Scaling Relations in Homogeneous Catalysis: Analyzing the Buchwald–Hartwig Amination Reaction

One-Step Regioselective Synthesis of N-1-Substituted Dihydrouracils: A Motif of Growing Popularity in the Targeted Protein Degradation Field

The increasing popularity of the dihydrouracil motif in cereblon (CRBN) recruiting proteolysis-targeting chimeras (PROTACs) has necessitated the development of a facile, cost-effective, and high-yielding method for its introduction into molecules. To that end, we disclose herein an N-1 selective Pd-catalyzed cross-coupling of dihydrouracil with aryl electrophiles to provide access to medicinally relevant scaffolds in a single step. This approach exhibits excellent functional group tolerance and broad applicability to an abundance of (hetero)aryl halides and phenol derivatives and utilizes readily available catalyst/ligand systems. Thus, our strategy should find broad utility in the arena of PROTAC research, as it obviates the drawbacks of previous methodologies that rely on multistep synthetic routes and protecting group strategies to achieve N-1 selectivity.

Modeling Heterogeneous Catalysis and Electrocatalysis

With this special collection of articles with contributions from friends, former colleagues, collaborators, students, and postdocs, we celebrate Jens K. Nørskov′s belated 70th birthday. The studies reported here highlight just a small portion of the breadth and depth of Jens Nørskov's 40 years of influence in modeling heterogeneous catalysis and electrocatalysis. Many challenges remain in enabling in silico catalyst design, and the contributions in this special collection highlight the growing importance of machine learning approaches towards solving these challenges.

Desulfurization of Thiols for Nucleophilic Substitution

Although the desulfurization of thiols is a topic of great importance and has received significant attention, most efforts have focused on the hydrodesulfurization of thiols. In this work, we describe the desulfurization of thiols for nucleophilic substitution. This process occurs rapidly, promoted by the Ph3P/ICH2CH2I system, and can be extended to a wide range of nucleophiles. Notably, free amines can be employed as nucleophiles to synthesize various secondary and tertiary amines. This method tolerates a wide array of functional groups, including hydroxyl groups in amination reactions. Benzyl thiols are particularly reactive and can be completely converted at room temperature within 15 min. Although alkyl thiols show lower reactivity, they can also be converted smoothly at a reaction temperature of 70 °C overnight.

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Correlations between the Electronic Structure and Energetics of the Catalytic Steps in Homogeneous Water Oxidation Catalysis

The development of an efficient electrocatalyst for the water oxidation reaction is limited by unfavorable scaling relations between catalytic intermediates, resulting in an overpotential. In contrast to heterogeneous catalysts, the electronic structure of homogeneous catalysts can be modified to a great extent due to a tailored ligand design. However, studies utilizing the tunability of organic ligands have rarely been conducted in a systematic manner and, as of yet, have not produced catalytic paths that avoid the aforementioned unfavorable scaling relations. To investigate the influence of electron-donating groups (EDGs) or electron-withdrawing groups (EWGs) on elementary steps in electrochemical water oxidation catalysis, cis-[Ru(bpy)2(H2O)]2+ (bpy = 2,2'-bipyridine) was selected as the scaffold that was modified with methyl, methoxy, chloro, and trifluoromethyl groups. This catalyst can undergo several electron transfer (ET), proton transfer (PT), and proton-coupled electron transfer (PCET) steps that were all probed experimentally. In this systematic study, it was found that PCET steps are relatively insensitive with respect to the presence of EDGs or EWGs, while the decoupled ET and PT steps are more heavily affected. However, the influence of the substituents decreases with an increasing oxidation state of Ru due to a lack of d-electrons available at the Ru center for π-backbonding to the bipyridine ligand. Therefore, the RuV/VI redox couple appears to be relatively unaffected by the substituent. Nevertheless, the implementation of EWGs can shift all oxidation events to a very narrow potential window. Not only do our findings illustrate how electronic substituents affect the entire potential energy landscape of the catalytic water oxidation reaction, but they also show that the cis-[Ru(bpy)2(H2O)]2+ compounds follow different design rules and scaling relations, as has been reported for every other oxygen evolution catalyst thus far.

