Impact of Surface Ligand Identity and Density on the Thermodynamics of H Atom Uptake at Polyoxovanadate-Alkoxide Surfaces.

An understanding of how molecular structure influences the thermodynamics of H atom transfer is critical to designing efficient catalysts for reductive chemistries. Herein, we report experimental and theoretical investigations summarizing structure-function relationships of polyoxovanadate-alkoxides that influence bond dissociation free energies of hydroxide ligands located at the surface of the cluster. We evaluate the thermochemical descriptors of O-H bond strength for a series of clusters, namely [V6O13-x(OH)x(TRIOLR)2]-2 (x = 2, 4, 6; R = NO2, Me) and [V6O11-x(OMe)2(OH)x(TRIOLNO2)2]-2, via computational analysis and open circuit potential measurements. Our findings reveal that modifications to the TRIOL ligand (e.g., changing from the previously reported electron withdrawing nitro-backed ligand to the electron-donating methyl variant) have limited influence on the strength of surface O-H bonds as a result of near complete thermodynamic compensation in these systems (i.e., correlated changes in redox potential and cluster basicity). In contrast, changes in surface density of alkoxide ligands via direct alkoxylation of the polyoxovanadate-alkoxide surface result in measurable increases in bond dissociation free energies of surface O-H bonds for the mixed-valent derivatives. Our findings indicate that the extent of (de)localization of electron density across the cluster core has an impact on the bond dissociation free energies of surface O-H bonds across all oxidation states of the assembly.

Participation of electrochemically inserted protons in the hydrogen evolution reaction on tungsten oxides.

Understanding the mechanisms by which electrodes undergo the hydrogen evolution reaction (HER) is necessary to design better materials for aqueous energy storage and conversion. Here, we investigate the HER mechanism on tungsten oxide electrodes, which are stable in acidic electrolytes and can undergo proton-insertion coupled electron transfer concomitant with the HER. Electrochemical characterization showed that anhydrous and hydrated tungsten oxides undergo changes in HER activity coincident with changes in proton composition, with activity in the order HxWO3·H2O > HxWO3 > HxWO3·2H2O. We used operando X-ray diffraction and density functional theory to understand the structural and electronic changes in the materials at high states of proton insertion, when the oxides are most active towards the HER. H0.69WO3·H2O and H0.65WO3 have similar proton composition, structural symmetry, and electronic properties at the onset of the HER, yet exhibit different activity. We hypothesize that the electrochemically inserted protons can diffuse in hydrogen bronzes and participate in the HER. This would render the oxide volume, and not just the surface, as a proton and electron reservoir at high overpotentials. HER activity is highest in HxWO3·H2O, which optimizes both the degree of proton insertion and solid-state proton transport kinetics. Our results highlight the interplay between the HER and proton insertion-coupled electron transfer on transition metal oxides, many of which are non-blocking electrodes towards protons.

Single Atom Alloys Segregation in the Presence of Ligands.

Single atom alloys (SAAs) have gained remarkable attention due to their tunable properties leading to enhanced catalytic performance, such as high activity and selectivity. The stability of SAAs is dictated by surface segregation, which can be affected by the presence of surface adsorbates. Research efforts have primarily focused on the effect of commonly found catalytic reaction intermediates, such as CO and H, on the stability of SAAs. However, there is a knowledge gap in understanding the effect of ligands from colloidal nanoparticle (NP) synthesis on surface segregation. Herein, we combine density functional theory (DFT) and machine learning to investigate the effect of thiol and amine ligands on the stability of colloidal SAAs. DFT calculations revealed rich segregation energy (Eseg) data of SAAs with d8 (Pt, Pd, Ni) and d9 (Ag, Au, Cu) metals exposing (111) and (100) surfaces, in the presence and absence of ligands. Using these data, we developed an accurate four-feature neural network using a multilayer perceptron regression (NN MLP) model. The model captures the underlying physics behind surface segregation in the presence of adsorbed ligands by incorporating features representing the thermodynamic stability of metals through the bulk cohesive energy, structural effects using the coordination number of the dopant and the ligands, the binding strength of the adsorbate to the metals, strain effects using the Wigner-Seitz radius, and electronic effects through electron affinities. We found that the presence of ligands makes the thermodynamics of segregation milder compared to the bare (nonligated) SAA surfaces. Importantly, the adsorption configuration (e.g., top vs bridge) and the binding strength of the ligand to the SAA surface (e.g., amines vs thiols) play an important role in altering the Eseg trends compared to the bare surface. We also developed an accurate NN MLP model that predicts Eseg in the presence of ligands to find thermodynamically stable SAAs, leading to the rapid and efficient screening of colloidal SAAs. Our model captures several experimental observations and elucidates complex physics governing segregation at nanoscale interfaces.

