Analyzing the Case for Bifunctional Catalysis

Understanding Cooperativity in Homo- and Heterometallic Complexes: From Basic Concepts to Design

Cooperative effects have attracted considerable attention in recent years. These effects are ubiquitous in chemistry and biology and can govern interactions of proteins with other biomolecules, mechanisms of supramolecular recognition and polymerization, catalysis, assembly of compounds on surfaces, and physical properties such as magnetic, electronic or optical properties, e. g. Consequently, the understanding of cooperative effects can lead to a structure-property relation that can pave the way to future applications in various research areas; however, with regard to cooperative effects in homo- and heterometallic complexes, we still are at the beginning of understanding. Nevertheless, concepts to describe cooperativity of metal centers as well as methodologies to investigate and model these effects have emerged over the last years. This concept article gives an overview of these existing concepts, approaches, and strategies to understand cooperative effects in homo- and heterometallic complexes. Special emphasis is put on concepts to define cooperative effects, their quantification, as well as methods to investigate cooperative effects.

Titanium Oxycarbide as Platinum-Free Electrocatalyst for Ethanol Oxidation

The compound material titanium oxycarbide (TiOC) is found to be an effective electrocatalyst for the electrochemical oxidation of ethanol to CO2. The complete course of this reaction is one of the main challenges in direct ethanol fuel cells (DEFCs). While TiOC has previously been investigated as catalyst support material only, in this study we show that TiOC alone is able to oxidize ethanol to acetaldehyde without the need of expensive noble metal catalysts like Pt. It is suggested that this behavior is attributed to the presence of both undercoordinated sites, which allow ethanol to adsorb, and oxygenated sites, which facilitate the activation of water. This is a milestone in DEFC research and development and opens up innovative possibilities for the design of catalyst materials for intermediate temperature fuel cells.

Combining Renewable Electricity and Renewable Carbon: Understanding Reaction Mechanisms of Biomass-Derived Furanic Compounds for Design of Catalytic Nanomaterials

ConspectusDespite the growing deployment of renewable energy conversion technologies, a number of large industrial sectors remain challenging to decarbonize. Aviation, heavy transport, and the production of steel, cement, and chemicals are heavily dependent on carbon-containing fuels and feedstocks. A hopeful avenue toward carbon neutrality is the implementation of renewable carbon for the synthesis of critical fuels, chemicals, and materials. Biomass provides an opportune source of renewable carbon, naturally capturing atmospheric CO2 and forming multicarbon linkages and useful chemical functional groups. The constituent molecules nonetheless require various chemical transformations, often best facilitated by catalytic nanomaterials, in order to access usable final products.Catalyzed transformations of renewable biomass compounds may intersect with renewable energy production by offering a means to utilize excess intermittent electricity and store it within chemical bonds. Electrochemical catalytic processes can often offer advantages in energy efficiency, product selectivity, and modular scalability compared to thermal-driven reactions. Electrocatalytic reactions with renewable carbon feedstocks can further enable related processes such as water splitting, where value-adding organic oxidation reactions may replace the evolution of oxygen. Organic electroreduction reactions may also allow desirable hydrogenations of bonds without intermediate formation of H2 and need for additional reactors.This Account highlights recent work aimed at gaining a fundamental understanding of transformations involving biomass-derived molecules in electrocatalytic nanomaterials. Particular emphasis is placed on the oxidation of biomass derived furanic compounds such as furfural and 5-hydroxymethylfurfural (HMF), which can yield value-added chemicals, including furoic acid (FA), maleic acid (MA), and 2,5-furandicarboxylic acid (FDCA) for renewable materials and other commodities. We highlight advanced implementations of online electrochemical mass spectrometry (OLEMS) and vibrational spectroscopies such as attenuated total reflectance surface enhanced infrared reflection absorption spectroscopy (ATR-SEIRAS), combined with microkinetic models (MKMs) and quantum chemical calculations, to shed light on the elementary mechanistic pathways involved in electrochemical biomass conversion and how these paths are influenced by catalytic nanomaterials. Perspectives are given on the potential opportunities for materials development toward more efficient and selective carbon-mitigating reaction pathways.

