Here, we elucidate the possible urea production on metals through co-reduction of nitric oxide (NO) and carbon oxide (CO). Based on adsorption energies calculated by DFT, we find that Cu is able to bind both *NO and *CO while not binding *H. During NO + CO co-reduction, we identify two kinetically and thermodynamically possible C-N couplings via *CO + *N and *CONH + *N, and further hydrogenation leads to urea formation. A 2-D activity heatmap has been constructed for describing nitrogen conversion to urea. This work provides a clear example of using computational simulations to predict selective and active materials for sustainable urea production.

Electrocatalytic conversion is a promising technology for storing renewable electricity in the chemical form. Substantial efforts have been made on the multi-carbon feedstock production,while producing nitrogen-containing chemicals like urea via C-N coupling little is known. Here, we elucidate the possible urea production on metals through co-reduction of nitric oxide (NO) and carbon oxide (CO). Based on adsorption energies calculated by DFT, we find that Cu is able to bind both *NO and *CO while not binding *H. During NO + CO co-reduction, We identify two kinetically and thermodynamically possible C-N couplings via CO + *N and CONH + *N, and further hydrogenation leads to urea formation. A 2-D activity heatmap has been constructed for describing nitrogen conversion to urea. This work provides a clear example of using computational simulations to predict selective and active materials for sustainable urea production.

Cu is currently the most effective monometallic catalyst for producing valuable multi-carbon-based products, such as ethylene and ethanol, from the CO2 reduction reaction (CO2RR). One approach to optimize the activity and selectivity of the metal Cu catalyst is to functionalize the Cu electrode with a molecular modifier. We investigate from a data standpoint whether any reported functionalized Cu catalyst improves the intrinsic activity and/or multi-carbon product selectivity compared to the performance of bare Cu foil and the best single crystal Cu facets. Our analysis shows that the reported increases in activity are due to increased surface roughness and disappear once normalizing with respect to electrochemical surface area. The intrinsic activity generally falls below that of bare Cu foil, both for total and product-specific current, which we attribute to non-selective blocking of active sites by the modifier on the surface. Instead, we show that the modifier allows for easier diffusion of CO2 compared to H2O to the surface, leading to greater selectivity for CO2RR and C2+ products. As such, our analysis finds no catalyst for CO2RR that intrinsically outperforms bare Cu.

(Bi)carbonate adsorption on Cu(100) in 0.1 M KHCO3 has been studied by in situ scanning tunneling microscopy. Coexistence of different ordered adlayer phases with ( 2 ${\sqrt{2}}$ x6 2 ${\sqrt{2}}$ )R45 degrees and (4x4) unit cells was observed in the double layer potential regime. The adlayer is rather dynamic and undergoes a reversible order-disorder phase transition at 0 V vs. the reversible hydrogen electrode. Density functional calculations indicate that the adlayer consists of coadsorbed carbonate and water molecules and is strongly stabilized by liquid water in the adjacent electrolyte.

Iridium nanoparticles are important catalysts for several chemical and energy conversion reactions. Studies of iridium nanoparticles have also been key for the development of kinetic models of nanomaterial formation. However, compared to other metals such as gold or platinum, there is very limited knowledge on the actual formation pathway of iridium nanoparticles on the atomic and molecular level. Here, we use in situ X-ray total scattering experiments with pair distribution function analysis to study a simple, surfactant-free synthesis of colloidal iridium nanoparticles. The reaction is performed in methanol at 50 °C with only a base and an iridium salt as precursor. From different precursor salts - IrCl3, IrCl4, H2IrCl6, or Na2IrCl6 – colloidal nanoparticles as small as Ir55 are obtained as the final product. The nanoparticles do not show the bulk iridium face-centered cubic (fcc) structure, but decahedral and icosahedral structures. The formation route is highly dependent on the precursor salt used. Using IrCl3 or IrCl4, metallic iridium nanoparticles form rapidly from IrxCly complexes, whereas using H2IrCl6 or Na2IrCl6, the iridium nanoparticle formation follows a sudden growth after an induction period and the brief ap-pearance of a crystalline phase. With H2IrCl6, the formation of different Irn (n= 55, 55, 85, 116) nanoparticles depends on the nature of the cation in the base - LiOH, NaOH, KOH, or CsOH, respectively - and larger particles are obtained with larger cations. As the particles grow, the nanoparticle structure changes from partly icosahedral to decahedral. The presented results introduce a new iridium nanoparticle synthesis model system and provide new chemical insights into nanoparticle formation and growth.