The Role of Grain Boundary Sites for the Oxidation of Copper Catalysts during the CO Oxidation Reaction.

The oxidation of transition metal surfaces is a process that takes place readily at ambient conditions and that, depending on the specific catalytic reaction at hand, can either boost or hamper activity and selectivity. Cu catalysts are no exception in this respect since they exhibit different oxidation states for which contradicting activities have been reported, as, for example, in the catalytic oxidation of CO. Here, we investigate the impact of low-coordination sites on nanofabricated Cu nanoparticles with engineered grain boundaries on the oxidation of the Cu surface under CO oxidation reaction conditions. Combining multiplexed in situ single particle plasmonic nanoimaging, ex situ transmission electron microscopy imaging, and density functional theory calculations reveals a distinct dependence of particle oxidation rate on grain boundary density. Additionally, we found that the oxide predominantly nucleates at grain boundary-surface intersections, which leads to nonuniform oxide growth that suppresses Kirkendall-void formation. The oxide nucleation rate on Cu metal catalysts was revealed to be an interplay of surface coordination and CO oxidation behavior, with low coordination favoring Cu oxidation and high coordination favoring CO oxidation. These findings explain the observed single particle-specific onset of Cu oxidation as being the consequence of the individual particle grain structure and provide an explanation for widely distributed activity states of particles in catalyst bed ensembles.

Global and Local Connectivities Describe Hydrogen Intercalation in Metal Oxides.

We introduce global connectivity, Ω, which quantifies the detailed connectivity of a material and accurately captures reactivity behavior. We demonstrate that combining global and local connectivity describes how metal oxides interact with hydrogen. Blending density functional theory, graph theory, and machine learning we built a reactivity model which accurately predicts hydrogen intercalation potentials of different metal oxides experimentally measured in the lab. The use of global connectivity can accelerate materials design through the development of novel structure-property relationships.

Confined Ru Sites in a 13X Zeolite for Ultrahigh H2 Production from NH3 Decomposition.

Catalytic NH3 synthesis and decomposition offer a new promising way to store and transport renewable energy in the form of NH3 from remote or offshore sites to industrial plants. To use NH3 as a hydrogen carrier, it is important to understand the catalytic functionality of NH3 decomposition reactions at an atomic level. Here, we report for the first time that Ru species confined in a 13X zeolite cavity display the highest specific catalytic activity of over 4000 h-1 for the NH3 decomposition with a lower activation barrier, compared to most reported catalytic materials in the literature. Mechanistic and modeling studies clearly indicate that the N-H bond of NH3 is ruptured heterolytically by the frustrated Lewis pair of Ruδ+-Oδ- in the zeolite identified by synchrotron X-rays and neutron powder diffraction with Rietveld refinement as well as other characterization techniques including solid-state nuclear magnetic resonance spectroscopy, in situ diffuse reflectance infrared transform spectroscopy, and temperature-programmed analysis. This contrasts with the homolytic cleavage of N-H displayed by metal nanoparticles. Our work reveals the unprecedented unique behavior of cooperative frustrated Lewis pairs created by the metal species on the internal zeolite surface, resulting in a dynamic hydrogen shuttling from NH3 to regenerate framework Brønsted acid sites that eventually are converted to molecular hydrogen.

Ab Initio Thermochemistry of Highly Flexible Molecules for Thermal Decomposition Analysis.