Bifunctional hydroformylation on heterogeneous Rh-WOx pair site catalysts

Metal-catalysed reactions are often hypothesized to proceed on bifunctional active sites, whereby colocalized reactive species facilitate distinct elementary steps in a catalytic cycle^1-8. Bifunctional active sites have been established on homogeneous binuclear organometallic catalysts^9-11. Empirical evidence exists for bifunctional active sites on supported metal catalysts, for example, at metal-oxide support interfaces^2,6,7,12. However, elucidating bifunctional reaction mechanisms on supported metal catalysts is challenging due to the distribution of potential active-site structures, their dynamic reconstruction and required non-mean-field kinetic descriptions^7,12,13. We overcome these limitations by synthesizing supported, atomically dispersed rhodium-tungsten oxide (Rh-WO_x) pair site catalysts. The relative simplicity of the pair site structure and sufficient description by mean-field modelling enable correlation of the experimental kinetics with first principles-based microkinetic simulations. The Rh-WO_x pair sites catalyse ethylene hydroformylation through a bifunctional mechanism involving Rh-assisted WO_x reduction, transfer of ethylene from WO_x to Rh and H₂ dissociation at the Rh-WO_x interface. The pair sites exhibited >95% selectivity at a product formation rate of 0.1 g_propanal cm^-3 h^-1 in gas-phase ethylene hydroformylation. Our results demonstrate that oxide-supported pair sites can enable bifunctional reaction mechanisms with high activity and selectivity for reactions that are performed in industry using homogeneous catalysts.

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.

Activation of N2 on Manganese Nitride-Supported Ni3 and Fe3 Clusters and Relevance to Ammonia Formation

Dual-site models were constructed to represent manganese nitride (Mn₄N)-supported Ni₃ and Fe₃ clusters for NH₃ synthesis. Density functional theory calculations produced an energy barrier of approximately 0.55 eV for N-N bond activation at the interfacial nitrogen vacancy sites (N_v); also, the hydrogenation and removal of interfacial N is promoted by earth-abundant Ni and Fe metals. Steady-state microkinetic modeling revealed that the turnover frequencies of NH₃ production follow an order of Fe₃@Mn₄N ≈ Ni₃@Mn₄N > Mn₄N > Fe ≫ Ni. Moreover, we present clear evidence that, before NH₃ formation, NH migrates from N_v onto the metallic sites. Using N binding energy (BE_N) and the transition-state energy of N₂ activation (E_TS) as descriptors, we concluded that the beneficial effects owing to interfacial N_v sites are the most pronounced when BE_N is either too strong or too weak while E_TS is high; otherwise, excessive N_v sites may hinder catalyst performance.

Visualizing Element Migration over Bifunctional Metal-Zeolite Catalysts and its Impact on Catalysis

The catalytic performance of composite catalysts is not only affected by the physicochemical properties of each component, but also the proximity and interaction between them. Herein, we employ four representative oxides (In₂ O₃ , ZnO, Cr₂ O₃ , and ZrO₂ ) to combine with H-ZSM-5 for the hydrogenation of CO₂ to hydrocarbons directed by methanol intermediate and clarify the correlation between metal migration and the catalytic performance. The migration of metals to zeolite driven by the harsh reaction conditions can be visualized by electron microscopy, meanwhile, the change of zeolite acidity is also carefully characterized. The protonic sites of H-ZSM-5 are neutralized by mobile indium and zinc species via a solid ion-exchange mechanism, resulting in a drastic decrease of C₂₊ hydrocarbon products over In₂ O₃ /H-ZSM-5 and ZnO/H-ZSM-5. While, the thermomigration ability of chromium and zirconium species is not significant, endowing Cr₂ O₃ /H-ZSM-5 and ZrO₂ /H-ZSM-5 catalysts with high selectivity of C₂₊ hydrocarbons.

