CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends

CuNi alloy anchored on dual-substrate TiOx/N-doped carbon nanofibers as a bifunctional electrocatalyst for rechargeable zinc-air batteries

Development of non-noble metal-based electrocatalysts to enhance the performance of zinc-air batteries (ZABs) is of great significance, but it remains a formidable challenge due to their poor stability and activity. Herein, a bifunctional CuNi-TiOx/NCNFS electrocatalyst, featuring with electron-rich copper-nickel (CuNi) alloy nanoparticles anchored on titanium oxide/N-doped carbon nanofibers (TiOx/NCNFS), is constructed by a dual-substrate loading strategy. The introduction of TiOx has led to a significant increase in the stability of the dual-substrate. The strong electronic interaction between CuNi and TiOx strengthens the anchoring of active metal sites, thus accelerating the electron transfer. Theoretical calculations unclose that NCNFS can regulate the charge distribution of TiOx, inducing the charge transfer from NCNFS → TiOx → CuNi, thereby reducing the d-band center of Cu and Ni, which is beneficial to the desorption of intermediate oxide species of the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). Therefore, CuNi-TiOx/NCNFS delivers a remarkable bifunctional performance with a low OER overpotential of 258 mV at 10 mA cm-2 and an ORR half-wave potential of 0.85  V. When assembled into ZABs, CuNi-TiOx/NCNFS shows a low potential gap of 0.64 V, a higher power density of 149.6 mW cm-2 at 330 mA cm-2, and an outstanding stability for 250 h at 5mA cm-2. This study provides a novel approach by constructing dual-substrate to tune the electronic structure of active metal sites for efficient rechargeable ZABs.

Tuning the product selectivity of single-atom catalysts for CO2 reduction beyond CO formation by orbital engineering

Electrochemical CO2 reduction (CO2R) is one of the promising strategies for developing sustainable energy resources. Single-atom catalysts (SACs) have emerged as efficient catalysts for CO2R. However, the efficiency of SACs for the formation of reduction products beyond two-step CO formation is low due to the lower binding strength of the CO intermediate. In this study, we present an orbital engineering strategy based on density functional theory calculations and the fragment molecular orbital approach to tune product selectivity for the CO2R reaction on macrocycle based molecular catalysts (porphyrin and phthalocyanine) and extended SACs (graphene and covalent organic frameworks) with Fe, Co, and Ni dopants. The introduction of neutral axial ligands such as imidazole, pyridine, and trimethyl phosphine to the metal dopants enhances the binding affinity of the CO intermediate. The stability of the catalysts is investigated through the thermodynamic binding energy of the axial ligands and ab initio molecular dynamics simulations (AIMD). The grand canonical potential method is used to determine the reaction free energy values. Using a unified activity volcano plot based on the reaction free energy values, we investigated the catalytic activity and product selectivity at an applied potential of -0.8 V vs. SHE and a pH of 6.8. We found that with the imidazole and pyridine axial ligands, the selectivity of Fe-doped SACs towards the formation of the methanol product is improved. The activity volcano plot for these SACs shows a similar activity to that of the Cu (211) surface. The catalytic activity is found to be directly proportional to the sigma-donating ability of the axial ligands.

On the nature of Cu-carbon interaction through N-modification for enhanced ethanol synthesis from syngas and methanol