Pyrolysis is a promising technology for chemical recycling of waste plastics, since it enables the generation of high-value chemicals with low capital and operating cost. The calculation of thermodynamic equilibrium composition using the Gibbs free energy minimization approach can determine pyrolysis operating conditions that produce desired products. However, the availability of thermochemical data can limit the application of equilibrium calculations. While density functional theory (DFT) calculations have been commonly used to produce accurate thermochemical data (e.g., enthalpies of formation) of small molecules, the accuracy and computational cost of these calculations are both challenging to handle for large, flexible molecules, exhibiting multiple conformations at elevated (i.e., pyrolysis) temperatures. In this work, we develop a computational framework to calculate accurate, temperature-dependent thermochemistry of large and flexible molecules by combining force field based conformational search, DFT calculations, thermochemical corrections, and Boltzmann statistics. Our framework produces accurately calculated thermochemistry that is used to predict equilibrium thermal decomposition profiles of octadecane, a model compound of polyethylene. Our thermochemistry results are compared against literature data demonstrating a great agreement, and the predicted decomposition profiles rationalize a series of pyrolysis experimental observations. Our work systematically addresses entropic contributions of large molecules and suggests paths for accurate and yet computationally feasible calculations of Gibbs free energies. The first-principles-based thermodynamic equilibrium analysis proposed in this work can be a significant step toward predicting temperature-dependent product distributions from plastic pyrolysis and guide experimentation on chemical plastic recycling.

Tautomerism unveils a self-inhibition mechanism of crystallization.

Modifiers are commonly used in natural, biological, and synthetic crystallization to tailor the growth of diverse materials. Here, we identify tautomers as a new class of modifiers where the dynamic interconversion between solute and its corresponding tautomer(s) produces native crystal growth inhibitors. The macroscopic and microscopic effects imposed by inhibitor-crystal interactions reveal dual mechanisms of inhibition where tautomer occlusion within crystals that leads to natural bending, tunes elastic modulus, and selectively alters the rate of crystal dissolution. Our study focuses on ammonium urate crystallization and shows that the keto-enol form of urate, which exists as a minor tautomer, is a potent inhibitor that nearly suppresses crystal growth at select solution alkalinity and supersaturation. The generalizability of this phenomenon is demonstrated for two additional tautomers with relevance to biological systems and pharmaceuticals. These findings offer potential routes in crystal engineering to strategically control the mechanical or physicochemical properties of tautomeric materials.

Understanding formation thermodynamics of structurally diverse zeolite oligomers with first principles calculations.

The mechanisms of many zeolitic processes, including nucleation and interzeolite transformation, are not fully understood owing to complex growth mixtures that obfuscate in situ monitoring of molecular events. In this work, we provide insights into zeolite chemistry by investigating the formation thermodynamics of small zeolitic species using first principles calculations. We systematically study how formation energies of pure-silicate and aluminosilicate species differ by structure type and size, temperature, and the presence of alkali or alkaline earth metal cations (Na+, K+, and Ca2+). Highly condensed (cage-like) species are found to be strongly preferred to simple rings in the pure-silicate system, and this thermodynamic preference increases with temperature. Introducing aluminum leads to more favorable formation thermodynamics for all species. Moreover, for species with a low Si/Al ratio (≤2), a thermodynamic preference does not exist among structure types; instead, a pool of diverse aluminosilicate structures compete in formation. Metal cation effects strongly depend on the presence of aluminum, cage size, cation type, and location, since each of these factors can alter electrostatic interactions between cations and zeolitic species. We reveal that confined metal cations may destabilize pure-silicate cages due to localized interactions; conversely, they stabilize aluminosilicates due to strong cation-framework attractions in sufficiently large cages. Importantly, this work rationalizes a series of experimental observations and can potentially guide efforts for controlling zeolite nucleation/crystallization processes.

Demystifying the Chemical Ordering of Multimetallic Nanoparticles.