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors

Heterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H₂) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation

The extraordinary mass activity of jagged Pt nanowires can substantially improve the economics of the hydrogen evolution reaction (HER). However, it is a great challenge to fully unveil the HER kinetics driven by the jagged Pt nanowires with their multiscale morphology. Herein we present an end-to-end framework that combines experiment, machine learning, and multiscale advances of the past decade to elucidate the HER kinetics catalyzed by jagged Pt nanowires under alkaline conditions. The bifunctional catalysis conventionally refers to the synergistic increase in activity by the combination of two different catalysts. We report that monometals, such as jagged Pt nanowires, can exhibit bifunctional characteristics owing to its complex surface morphology, where one site prefers electrochemical proton adsorption and another is responsible for activation, resulting in a 4-fold increase in the activity. We find that the conventional design guideline that the sites with a 0 eV Gibbs free energy of adsorption are optimal for HER does not hold under alkaline conditions, and rather, an energy between -0.2 and 0.0 eV is shown to be optimal. At the reaction temperatures, the high activity arises from low-coordination-number (≤7) Pt atoms exposed by the jagged surface. Our current demonstration raises an emerging prospect to understand highly complex kinetic phenomena on the nanoscale in full by implementing end-to-end multiscale strategies.

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

Spatial Confinement as an Effective Strategy for Improving the Catalytic Selectivity in Acetylene Hydrogenation

While control over chemical reactions is largely achieved by altering the intrinsic properties of catalysts, novel strategies are constantly being proposed to improve the catalytic performance in an extrinsic way. Since the fundamental chemical behavior of molecules can remarkably change when their molecular scale is comparable to the size of the space where they are located, creating spatially confined environments around the active sites offers new means of regulating the catalytic processes. We demonstrate through first-principles calculations that acetylene hydrogenation can exhibit significantly improved selectivity within the confined sub-nanospace between two-dimensional (2D) monolayers and the Pd(111) substrate. Upon intercalation of molecules, the lifting and undulation of a 2D monolayer on Pd(111) influence the adsorption energies of intermediates to varying extents, which, in turn, changes the energy profiles of the hydrogenation reactions. Within the confined sub-nanospace, the formation of ethane is always unfavorable, demonstrating effective suppression of the unwanted overhydrogenation. Moreover, the catalytic properties can be further tuned by altering the coverage of the adsorbates as well as strains within the 2D monolayer. Our results also indicate that for improving the selectivity, the strategy of spatial confinement could not be combined with that of single-atom catalysis, since the reactant molecules cannot enter the sub-nanospace due to the too weak adsorbate-substrate interaction. This work sheds new light on designing novel catalysts with extraordinary performance for the selective hydrogenation of acetylene.

Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis

The direct synthesis of complex chemicals from simple precursors (such as syngas) is one of the main objectives of current research in heterogeneous catalysis. To rationally design catalytic materials for this purpose, it is essential to identify the critical elementary reaction steps that ultimately determine a catalyst's activity and selectivity with respect to a desired product. Unfortunately, the number of potentially relevant elementary steps is in the thousands, even for relatively simple target species like ethanol. The challenge of identifying the critical steps is thus akin to finding the proverbial needle in a haystack. Recently, a model-reduction scheme has been proposed, which tackles this problem by prescreening the barriers of all potential reactions with computationally inexpensive approximations. Although this route appears highly promising, it raises the question of how the starting point of the model-reduction process can be determined. In this contribution, we present a systematic method for enumerating all intermediates and elementary reactions relevant to a chemical process of interest. Using this approach, we construct reaction networks for C,H,O-containing systems consisting of up to four non-hydrogen atoms (more than 1 million reactions). Importantly, the scheme goes beyond simple bond-breaking reactions and allows considering rearrangement and transfer reactions as well. The presented reaction networks thus cover the chemistry of syngas-based processes (and beyond) to an unprecedented scale.

Catalytic resonance theory: superVolcanoes, catalytic molecular pumps, and oscillatory steady state

Catalytic reactions on surfaces with forced oscillations in physical or electronic properties undergo controlled acceleration consistent with the selected parameters of frequency, amplitude, and external stimulus waveform.

Assessment of mean-field microkinetic models for CO methanation on stepped metal surfaces using accelerated kinetic Monte Carlo

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.