Direct conversion of syngas into ethanol is an attractive process because of its short route and high-added value, but remains an enormous challenge due to the low selectivity caused by unclear active sites. Here, the Cu(111) supported N-modified graphene fragments C13-mNm/Cu(111) (m = 0-2) are demonstrated to be an efficient catalyst for fabricating ethanol from syngas and methanol. Our results suggest that the Cu-carbon interaction not only facilitates CO activation, but also significantly affects the adsorption stability of C2 intermediates and finally changes the fundamental reaction mechanism. The impeded hydrogenation performance of C13/Cu(111) due to the introduced Cu-carbon interaction is dramatically improved by N-doping. Multiple analyses reveal that the promoted electron transfer and the enhanced electron endowing ability of C13-mNm/Cu(111) (m = 1-2) to the co-adsorbed CH3CHxOH (x = 0-1) and H are deemed to be mainly responsible for the remarkable enhancement in hydrogenation ability. From the standpoint of the frontier molecular orbital, the decreased HOMO-LUMO gap and the increased overlap extent of HOMO and LUMO with the doping of N atoms also further verify the more facile hydrogenation reactions. Clearly, the Cu-carbon interaction through N-modification is of critical importance in ethanol formation. The final hydrogenation reaction during ethanol formation is deemed to be the rate-controlling step. The insights gained here could shed new light on the nature of Cu-carbon interaction in carbon material modified Cu-based catalysts for ethanol synthesis, which could be extended to design and modify other metal-carbon catalysts.

Hybridization State Transition under Working Conditions: Activity Origin of Single-Atom Catalysts

Single-atom catalysts (SACs) have been widely investigated and have emerged as a transformative approach in electrocatalysis. Despite their clear structure, the origin of their exceptional activity remains elusive. Herein, we elucidate a common phenomenon of the hybridization state transition of metal centers, which is responsible for the activity origin across various SACs for different reactions. Focusing on N-doped carbon-supported Ni SAC (NiN4 SAC) for CO2 reduction reaction (CO2RR), our comprehensive computations successfully clarify the hybridization state transition under working conditions and its relation with the activity. This transition, triggered by the reaction intermediates and applied potential, converts the Ni center from the inert dsp2 hybridization state to the active d2sp3 hybridization state. Importantly, the calculated activity and selectivity of the CO2RR over the d2sp3-hybridized Ni center are consistent with the experimental results, offering strong support for the proposed hypothesis. This work suggests a universal principle of electronic structure evolution in SACs that could revolutionize catalyst design, which also introduces a new paradigm for manipulating electronic states to enhance catalytic performance, with implications for various reactions and catalyst platforms.

Demagnetizing Ferromagnetic Catalysts to the Sabatier Optimal of Haber-Bosch Process

Achieving the Sabatier optimal of a chemical reaction has been the central topic in heterogeneous catalysis for a century. However, this ultimate goal was greatly hindered in previous catalyst design strategies since the active sites indeed changed. Fortunately, the magneto-catalytic effect (MCE) provides a promising solution to this long-standing challenge. Recent research suggests that the performance of ferromagnetic catalysts is capable to be promoted without changing its chemical structure. Herein, we use time-dependent density functional perturbation theory (TDDFPT) calculations to elucidate that a partially demagnetized (DM) ferromagnet could be a Sabatier optimal catalyst. Using ammonia synthesis as the model reaction, we determined the activity of Cobalt at each DM state by including the magnetic thermal excitations via magnon analysis, making the 55% DM Co to the genuine Sabatier optimal. As an essential but underexcavated phenomenon in heterogeneous catalysis, the MCE will open a new avenue to design high-performance catalysts.

Number of sites-based solver for determining coverages from steady-state mean-field micro-kinetic models

Kinetic models parameterized by ab-initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady-state coverages and rates of mean-field micro-kinetic models. As implemented in the open-source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root-finding algorithm to determine the coverage of all intermediates through the steady-state expressions, constraining all coverages to be non-negative and to properly sum to unity. Though intuitive, this root-finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean-field steady-state micro-kinetic model in the space of number of sites instead of solving it in the space of coverages. We transform the constrained root-finding problem to an unconstrained least-squares minimization problem, leading to significantly improved convergence in solving micro-kinetic models and thus enabling the efficient study of more complex catalytic reactions.