Pd-NHC catalysed regioselective activation of B(3,6)-H of o-carborane - a synergy between experiment and theory

Regioselective B-H activation of o-carboranes is an effective way for constructing o-carborane derivatives, which have broad applications in medicine, catalysis and the wider chemical industry. However, the mechanistic basis for the observed selectivities remains unresolved. Herein, a series of density functional theory (DFT) calculations were employed to characterise the palladium N-heterocyclic carbene (Pd-NHC) catalysed regioselective B(3,6)-diarylation of o-carboranes. Computational results at the IDSCRF(ether)-LC-ωPBE/BS1 and IDSCRF(ether)-LC-ωPBE/BS2 levels showed that the reaction undergoes a Pd(0) → Pd(II) → Pd(0) oxidation/reduction cycle, with the regioselective B(3)-H activation being the rate-determining step (RDS) for the full reaction profile. The computed RDS free energy barrier of 24.3 kcal mol-1 agrees well with the 82% yield of B(3,6)-diphenyl-o-carborane in ether solution at 298 K after 24 hours of reaction. The Ag2CO3 additive was shown to play a crucial role in lowering the RDS free energy barrier and facilitating the reaction. Natural charge population (NPA) and molecular surface electrostatic potential (ESP) analyses successfully predicted the experimentally observed regioselectivities, with electronic effects being revealed to be the dominant contributors to product selectivity. Steric hindrance was also shown to impact the reaction rate, as revealed by experimental and computational characterisation studies of substituents and ligand effects. Furthermore, computational predictions aligned with the experimental findings that NHC ligands outperform the phosphine ones for this particular reaction. Overall, the observed trends reported in this work are expected to assist in the rational optimisation of the efficiency and regioselectivity of this and related reactions.

Multiscale Model of CVD Growth of Graphene on Cu(111) Surface

Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments.

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.

Selective and high-rate CO2 electroreduction by metal-doped covalent triazine frameworks: a computational and experimental hybrid approach

The electrochemical CO2 reduction reaction (CO2RR) has attracted intensive attention as a technology to achieve a carbon-neutral society. The use of gas diffusion electrodes (GDEs) enables the realization of high-rate CO2RRs, which is one of the critical requirements for social implementation. Although both a high reaction rate and good selectivity are simultaneously required for electrocatalysts on GDEs, no systematic study of the relationship among active metal centers in electrocatalysts, reaction rate, and selectivity under high-rate CO2RR conditions has been reported. In the present study, we employed various metal-doped covalent triazine frameworks (M-CTFs) as platforms for CO2 reduction reaction (CO2RR) electrocatalysts on GDEs and systematically investigated them to deduce sophisticated design principles using a combined computational and experimental approach. The Ni-CTF showed both high selectivity (faradaic efficiency (FE) > 98% at -0.5 to -0.9 V vs. reversible hydrogen electrode) and a high reaction rate (current density < -200 mA cm-2) for CO production. By contrast, the Sn-CTF exhibited selective formic acid production, and the FE and partial current density reached 85% and 150 mA cm-2, respectively. These results for the CO2RR activity and selectivity at high current density with respect to metal centers correspond well with predictions based on first-principles calculations. This work is the first demonstration of a clear relationship between the computational adsorption energy of intermediates depending on metal species and the experimental high-rate gaseous CO2RR.

Breaking the Volcano-Shaped Relationship for Highly Efficient Electrocatalytic Nitrogen Reduction: A Computational Guideline

The volcano-shaped relationship is very common in electrocatalytic nitrogen reduction reaction (e-NRR) and is usually caused by the competition between the first and last hydrogenation steps. How to break such a relationship to further improve the catalytic performance remains a great challenge. Herein, using first-principles calculations, we investigate a range of transition-metal (TM)-doped Cu-based single-atom alloys (TM₁-Cu(111)) as catalysts for e-NRR. When the adsorption of N₂ on the catalysts is strong enough, the inert N₂ molecules can be effectively activated for the first hydrogenation step. Meanwhile, the last hydrogenation step is not affected by the scaling relationship and remains easy on all of the catalysts due to the unstable top-site adsorption of NH₂, resulting in the break of the volcano-shaped relationship in e-NRR. Thus, only the first hydrogenation step is identified as the potential determining step. Four TM₁-Cu(111) catalysts (TM = Re, W, Tc, and Mo) are selected as promising catalysts with limiting potential ranging from -0.38 to -0.56 V, showing outstanding e-NRR activity. Besides, the four catalysts also inhibit the competing hydrogen evolution reaction and long-term stability. Our work provides a guideline for breaking the volcano-shaped relationship in e-NRR and significant in the rational design of highly efficient electrocatalysts.

Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes

Volcano plots and activity maps are powerful tools for studying homogeneous catalysis. Once constructed, they can be used to estimate and predict the performance of a catalyst from one or more descriptor variables. The relevance and utility of these tools has been demonstrated in several areas of catalysis, with recent applications to homogeneous catalysts having been pioneered by our research group. Both volcano plots and activity maps are built from linear free energy scaling relationships that connect the value of a descriptor variable(s) with the relative energies of other catalytic cycle intermediates/transition states. These relationships must be both constructed and postprocessed appropriately to obtain the resulting plots/maps; this process requires careful execution to obtain meaningful results. In this protocol, we provide a step-by-step guide to building volcano plots and activity maps using curated reaction profile data. The reaction profile data are obtained using density functional theory computations to model the catalytic cycle. In addition, we provide volcanic, a Python code that automates the steps of the process following data acquisition. Unlike the computation of individual reaction energy profiles, our tools lead to a holistic view of homogeneous catalyst performance that can be broadly applied for both explanatory and screening purposes.

Electronic Ni–N interaction enhanced reductive amination on an N-doped porous carbon supported Ni catalyst

Reductive amination on Ni/N-doped porous carbon catalyst was enhanced by the formation of Ni–Nx sites and the electronic interaction of N and Ni species, which promoted the reductive amination of CO bonds and reduced the activation energy.

Efficient workflow for the investigation of the catalytic cycle of water oxidation catalysts: Combining GFN-xTB and density functional theory

Photocatalytic water oxidation remains the bottleneck in many artificial photosynthesis devices. The efficiency of this challenging process is inherently linked to the thermodynamic and electronic properties of the chromophore and the water oxidation catalyst (WOC). Computational investigations can facilitate the search for favorable chromophore‐catalyst combinations. However, this remains a demanding task due to the requirements on the computational method that should be able to correctly describe different spin and oxidation states of the transition metal, the influence of solvation and the different rates of the charge transfer and water oxidation processes. To determine a suitable method with favorable cost/accuracy ratios, the full catalytic cycle of a molecular ruthenium based WOC is investigated using different computational methods, including density functional theory (DFT) with different functionals (GGA, Hybrid, Double Hybrid) as well as the semi‐empirical tight binding approach GFN‐xTB. A workflow with low computational cost is proposed that combines GFN‐xTB and DFT and provides reliable results. GFN‐xTB geometries and frequencies combined with single‐point DFT energies give free energy changes along the catalytic cycle that closely follow the full DFT results and show satisfactory agreement with experiment, while significantly decreasing the computational cost. This workflow allows for cost efficient determination of energetic, thermodynamic and dynamic properties of WOCs.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Spatial-confinement induced electroreduction of CO and CO2 to diols on densely-arrayed Cu nanopyramids

The electroreduction of carbon dioxide (CO2) and carbon monoxide (CO) to liquid alcohol is of significant research interest. This is because of a high mass-energy density, readiness for transportation and established utilization infrastructure. Current success is mainly around monohydric alcohols, such as methanol and ethanol. There exist few reports on converting CO2 or CO to higher-valued diols such as ethylene glycol (EG; (CH2OH)2). The challenge to producing diols lies in the requirement to retain two oxygen atoms in the compound. Here for the first time, we demonstrate that densely-arrayed Cu nanopyramids (Cu-DAN) are able to retain two oxygen atoms for hydroxyl formation. This results in selective electroreduction of CO2 or CO to diols. Density Functional Theory (DFT) computations highlight that the unique spatial-confinement induced by Cu-DAN is crucial to selectively generating EG through a new reaction pathway. This structure promotes C-C coupling with a decreased reaction barrier. Following C-C coupling the structure facilitates EG production by (1) retaining oxygen and promoting the *COH-CHO pathway, which is a newly identified pathway toward ethylene glycol production; and, (2) suppressing the carbon-oxygen bond breaking in intermediate *CH2OH-CH2O and boosting hydrogenation to EG. Our findings will be of immediate interest to researchers in the design of highly active and selective CO2 and CO electroreduction to diols.