ConspectusMultimetallic nanoparticles (NPs) have highly tunable properties due to the synergy between the different metals and the wide variety of NP structural parameters such as size, shape, composition, and chemical ordering. The major problem with studying multimetallic NPs is that as the number of different metals increases, the number of possible chemical orderings (placements of different metals) for a NP of fixed size explodes. Thus, it becomes infeasible to explore NP energetic differences with highly accurate computational methods, such as density functional theory (DFT), which has a high computational cost and is typically applied to up to a couple of hundred metal atoms. Here, we demonstrate a methodology advancing NP simulations by effectively exploring the vast materials space of multimetallic NPs and accurately identifying the ones with the most thermodynamically preferred chemical orderings. With accuracies reaching that of DFT, our methodology is applicable to practically any NP size, shape, and metal composition. We achieve this by significantly advancing the bond-centric (BC) model, a physics-based model that has been previously shown to rapidly predict bimetallic NP cohesive energies (CEs). Specifically, the BC model is trained in a way to understand how the bimetallic bond strength changes under different coordination environments present on a NP and how the metal composition of every site affects the detailed coordination environment using fractional coordination numbers. This newly modified BC model leads to an improvement from 0.331 (original model) to 0.089 eV/atom in CE predictions when compared to DFT values on a robust data set of 90 different NPs consisting of PtPd, AuPt, and AuPd NPs with varying compositions and chemical orderings. By incorporating the modified BC model into an in-house-developed genetic algorithm (GA) we can effectively and accurately predict the most stable chemical orderings of large, realistic bimetallic NPs consisting of thousands of metal atoms. This is demonstrated on AuPd bimetallic NPs, a challenging system due to the similarity in the cohesion of the two metals. By training our BC model using a unique DFT calculation on a bimetallic NP (one calculation for two metals combining together), we expand to explore the chemical ordering of multimetallic NPs. We first demonstrate the application of our methodology on a AuPdPt NP and validate our stability predictions with literature data. Then, we effectively explore the vast materials space of multimetallic NPs consisting of combinations of Au, Pt, and Pd as a function of metal composition. Our thermodynamic stability trends are presented in a ternary diagram revealing detailed, and yet, unexpected chemical ordering trends. Our computational framework can aid both experimental and computational researchers toward effectively screening multimetallic NP stability. Moreover, we provide an outlook of how this framework can be applied to catalyst discovery, high-entropy alloys, and single-atom alloys.

Dissecting Critical Factors for Electrochemical CO2 Reduction on Atomically Precise Au Nanoclusters.

This work investigates the critical factors impacting electrochemical CO2 reduction reaction (CO2 RR) using atomically precise Au nanoclusters (NCs) as electrocatalysts. First, the influence of size on CO2 RR is studied by precisely controlling NC size in the 1-2.5 nm regime. We find that the electrocatalytic CO partial current density increases for smaller NCs, but the CO Faradaic efficiency (FE) is not directly associated with the NC size. This indicates that the surface-to-volume ratio, i.e. the population of active sites, is the dominant factor for determining the catalytic activity, but the selectivity is not directly impacted by size. Second, we compare the CO2 RR performance of Au38 isomers (Au38 Q and Au38 T) to reveal that structural rearrangement of identical size NCs can lead to significant changes in both CO2 RR activity and selectivity. Au38 Q shows higher activity and selectivity towards CO than Au38 T, and density functional theory (DFT) calculations reveal that the average formation energy of the key *COOH intermediate on the proposed active sites is significantly lower on Au38 Q than Au38 T. These results demonstrate how the structural isomerism can impact stabilization of reaction intermediates as well as the overall CO2 RR performance of identical size Au NCs. Overall, this work provides important structure-property relationships for tailoring the NCs for CO2 RR.

Heteroatom Manipulation of Zeolite Crystallization: Stabilizing Zn-FAU against Interzeolite Transformation.

The preparation of metastable zeolites is often restricted to a limited range of synthesis conditions, which is exemplified in commercial syntheses lacking organics to stabilize the crystal structure. In the absence of an organic structure-directing agent, interzeolite transformation is a common phenomenon that can lead to undesirable products or impurities. Many studies have investigated the substitution of Si and Al in zeolite frameworks with alternative elements (heteroatoms) as a means of tailoring the properties of zeolites; however, relatively few studies have systematically explored the impact of heteroatoms on interzeolite transformations and their concomitant effects on zeolite crystallization. In this study, we examine methods to prepare isostructures of faujasite (FAU), which is one of the most commercially relevant zeolites and also a thermodynamically metastable structure. A survey of multivalent elements revealed that zinc is capable of stabilizing FAU at high temperatures and inhibiting its frequent transformation to zeolite gismondine (GIS). Using combined experimental and computational studies, we show that zinc alters the chemical nature of growth mixtures by sequestering silicates. Zinc heteroatoms incorporate in the FAU framework with a loading-dependent coordination. Our collective findings provide an improved understanding of driving forces for the FAU-to-GIS interzeolite transformation where we observe that heteroatoms (e.g., zinc) can stabilize zeolite FAU over a broad range of synthesis conditions. Given the growing interest in heteroatom-substituted zeolites, this approach to preparing zinc-containing FAU may prove applicable to a broader range of zeolite structures.