Benchmarking pH-field coupled microkinetic modeling against oxygen reduction in large-scale Fe-azaphthalocyanine catalysts

Molecular metal-nitrogen-carbon (M-N-C) catalysts with well-defined structures and metal-coordination environments exhibit distinct structural properties and excellent electrocatalytic performance, notably in the oxygen reduction reaction (ORR) for fuel cells. Metal-doped azaphthalocyanine (AzPc) catalysts, a variant of molecular M-N-Cs, can be structured with unique long stretching functional groups, which make them have a geometry far from a two-dimensional geometry when loaded onto a carbon substrate, similar to a "dancer" on a stage, and this significantly affects their ORR efficiency at different pH levels. However, linking structural properties to performance is challenging, requiring comprehensive microkinetic modeling, substantial computational resources, and a combination of theoretical and experimental validation. Herein, we conducted pH-dependent microkinetic modeling based upon ab initio calculations and electric field-pH coupled simulations to analyze the pH-dependent ORR performance of carbon-supported Fe-AzPcs with varying surrounding functional groups. In particular, this study incorporates large molecular structures with complex long-chain "dancing patterns", each featuring >650 atoms, to analyze their performance in the ORR. Comparison with experimental ORR data shows that pH-field coupled microkinetic modeling closely matches the observed ORR efficiency at various pH levels in Fe-AzPc catalysts. Our results also indicate that assessing charge transfer at the Fe-site, where the Fe atom typically loses around 1.3 electrons, could be a practical approach for screening appropriate surrounding functional groups for the ORR. This study provides a direct benchmarking analysis for the microkinetic model to identify effective M-N-C catalysts for the ORR under various pH conditions.

Spin-mediated promotion of Co catalysts for ammonia synthesis

Over the past two decades, there has been growing interest in developing catalysts to enable Haber-Bosch ammonia synthesis under milder conditions than currently pertain. Rational catalyst design requires theoretical guidance and clear mechanistic understanding. Recently, a spin-mediated promotion mechanism was proposed to activate traditionally unreactive magnetic materials such as cobalt (Co) for ammonia synthesis by introducing hetero metal atoms bound to the active site of the catalyst surface. We combined theory and experiment to validate this promotion mechanism on a lanthanum (La)/Co system. By conducting model catalyst studies on Co single crystals and mass-selected Co nanoparticles at ambient pressure, we identified the active site for ammonia synthesis as the B5 site of Co steps with La adsorption. The turnover frequency of 0.47 ± 0.03 per second achieved on the La/Co system at 350°C and 1 bar surpasses those of other model catalysts tested under identical conditions.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

High-Throughput Screening of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Learning and Microkinetic Modeling

The catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge3Cu1, Ge3Ni1, Ge3Co1, and Ge3Fe1) and the Ni3Cu1 alloy were identified as promising and cost-effective sulfur-resistant catalysts.

Theoretical Approach toward a Mild Condition Haber-Bosch Process on the Zeolite Catalyst with Confined Dual Active Sites

The Haber-Bosch (H-B) process is today's dominant technology for ammonia production, but achieving a mild reaction condition is still challenging. Herein, we combined density functional theory (DFT) calculations and microkinetic modeling (MKM) to demonstrate the feasibility of conducting the H-B process under ambient conditions on a zeolite catalyst with confined dual active sites. Our designed dual Mo(II) cation-anchored ferrierite [2Mo(II)-FER] catalyst shows an energy barrier of only 0.58 eV for N≡N bond breaking due to the enhanced π-back-donation. Meanwhile, the three hydrogen sources (BH, FMH, and NMH) within 2Mo(II)-FER greatly enrich the hydrogenation mechanisms of NHx species, resulting in barriers of <1.1 eV for NHx (x = 0-2) hydrogenations. This dual-site catalyst properly decouples the N2 dissociation and NHx hydrogenation steps, which elegantly circumvents the linear scaling relation between the N2 dissociation barrier and the nitrogen binding energy. It is worth noting that our MKM results show 4 orders of magnitude higher reaction rates on 2Mo(II)-FER than the stepped sites of the FCC Ru catalyst at low temperatures, paving a solid basis to conduct the H-B process at low temperatures. We believe that our strategy will provide crucial guidance for synthesizing state-of-the-art zeolite catalysts to achieve the near-ambient condition H-B process and other chemical reactions in heterogeneous catalysis.