High-entropy alloy (HEA) electrocatalysts offer a vast composition space that awaits exploration to identify interesting materials for energy conversion reactions. While attempts have been made to explore the composition space of HEA thin-film libraries and compare experimental and computational studies, no corresponding approaches exist for HEA nanoparticles. So far, catalytic investigations on HEA nanoparticles are limited to small sets of individual catalysts. Here, we report the experimental exploration of the composition space of carbon-supported Pt−Ru−Pd−Rh− Au nanoparticles for the H2/CO oxidation reaction by constructing a dataset using Bayesian optimization as guidance. Applying a surfactant-free synthesis platform, a dataset of 68 samples was investigated. By constructing machine learning models, the relationship between the concentrations of the constituent elements and the catalytic activity was analyzed and compared to density functional theory calculations. The machine learning models confirm findings from previous studies concerning the role of Ru in the H2/CO oxidation reaction. This has been achieved starting from a random set of compositions and without any prior assumptions for the reaction mechanism nor any in-depth design of the active site. In addition, by comparing the trends of the computational and experimental studies, it is seen that the “onset potentials” across the compositions can be correlated with the adsorption energy of *OH. The best correlation between the computational and experimental data is obtained when considering 5% of the most strongly *OH adsorbing sites.

Electrocatalytic conversion of formic acid oxidation to CO2 and the related CO2 reduction to formic acid represent a potential closed carbon-loop based on renewable energy. However, formic acid fuel cells are inhibited by the formation of site-blocking species during the formic acid oxidation reaction. Recent studies have elucidated how the binding of carbon and hydrogen on catalyst surfaces promote CO2 reduction towards CO and formic acid. This has also given fundamental insights to the reverse reaction, i.e. the oxidation of formic acid. In this work, simulations on multiple materials have been combined with formic acid oxidation experiments on electrocatalysts to shed light on the reaction and the accompanying catalytic limitations. We correlate data on different catalysts to show that (i) formate, which is the proposed formic acid oxidation intermediate, has similar binding energetics on Pt, Pd and Ag, while Ag does not work as catalyst, and (ii) *H adsorbed on the surface results in *CO formation and poisoning through a chemical disproportionation step. Using these results, the fundamental limitations can be revealed and progress our understanding of the mechanism of the formic acid oxidation reaction .

Compared to aqueous electrolytes, the fundamental understanding of the chemical and electrochemical processes occurring in non-aqueous electrolytes in general is far less developed. This is no different for Li-ion battery (LiB) electrolytes, where many questions regarding the solid electrolyte interphase (SEI) on the anode side remain unanswered, including its chemical composition, the mechanism of formation and the impact on LiB performance. Here, we present a detailed experimental and theoretical study of the electrochemistry of ethylene carbonate (EC) and its chemical relationship with trace amounts of water and HF across a vast range of electrode materials, from well-ordered single crystals to realistic graphite electrodes. We reveal the electrocatalytic nature of EC, HF and water electroreduction at all interfaces. Moreover, we show that these reactions are connected in a closed cycle by chemical reactions, that take place either at the interface or in the bulk of the electrolyte. For the first time, we unveil the catalytic role of water in EC electroreduction and demonstrate that the composition of the SEI depends predominantly on the balance between the (electro)chemistry of EC, water and HF.

A promising opportunity and major challenge in the field of High-Entropy Alloy (HEA) catalysis is the abundance of possible compositions. The number of possible compositions makes it impossible to study all of them. Therefore, sophisticated methods are required to intelligently select interesting compositions. On one hand, adding an element to a composition space increases the dimensionality of that space and the number of compositions within it combinatorially. However, it also increases the number of sub-spaces that are part of this larger composition space. Assuming a constant sampling density, the number of experiments required to study a large, combined composition space of sufficient dimensionality can be less than studying all of its individual sub-spaces. This hypothesis is investigated using experimental work in which 200 compositions in an 8-element composition space composed of Au, Ir, Os, Pd, Pt, Re, Rh, and Ru were synthesized as nanoparticles. Each composition was experimentally tested for the electrocatalytic activity towards the oxygen reduction reaction. The model that was constructed using this data turned out to adequately predict data in three of its 5-element composition sub-spaces. This observation paves way for a backward elimination strategy in HEA discovery. According to this strategy all elements of interest are studied in a single composition space from which knowledge of all subspaces can be learnt. Subsequently, elements that do not show a positive influence towards the studied reaction can be removed from the search space. Ultimately, this backward elimination search produces an alloy space which has a high probability of containing the most active catalyst composition.