Size-dependent shape distributions of platinum nanoparticles.

While it is well established that nanoparticle shape can depend on equilibrium thermodynamics or growth kinetics, recent computational work has suggested the importance of thermal energy in controlling the distribution of shapes in populations of nanoparticles. Here, we used transmission electron microscopy to characterize the shapes of bare platinum nanoparticles and observed a strong dependence of shape distribution on particle size. Specifically, the smallest nanoparticles (<2.5 nm) had a truncated octahedral shape, bound by 〈111〉 and 〈100〉 facets, as predicted by lowest-energy thermodynamics. However, as particle size increased, the higher-energy 〈110〉 facets became increasingly common, leading to a large population of non-equilibrium truncated cuboctahedra. The observed trends were explained by combining atomistic simulations (both molecular dynamics and an empirical square-root bond-cutting model) with Boltzmann statistics. Overall, this study demonstrates experimentally how thermal energy leads to shape variation in populations of metal nanoparticles, and reveals the dependence of shape distributions on particle size. The prevalence of non-equilibrium facets has implications for metal nanoparticles applications from catalysis to solar energy.

The Sensitivity of Metal Oxide Electrocatalysis to Bulk Hydrogen Intercalation: Hydrogen Evolution on Tungsten Oxide.

Metal oxides are attracting increased attention as electrocatalysts owing to their affordability, tunability, and reactivity. However, these materials can undergo significant chemical changes under reaction conditions, presenting challenges for characterization and optimization. Herein, we combine experimental and computational methods to demonstrate that bulk hydrogen intercalation governs the activity of tungsten trioxide (WO3) toward the hydrogen evolution reaction (HER). In contrast to the focus on surface processes in heterogeneous catalysis, we demonstrate that bulk oxide modification is responsible for experimental HER activity. Density functional theory (DFT) calculations reveal that intercalation enables the HER by altering the acid-base character of surface sites and preventing site blocking by hydration. First-principles microkinetic modeling supports that the experimental HER rates can only be explained by intercalated HxWO3, whereas nonintercalated WO3 does not catalyze the HER. Overall, this work underscores the critical influence of hydrogen intercalation on aqueous cathodic electrocatalysis at metal oxides.

Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning.

Single atom alloys (SAAs) show great promise as catalysts for a wide variety of reactions due to their tunable properties, which can enhance the catalytic activity and selectivity. To design SAAs, it is imperative for the heterometal dopant to be stable on the surface as an active catalytic site. One main approach to probe SAA stability is to calculate surface segregation energy. Density functional theory (DFT) can be applied to investigate the surface segregation energy in SAAs. However, DFT is computationally expensive and time-consuming; hence, there is a need for accelerated frameworks to screen metal segregation for new SAA catalysts across combinations of metal hosts and dopants. To this end, we developed a model that predicts surface segregation energy using machine learning for a series of SAA periodic slabs. The model leverages elemental descriptors and features inspired by the previously developed bond-centric model. The initial model accurately captures surface segregation energy across a diverse series of FCC-based SAAs with various surface facets and metal-host pairs. Following our machine learning methodology, we expanded our analysis to develop a new model for SAAs formed from FCC hosts with FCC, BCC, and HCP dopants. Our final, five-feature model utilizes second-order polynomial kernel ridge regression. The model is able to predict segregation energies with a high degree of accuracy, which is due to its physically motivated features. We then expanded our data set to test the accuracy of the five features used. We find that the retrained model can accurately capture E seg trends across different metal hosts and facets, confirming the significance of the features used in our final model. Finally, we apply our pretrained model to a series of Ir- and Pd-based SAA cuboctahedron nanoparticles (NPs), ranging in size and FCC dopants. Remarkably, our model (trained on periodic slabs) accurately predicts the DFT segregation energies of the SAA NPs. The results provide further evidence supporting the use of our model as a general tool for the rapid prediction of SAA segregation energies. By creating a framework to predict the metal segregation from bulk surfaces to NPs, we can accelerate the SAA catalyst design while simultaneously unraveling key physicochemical properties driving thermodynamic stabilization of SAAs.