Computational screening of single-atom doped In2O3 catalysts for the reverse water gas shift reaction

The reverse water gas shift (RWGS) reaction is an important method for converting carbon dioxide (CO2) into valuable chemicals and fuels by hydrogenation. In this paper, the catalytic activity of single-atom metal-doped (M = Pt, Ir, Pd, Rh, Cu, Ni) indium oxide (c-In2O3) catalysts in the cubic phase for the RWGS reaction was investigated using density functional theory (DFT) calculations. This was achieved by identifying metal sites, screening oxygen vacancies, followed by further calculating the energy barriers for the direct and indirect dissociation pathways of the RWGS reaction. Our results show that the single-atom dopant in the indium oxide lattice promotes the creation of oxygen vacancies on the In2O3 surface, thereby facilitating the adsorption and activation of CO2 by the oxide surface and initiating the subsequent RWGS reaction. Furthermore, we find that the oxygen vacancy (OV) formation energy on the surface of the single-atom metal doped c-In2O3(111) surface can be used as a descriptor for CO2 adsorption, and the higher the OV formation energy, the more stable the CO2 adsorption structure is. The Cu/In2O3 structure has relatively high energy barriers for both direct (1.92 eV) and indirect dissociation (2.09 eV) in the RWGS reaction, indicating its low RWGS reactivity. In contrast, the Ir/In2O3 and Rh/In2O3 structures are more conducive to the direct dissociation of CO2 into CO, which may serve as more efficient RWGS catalysts. Furthermore, microkinetic simulations show that single atom metal doping to In2O3 enhances CO2 conversion, especially under high reaction temperatures, where the formation of oxygen vacancies is the limiting factor for CO2 reactivity on the M/In2O3 (M = Cu, Ir, Rh) models. Among these three single-atom catalysts, the Ir/In2O3 model was predicted to have the best CO2 reactivity at reaction temperatures above 573 K.

First-principles study of CO2 hydrogenation on Cd-doped ZrO2: Insights into the heterolytic dissociation of H2

Cd-doped ZrO2 catalyst has been found to have high selectivity and activity for CO2 hydrogenation to methanol. In this work, density functional theory calculations were carried out to investigate the microscopic mechanism of the reaction. The results show that Cd doping effectively promotes the generation of oxygen vacancies, which significantly activate the CO2 with stable adsorption configurations. Compared with CO2, gaseous H2 adsorption is more difficult, and it is mainly dissociated and adsorbed on the surface as [HCd-HO]* or [HZr-HO]* compact ion pairs, with [HCd-HO]* having the lower energy barrier. The reaction pathways of CO2 to methanol has been investigated, revealing the formate path as the dominated pathway via HCOO* to H2COO* and to H3CO*. The hydrogen anions, HCd* and HZr*, significantly reduce the energy barriers of the reaction.

Automated MUltiscale simulation environment

Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/In2O3 catalyst employed for the CO2 hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level.

Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning

Despite today's commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.

Thermodynamic and kinetic modeling of electrocatalytic reactions using a first-principles approach