High-entropy alloys (HEAs) represent near-equimolar points in the middle of a vast composition space of multi-metallic catalysts. Successful modeling of the catalytic activity of these complex materials allows to search this composition space for optimal catalysts. Previous models of HEA catalytic activity have been based on local and intricate descriptions of the atomic environment on the catalyst surface to predict accurate adsorption energies. These are subsequently used to model the catalytic activity. In this study, we show that by approximating the ligand effect of the surrounding atoms around an adsorption site with a mean-field perturbation corresponding to equimolar AgIrPdPtRu, the same trend in the predictions of the oxygen reduction reaction catalytic activities are obtained for a majority of the quinary Ag-Ir-Pd-Pt-Ru composition space. By comparing to models that consider the ligand effect locally, we show that the extent of such a mean-field approximation is valid up to and including equimolar ternary alloys, corresponding to 60.3 % of the quinary composition space. When extrapolating to make predictions far from near-equimolar compositions, such as for binary alloys, the mean field has been sufficiently perturbed to cause large discrepancies compared to the local models. Here, the intricate models thus prove more useful for discovering optimal catalysts.

High-entropy alloys (HEAs) have the potential to be a paradigm-shift for rational catalyst discovery but this new type of alloy requires a completely new approach to predict the surface reactivity. In addition to the ligand effect perturbing the surface-adsorbate bond, the random configuration of elements in the surface will also induce local strain effects due to the varying radii of neighboring atoms. Accurate modelling of HEA surface reactivity requires an estimate of this effect: To what degree is the adsorption of intermediates on these lattice distorted atomic environments affected by local strain? In this study, more than 3,500 density functional theory (DFT) calculated adsorption energies of *OH and *O adsorbed on the HEAs IrPdPtRhRu and AgAuCuPdPt are statistically analyzed with respect to the lattice constants of the alloys and the surfaces of each individual binding site. It is found that the inherent distortion of the lattice structure in HEAs releases the local strain effect on the adsorption energy as the atomic environment surrounding the binding atom(s) settles into a relaxed structure. This is even observed to be true for clusters of atoms of which the sizes deviate significantly from the atomic environment in which they are embedded. This elucidates an important aspect of binding site interaction with the neighboring atoms and thus constitutes a step towards a more accurate theoretical model of estimating the reactivity of HEA surfaces.

High-entropy alloys are slowly making their debut as a platform for catalyst discovery, but conventional methods, theoretical as well as experimental, may fall short of screening the vast composition space inhabited by this class of materials. New theoretical approaches are needed to gauge the catalytic activity of high-entropy alloys and optimize the alloy composition within a feasible time frame as a prerequisite for further experimental studies. Herein, we establish a workflow for simulations of catalysis on high-entropy alloy surfaces. For each step of the modeling we present our choice of method, however, we also acknowledge that alternative options are available. We apply the developed methodology to predict the net catalytic activity of any alloy composition, within the composition space spanned by Ag-Ir-Pd-Pt-Ru, for the oxygen reduction reaction. Based on first-principle calculations, a graph convolution neural network is used to predict adsorption energies of *OH and *O. Subsequently, taking competitive co-adsorption of reaction intermediates into account, we couple the net adsorption energy distribution of a high-entropy alloy surface to the expected current density. Lastly, this procedure is used in conjunction with a Bayesian optimization scheme to search for optimal alloy compositions, which yields several promising compositions. This result shows that an unbiased in silico pre-screening and discovery of catalyst candidates is viable and will help scale the otherwise insurmountable challenge of searching for high-entropy alloy catalysts. It is our hope that our computational framework, which is freely available on GitHub, will aid other research groups to efficiently identify promising high-entropy alloy catalysts.