A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products.

Polycyclic aromatic hydrocarbons (PAHs) are a complex group of environmental contaminants, many having long environmental half-lives. As these compounds degrade, the changes in their structure can result in a substantial increase in mutagenicity compared to the parent compound. Over time, each individual PAH can potentially degrade into several thousand unique transformation products, creating a complex, constantly evolving set of intermediates. Microbial degradation is the primary mechanism of their transformation and ultimate removal from the environment, and this process can result in mutagenic activation similar to the metabolic activation that can occur in multicellular organisms. The diversity of the potential intermediate structures in PAH-contaminated environments renders hazard assessment difficult for both remediation professionals and regulators. A mixture of structural and energetic descriptors has proven effective in existing studies for classifying which PAH transformation products will be mutagenic. However, most existing studies of environmental PAH mutagens primarily focus on nitrogenated derivatives, which are prevalent in the atmosphere and not as relevant in soil. Additionally, PAH products commonly found in the environment can range from as large as five rings to as small as a single ring, requiring a broadly inclusive methodology to comprehensively evaluate mutagenic potential. We developed a combination of supervised and unsupervised machine learning methods to predict environmentally induced PAH mutagenicity with improved performance over currently available tools. K-means clustering with principal component analysis allows us to identify molecular clusters that we hypothesize to have similar mechanisms of action. Recursive feature elimination identifies the most influential descriptors. The cluster-specific regression outperforms available classifiers in predicting direct-acting mutagens resulting from the microbial biodegradation of PAHs and provides direction for future studies evaluating the environmental hazards resulting from PAH biodegradation.

CuZrO3: If it exists it should be a sandwich.

CuZrO3 has been hypothesized to be a catalytic material with potential applications for CO2 reduction. Unfortunately, this material has received limited attention in the literature, and to the best of our knowledge the exact crystal structure is still unknown. To address this challenge, we utilize several different structural prediction techniques in concert, including the Universal Structure Predictor: Evolutionary Xtallography (USPEX), the Materials Project Structure Predictor, and the Open Quantum Materials Database (OQMD). Leveraging these structural prediction techniques in conjunction with Density-Functional Theory (DFT) calculations, we determine a possible structure for CuZrO3, which resembles a "sandwich" morphology. Our calculations reveal that this new structure is significantly lower in energy than a previously hypothesized perovskite structure, albeit it still has a thermodynamic preference to decompose into CuO and ZrO2. In addition, we experimentally tried to synthesize CuZrO3 based on literature reports and compared computational to experimental X-ray Diffraction (XRD) patterns confirming that the final product is a mixture of CuO and ZrO2. Finally, we conducted a computational surface energetics and CO2 adsorption study on our discovered sandwich morphology, demonstrating that CO2 can adsorb and activate on the material. However, these CO2 adsorption results deviate from previously reported results further confirming that the CuZrO3 is a metastable form and may not be experimentally accessible as a well-mixed oxide, since phase segregation to CuO and ZrO2 is preferred. Taken together, our combined computational and experimental study provides evidence that the synthesis of CuZrO3 is extremely difficult and if this oxide exists, it should have a sandwich-like morphology.

Correlating structural rules with electronic properties of ligand-protected alloy nanoclusters.