The computational modeling of electrochemical interfaces and their applications in electrocatalysis has attracted great attention in recent years. While tremendous progress has been made in this area, however, the accurate atomistic descriptions at the electrode/electrolyte interfaces remain a great challenge. The Computational Hydrogen Electrode (CHE) method and continuum modeling of the solvent and electrolyte interactions form the basis for most of these methodological developments. Several posterior corrections have been added to the CHE method to improve its accuracy and widen its applications. The most recently developed grand canonical potential approaches with the embedded diffuse layer models have shown considerable improvement in defining interfacial interactions at electrode/electrolyte interfaces over the state-of-the-art computational models for electrocatalysis. In this Review, we present an overview of these different computational models developed over the years to quantitatively probe the thermodynamics and kinetics of electrochemical reactions in the presence of an electrified catalyst surface under various electrochemical environments. We begin our discussion by giving a brief picture of the different continuum solvation approaches, implemented within the ab initio method to effectively model the solvent and electrolyte interactions. Next, we present the thermodynamic and kinetic modeling approaches to determine the activity and stability of the electrocatalysts. A few applications to these approaches are also discussed. We conclude by giving an outlook on the different machine learning models that have been integrated with the thermodynamic approaches to improve their efficiency and widen their applicability.

Mn/Ce@HKUST-1 for Efficient Removal of Gaseous Thallium: Insights from Kinetic and Experimental Studies

Gaseous thallium (Tl) pollution events, primarily caused by non-ferrous mineral refineries and fossil fuel combustion, have increased over the past few decades. To prevent gaseous Tl distribution from flue gas, MnO2/CeO2@HKUST-1 (MCH) was synthesized and found to achieve a gaseous Tl(I) removal level of up to 90% at 423 K, a weight hourly space velocity (WHSV) of 2000 h-1/mL with an Mn dose of 10%, maintained over 10 h. The best Mn/Ce ratio was found to be 9:1. To further investigate surface kinetic behavior, four commonly used kinetic models were applied, including the Eley-Rideal (ER) model, Langmuir-Hinshelwood (LH) model, Mars-van Krevelen (MVK) model, and pseudo-first-order (PFO) model. While the ER and LH models had the slightest deviation, the MVK model was the most reliable. The CatMAP software was also used to match the simulation deviation. This work demonstrated the Tl removal mechanism and provided insights into the accuracy of kinetic models on minor-radius heavy metal. Thus, this research may help promote the design of reactors, heavy metal removal rates, and flue gas purification technology selection.

Unraveling the reaction mechanisms for furfural electroreduction on copper

Electrochemical routes for the valorization of biomass-derived feedstock molecules offer sustainable pathways to produce chemicals and fuels. However, the underlying reaction mechanisms for their electrochemical conversion remain elusive. In particular, the exact role of proton-electron coupled transfer and electrocatalytic hydrogenation in the reaction mechanisms for biomass electroreduction are disputed. In this work, we study the reaction mechanism underlying the electroreduction of furfural, an important biomass-derived platform chemical, combining grand-canonical (constant-potential) density functional theory-based microkinetic simulations and pH dependent experiments on Cu under acidic conditions. Our simulations indicate the second PCET step in the reaction pathway to be the rate- and selectivity-determining step for the production of the two main products of furfural electroreduction on Cu, i.e., furfuryl alcohol and 2-methyl furan, at moderate overpotentials. We further identify the source of Cu's ability to produce both products with comparable activity in their nearly equal activation energies. Furthermore, our microkinetic simulations suggest that surface hydrogenation steps play a minor role in determining the overall activity of furfural electroreduction compared to PCET steps due to the low steady-state hydrogen coverage predicted under reaction conditions, the high activation barriers for surface hydrogenation and the observed pH dependence of the reaction. As a theoretical guideline, low pH (<1.5) and moderate potential (ca. -0.5 V vs. SHE) conditions are suggested for selective 2-MF production.

Promoted coke resistance of Ni by surface carbon for the dry reforming of methane

Dry reforming of methane (DRM) is an efficient process to transform methane and carbon dioxide to syngas. Nickel could show good catalytic activity for DRM, whereas the deactivation of nickel surfaces by the formation of inert carbon structures is inevitable. In this study, we carry out a detailed investigation of the evolution and catalytic performance of the carbon-covered surface structure on Ni(100) with a combined density functional theory and microkinetic modeling approach. The results suggest that the pristine Ni(100) surface is prone to carbon deposition and accumulation under reaction conditions. Further studies show that over this carbon-covered reconstructed Ni(100) surface, a carbon-based Mars-van-Krevelen mechanism would be favored, and the activity and coke resistance is promoted. This surface state and reaction mechanism were rarely reported before and would provide more insights into the DRM process under real reaction conditions and would help design more stable Ni catalysts.