The Genesis of Molecular Volcano Plots

ConspectusFor the past two decades, linear free energy scaling relationships and volcano plots have seen frequent use as computational tools that aid in understanding and predicting the catalytic behavior of heterogeneous and electrocatalysts. Based on Sabatier's principle, which states that a catalyst should bind a substrate neither too strongly nor too weakly, volcano plots provide an estimate of catalytic performance (e.g., overpotential, catalytic cycle thermodynamics/kinetics, etc.) through knowledge of a descriptor variable. By the use of linear free energy scaling relationships, the value of this descriptor is employed to estimate the relative energies of other catalytic cycle intermediates/transition states. Postprocessing of these relationships leads to a volcano curve that reveals the anticipated performance of each catalyst, with the best species appearing on or near the peak or plateau. While the origin of volcanoes is undoubtedly rooted in examining heterogeneously catalyzed reactions, only recently has this concept been transferred to the realm of homogeneous catalysis. This Account summarizes the work done by our group in implementing and refining "molecular volcano plots" for use in analyzing and predicting the behavior of homogeneous catalysts.We begin by taking the reader through the initial proof-of-principle study that transferred the model from heterogeneous to homogeneous catalysis by examining thermodynamic aspects of a Suzuki-Miyaura cross-coupling reaction. By establishing linear free energy scaling relationships and reproducing the volcano shape, we definitively showed that volcano plots are also valid for homogeneous systems. On the basis of this key finding, we further illustrate how unified pictures of C-C cross-coupling thermodynamics were created using three-dimensional molecular volcanoes.The second section highlights an important transformation from "thermodynamic" to "kinetic" volcanoes by using the descriptor variable to directly estimate transition state barriers. Taking this idea further, we demonstrate how volcanoes can be used to directly predict an experimental observable, the turnover frequency. Discussion is also provided on how different flavors of molecular volcanoes can be used to analyze aspects of homogeneous catalysis of interest to experimentalists, such as determining the product selectivity and probing the substrate scope.The third section focuses on incorporating machine learning approaches into molecular volcanoes and invoking big-data-type approaches in the analysis of catalytic behavior. Specifically, we illustrate how machine learning can be used to predict the value of the descriptor variable, which facilitates nearly instantaneous screening of thousands of catalysts. With the large amount of data created from the machine learning/volcano plot tandem, we show how the resulting database can be mined to garner an enhanced understanding of catalytic processes. Emphasis is also placed on the latest generation of augmented volcano plots, which differ fundamentally from earlier volcanoes by elimination of the use of linear free energy scaling relationships and by assessment of the similarity of the complete catalytic cycle energy profile to that for an ideal reference species that is used to discriminate catalytic performance.We conclude by examining a handful of applications of molecular volcano plots to interesting problems in homogeneous catalysis and offering thoughts on the future prospects and uses of this new set of tools.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

Scaling Relationships and Volcano Plots in Homogeneous Catalysis

Scaling relations and volcano plots are widely used in heterogeneous catalysis. In this Perspective, we discuss the prospects and challenges associated with the application of similar concepts in homogeneous catalysis using examples from the literature that have appeared recently.

Data-powered augmented volcano plots for homogeneous catalysis

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce "augmented volcano plots" as a means to easily visualize the similarity of each catalyst's complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of closely lying species in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships from cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the augmented volcanoes by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species.

Surface chemistry dictates stability and oxidation state of supported single metal catalyst atoms

Single atom catalysts receive considerable attention due to reducing noble metal utilization and potentially eliminating certain side reactions. Yet, the rational design of highly reactive and stable single atom catalysts is hampered by the current lack of fundamental insights at the single atom limit. Here, density functional theory calculations are performed for a prototype reaction, namely CO oxidation, over different single metal atoms supported on alumina. The governing reaction mechanisms and scaling relations are identified using microkinetic modeling and principal component analysis, respectively. A large change in the oxophilicity of the supported single metal atom leads to changes in the rate-determining step and the catalyst resting state. Multi-response surfaces are introduced and built cheaply using a descriptor-based, closed form kinetic model to describe simultaneously the activity, stability, and oxidation state of single metal atom catalysts. A double peaked volcano in activity is observed due to competing rate-determining steps and catalytic cycles. Reaction orders of reactants provide excellent kinetic signatures of the catalyst state. Importantly, the surface chemistry determines the stability, oxidation, and resting state of the catalyst.

Imidazole-directed fabrication of three polyoxovanadate-based copper frameworks as efficient catalysts for constructing C–N bonds

Three polyoxovanadate-based copper frameworks with 3D, 2D and 1D networks have been developed and they displayed efficient heterogeneous catalytic activities in the Chan-Lam reaction.