Thiolate protected gold nanoclusters (TPNCs) are a unique class of nanomaterials finding applications in various fields, such as biomedicine, optics, and catalysis. The atomic precision of their structure, characterized through single crystal x-ray diffraction, enables the accurate investigation of their physicochemical properties through electronic structure calculations. Recent experimental efforts have led to the successful heterometal doping of TPNCs, potentially unlocking a large domain of bimetallic TPNCs for targeted applications. However, how TPNC size, bimetallic composition, and location of dopants influence electronic structure is unknown. To this end, we introduce novel structure-property relationships (SPRs) that predict electronic properties such as ionization potential (IP) and electron affinity (EA) of AgAu TPNCs based on physically relevant descriptors. The models are constructed by first generating a hypothetical AgAu TPNC dataset of 368 structures with sizes varying from 36 to 279 metal atoms. Using our dataset calculated with density functional theory (DFT), we employed systematic analyses to unravel size, composition, and, importantly, core-shell effects on TPNC EA and IP behavior. We develop generalized SPRs that are able to predict electronic properties across the AgAu TPNC materials space. The models leverage the same three fundamental descriptors (i.e., size, composition, and core-shell makeup) that do not require DFT calculations and rely only on simple atom counting, opening avenues for high throughput bimetallic TPNC screening for targeted applications. This work is a first step toward finely controlling TPNC electronic properties through heterometal doping using high throughput computational means.

Hydrogen Evolution Electrocatalyst Design: Turning Inert Gold into Active Catalyst by Atomically Precise Nanochemistry.

Electrocatalytic hydrogen evolution reaction (HER) holds promise in the renewable clean energy scheme. Crystalline Au and Ag are, however, poor in catalyzing HER, and the ligands on colloidal nanoparticles are generally another disadvantage. Herein, we report a thiolate (SR)-protected Au36Ag2(SR)18 nanocluster with low coverage of ligands and a core composed of three icosahedral (Ih) units for catalyzing HER efficiently. This trimeric structure, together with the monomeric Ih Au25(SR)18- and dimeric Ih Au38(SR)24, constitutes a unique series, providing an opportunity for revealing the correlation between the catalytic properties and the catalyst's structure. The Au36Ag2(SR)18 surprisingly exhibits high catalytic activity at lower overpotentials for HER due to its low ligand-to-metal ratio, low-coordinated Au atoms and unfilled superatomic orbitals. The current density of Au36Ag2(SR)18 at -0.3 V vs RHE is 3.8 and 5.1 times that of Au25(SR)18- and Au38(SR)24, respectively. Density functional theory (DFT) calculations reveal lower hydrogen binding energy and higher electron affinity of Au36Ag2(SR)18 for an energetically feasible HER pathway. Our findings provide a new strategy for constructing highly active catalysts from inert metals by pursuing atomically precise nanoclusters and controlling their geometrical and electronic structures.

Predicting ligand removal energetics in thiolate-protected nanoclusters from molecular complexes.

Thiolate-protected metal nanoclusters (TPNCs) have attracted great interest in the last few decades due to their high stability, atomically precise structure, and compelling physicochemical properties. Among their various applications, TPNCs exhibit excellent catalytic activity for numerous reactions; however, recent work revealed that these systems must undergo partial ligand removal in order to generate active sites. Despite the importance of ligand removal in both catalysis and stability of TPNCs, the role of ligands and metal type in the process is not well understood. Herein, we utilize Density Functional Theory to understand the energetic interplay between metal-sulfur and sulfur-ligand bond dissociation in metal-thiolate systems. We first probe 66 metal-thiolate molecular complexes across combinations of M = Ag, Au, and Cu with twenty-two different ligands (R). Our results reveal that the energetics to break the metal-sulfur and sulfur-ligand bonds are strongly correlated and can be connected across all complexes through metal atomic ionization potentials. We then extend our work to the experimentally relevant [M25(SR)18]- TPNC, revealing the same correlations at the nanocluster level. Importantly, we unify our work by introducing a simple methodology to predict TPNC ligand removal energetics solely from calculations performed on metal-ligand molecular complexes. Finally, a computational mechanistic study was performed to investigate the hydrogenation pathways for SCH3-based complexes. The energy barriers for these systems revealed, in addition to thermodynamics, that kinetics favor the break of S-R over the M-S bond in the case of the Au complex. Our computational results rationalize several experimental observations pertinent to ligand effects on TPNCs. Overall, our introduced model provides an accelerated path to predict TPNC ligand removal energies, thus aiding towards targeted design of TPNC catalysts.