Single-atom catalysis enabled by high-energy metastable structures

Owing to limited degrees of freedom, the active sites of stable single-atom catalyst (SAC) often have one structure that is energetically much lower than other local-minimum structures. Thus, the SAC adopts one lowest-energy structure (LES) with an overwhelmingly larger proportion than any other high-energy metastable structure (HEMS), and the LES is commonly assumed to be solely responsible for the catalytic performance of an SAC. Herein, we demonstrate with SACs anchored on CeO₂ that the HEMS of an SAC, even though its proportion remains several orders of magnitude lower than the LES throughout the catalytic reaction, can dictate catalysis with extraordinary activity arising from its unique coordination environment and oxidation state. Thus, we unravel the key role of HEMS-enabled catalysis in single-atom catalysis, which shakes the common assumption in the studies of SACs and urges new developments in both experiment and theory to identify and exploit catalysis via HEMSs.

Continuous-flow electrosynthesis of ammonia by nitrogen reduction and hydrogen oxidation

Ammonia is a critical component in fertilizers, pharmaceuticals, and fine chemicals and is an ideal, carbon-free fuel. Recently, lithium-mediated nitrogen reduction has proven to be a promising route for electrochemical ammonia synthesis at ambient conditions. In this work, we report a continuous-flow electrolyzer equipped with 25-square centimeter-effective area gas diffusion electrodes wherein nitrogen reduction is coupled with hydrogen oxidation. We show that the classical catalyst platinum is not stable for hydrogen oxidation in the organic electrolyte, but a platinum-gold alloy lowers the anode potential and avoids the decremental decomposition of the organic electrolyte. At optimal operating conditions, we achieve, at 1 bar, a faradaic efficiency for ammonia production of up to 61 ± 1% and an energy efficiency of 13 ± 1% at a current density of -6 milliamperes per square centimeter.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt₃Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt₃Ir, and Pt₃Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Overreact, an in silico lab: Automative quantum chemical microkinetic simulations for complex chemical reactions

Today's demand for precisely predicting chemical reactions from first principles requires research to go beyond Gibbs' free energy diagrams and consider other effects such as concentrations and quantum tunneling. The present work introduces overreact, a novel Python package for propagating chemical reactions over time using data from computational chemistry only. The overreact code infers all differential equations and parameters from a simple input that consists of a set of chemical equations and quantum chemistry package outputs for each chemical species. We evaluate some applications from the literature: gas-phase eclipsed-staggered isomerization of ethane, gas-phase umbrella inversion of ammonia, gas-phase degradation of methane by chlorine radical, and three solvation-phase reactions. Furthermore, we comment on a simple solvation-phase acid-base equilibrium. We show how it is possible to achieve reaction profiles and information matching experiments.

Positive Valent Metal Sites in Electrochemical CO2 Reduction Reaction

The discovery of high-performance catalysts for the electrochemical CO₂ reduction reaction (CO₂ RR) has faced an enormous challenge for years. The lack of cognition about the surface active structures or centers of catalysts in complex conditions limits the development of advanced catalysts for CO₂ RR. Recently, the positive valent metal sites (PVMS) are demonstrated as a kind of potential active sites, which can facilitate carbon dioxide (CO₂ ) activation and conversation but are always unstable under reduction potentials. Many advanced technologies in theory and experiment have been utilized to understand and develop excellent catalysts with PVMS for CO₂ RR. Here, we present an introduction of some typical catalysts with PVMS in CO₂ RR and give some understanding of the activity and stability for these related catalysts.