Cu is currently the most effective monometallic catalyst for producing valuable multi-carbon-based products, such as ethylene and ethanol, from the CO2 reduction reaction (CO2RR). One approach to optimize the activity and selectivity of the metal Cu catalyst is to functionalize the Cu electrode with a molecular modifier. We investigate from a data standpoint whether any reported functionalized Cu catalyst improves the intrinsic activity and/or multi-carbon product selectivity compared to the performance of bare Cu foil and the best single crystal Cu facets. Our analysis shows that the reported increases in activity are due to increased surface roughness and disappear once normalizing with respect to electrochemical surface area. The intrinsic activity generally falls below that of bare Cu foil, both for total and product-specific current, which we attribute to non-selective blocking of active sites by the modifier on the surface. Instead, we show that the modifier allows for easier diffusion of CO2 compared to H2O to the surface, leading to greater selectivity for CO2RR and C2+ products. As such, our analysis finds no catalyst for CO2RR that intrinsically outperforms bare Cu.

Complex solid solutions (“high entropy alloys”), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data‐driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition‐spread thin‐film libraries are synthesised. High‐throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag‐Ir‐Pd‐Pt‐Ru. The method can identify optimal complex‐solid‐solution materials for electrocatalytic reactions in an unprecedented manner., Complex solid solutions (“high‐entropy alloys”) promise a paradigm change in electrocatalysis but expose the challenge of almost unlimited options in adjusting their compositions. We propose the utilisation of computational models, combined with high‐throughput experimentation for the verification of the model assumptions, which allows for model refinement in iterative loops, understanding of binding mechanisms, and discovery of the most active composition.

Cu electrodes are promising materials to catalyze the conversion of CO2 and CO into renewable fuels and valuable chemicals. However, a detailed description of the properties of the Cu–electrolyte i...

Single-atom catalysts (SACs) have recently attracted broad scientific interests due to their unique structural feature, the single-atom dispersion. Optimized electronic structure as well as high stability are required for single-atom catalysts to enable efficient electrochemical production of H2O2. Herein, we report a facile synthesis method that stabilizes atomic Pd species on the reduced graphene oxide/N-doped carbon hollow carbon nanospheres (Pd1/N-C). Pd1/N-C exhibited remarkable electrochemical H2O2 production rate with high faradaic efficiency, reaching 80%. The single-atom structure and its high H2O2 production rate were maintained even after 10,000 cycle stability test. The existence of single-atom Pd as well as its coordination with N species is responsible for its high activity, selectivity, and stability. The N coordination number and substrate doping around Pd atoms are found to be critical for an optimized adsorption energy of intermediate *OOH, resulting in efficient electrochemical H2O2 production.

A challenge in the electrochemical CO2 reduction reaction (CO2RR) is the lack of efficient and selective electrocatalysts to valuable chemicals. Hydrocarbons and valuable chemicals from the CO2RR h...

Metal oxides and oxyhydroxides exhibit state-of-the-art activity for the oxygen evolution reaction (OER); however, their reaction mechanism, particularly the relationship between charging of the oxide and OER kinetics, remains elusive. Here, we investigate a series of Mn-, Co-, Fe-, and Zn-doped nickel oxides using operando UV-vis spectroscopy coupled with time-resolved stepped potential spectroelectrochemistry. The Ni2+/Ni3+redox peak potential is found to shift anodically from Mn- < Co- < Fe- < Zn-doped samples, suggesting a decrease in oxygen binding energetics from Mn- to Zn-doped samples. At OER-relevant potentials, using optical absorption spectroscopy, we quantitatively detect the subsequent oxidation of these redox centers. The OER kinetics was found to have a second-order dependence on the density of these oxidized species, suggesting a chemical rate-determining step involving coupling of two oxo species. The intrinsic turnover frequency per oxidized species exhibits a volcano trend with the binding energy of oxygen on the Ni site, having a maximum activity of ∼0.05 s-1at 300 mV overpotential for the Fe-doped sample. Consequently, we propose that for Ni centers that bind oxygen too strongly (Mn- and Co-doped oxides), OER kinetics is limited by O-O coupling and oxygen desorption, while for Ni centers that bind oxygen too weakly (Zn-doped oxides), OER kinetics is limited by the formation of oxo groups. This study not only experimentally demonstrates the relation between electroadsorption free energy and intrinsic kinetics for OER on this class of materials but also highlights the critical role of oxidized species in facilitating OER kinetics.