Analysing oxygen reduction electrocatalysis on transition metal doped niobium oxide(110)

A good oxygen reduction reaction (ORR) catalyst should be stable and active under electrochemical reaction conditions. Niobium pentaoxide (Nb₂O₅) is known to be stable under ORR conditions. However it has a large band gap, which makes conductivity a challenge during the reaction. In this work, we aim to understand if surface modification of the 110 facet of niobium pentaoxide with transition metal doping has any effect on its ORR activity and conductivity. While the problem of conductivity in the case of transition metal oxides (TMOs) can be partially rectified by transition metal doping, it has negligible influence on the ORR activity of the doped systems.

Promotional Effect of H2 Pretreatment on the CO PROX Performance of Pt1/Co3O4: A First-Principles-Based Microkinetic Analysis

Atomic Pt studded on cobalt oxide is a promising catalyst for CO preferential oxidation (PROX) dependent on its surface treatment. In this work, the CO PROX reaction mechanism on Co₃O₄ supported single Pt atom is investigated by a comprehensive first-principles based microkinetic analysis. It is found that as synthesized Pt₁/Co₃O₄ interface is poisoned by CO in a wide low temperature window, leading to its low reactivity. The CO poisoning effect can be effectively mitigated by a H₂ prereduction treatment, that exposes Co ∼ Co dimer sites for a noncompetitive Langmuir-Hinshelhood mechanism. In addition, surface H atoms assist O₂ dissociation via "twisting" mechanism, avoiding the high barriers associated with direct O₂ dissociation path. Microkinetic analysis reveals that the promotion of H-assisted pathway on H₂ treated sample helps improve the activity and selectivity at low temperatures.

Limits to scaling relations between adsorption energies?

Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. However, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalized adsorbate valence energies of lower-lying oxygen vs higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns-Anderson hybridization energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other.

Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling

Microkinetic modeling based on density functional theory (DFT) energies plays an essential role in heterogeneous catalysis because it reveals the fundamental chemistry for catalytic reactions and bridges the microscopic understanding from theoretical calculations and experimental observations. Microkinetic modeling requires building a set of ordinary differential equations (ODEs) based on the calculation results of thermodynamic properties of adsorbates and kinetic parameters for the reaction elementary steps. Solving a microkinetic model can extract information on catalytic chemistry, including critical reaction intermediates, reaction pathways, the surface species distribution, activity, and selectivity, thus providing vital guidelines for altering catalysts.

However, the quantitative reliability of traditional microkinetic models is often insufficient to conclusively extrapolate the mechanistic details of complex reaction systems. This can be attributed to several factors, the most important of which is the limitation of obtaining an accurate estimation of the energy inputs via traditional calculation methods. These limitations include the difficulty of using static DFT methods to calculate reaction energies of adsorption/desorption processes, often rate-controlling or selectivity-determining steps, and the inadequate consideration of surface coverage effects. In addition, the robust microkinetic software is rare, which also complicates the resolution of complex catalytic systems.

In this Account, we review our recent works toward refining the predictions of microkinetic modeling in heterogeneous catalysis and achieving theory–experiment parity for activity and selectivity. First, we introduce CATKINAS, a microkinetic software developed in our group, and show how it disentangles the problem that traditional microkinetic software has and how it can now be applied to obtain kinetic results for more sophisticated reaction systems. Second, we describe a molecular dynamics method developed recently to obtain the free-energy changes for the adsorption/desorption process to fill in the missing energy inputs. Third, we show that a rigorous consideration of surface coverage effects is pivotal for building more realistic models and obtaining accurate kinetic results. Following a series of studies on acetylene hydrogenation reactions on Pd catalysts, we demonstrate how this new approach can provide an improved quantitative understanding of the mechanism, active site, and intrinsic structural sensitivity. Finally, we conclude with a brief outlook and the remaining challenges in this field.