Correlating Ethylene Glycol Reforming Activity with In Situ EXAFS Detection of Ni Segregation in Supported NiPt Bimetallic Catalysts

Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, and experimental data for solvation free energies of phenol adsorption are reanalyzed using multiple adsorption isotherm models. Explicit solvation calculations predict an endergonic solvation free energy for phenol adsorption that agrees well with measurements to within the experimental and force field uncertainties. Computed adsorption free energies of solvation of carbon monoxide, ethylene glycol, benzene, and phenol over the (111) facet of Pt and Cu suggest that liquid water destabilizes all adsorbed species, with the largest impact on the largest adsorbates.

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

The synergistic effect of Hf-O-Ru bonds and oxygen vacancies in Ru/HfO2 for enhanced hydrogen evolution

Ru nanoparticles have been demonstrated to be highly active electrocatalysts for the hydrogen evolution reaction (HER). At present, most of Ru nanoparticles-based HER electrocatalysts with high activity are supported by heteroatom-doped carbon substrates. Few metal oxides with large band gap (more than 5 eV) as the substrates of Ru nanoparticles are employed for the HER. By using large band gap metal oxides substrates, we can distinguish the contribution of Ru nanoparticles from the substrates. Here, a highly efficient Ru/HfO₂ composite is developed by tuning numbers of Ru-O-Hf bonds and oxygen vacancies, resulting in a 20-fold enhancement in mass activity over commercial Pt/C in an alkaline medium. Density functional theory (DFT) calculations reveal that strong metal-support interaction via Ru-O-Hf bonds and the oxygen vacancies in the supported Ru samples synergistically lower the energy barrier for water dissociation to improve catalytic activities.

Although ruthenium nanomaterials have proven to be effective catalysts for H₂ evolution, there is still room for activity improvements. Here, authors develop an efficient Ru/HfO₂ electrocatalyst with tuned Ru-O-Hf bonds and oxygen vacancies that shows high activities for alkaline H₂ evolution.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd-C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd₅₅ nanocluster and extended it to new geometries, Pd₇₉, and Pd₈₅, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Recent Progress and Prospects in Catalytic Water Treatment

Presently, conventional technologies in water treatment are not efficient enough to completely mineralize refractory water contaminants. In this context, the implementation of catalytic processes could be an alternative. Despite the advantages provided in terms of kinetics of transformation, selectivity, and energy saving, numerous attempts have not yet led to implementation at an industrial scale. This review examines investigations at different scales for which controversies and limitations must be solved to bridge the gap between fundamentals and practical developments. Particular attention has been paid to the development of solar-driven catalytic technologies and some other emerging processes, such as microwave assisted catalysis, plasma-catalytic processes, or biocatalytic remediation, taking into account their specific advantages and the drawbacks. Challenges for which a better understanding related to the complexity of the systems and the coexistence of various solid-liquid-gas interfaces have been identified.

In Situ/Operando Electrocatalyst Characterization by X-ray Absorption Spectroscopy

During the last decades, X-ray absorption spectroscopy (XAS) has become an indispensable method for probing the structure and composition of heterogeneous catalysts, revealing the nature of the active sites and establishing links between structural motifs in a catalyst, local electronic structure, and catalytic properties. Here we discuss the fundamental principles of the XAS method and describe the progress in the instrumentation and data analysis approaches undertaken for deciphering X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectra. Recent usages of XAS in the field of heterogeneous catalysis, with emphasis on examples concerning electrocatalysis, will be presented. The latter is a rapidly developing field with immense industrial applications but also unique challenges in terms of the experimental characterization restrictions and advanced modeling approaches required. This review will highlight the new insight that can be gained with XAS on complex real-world electrocatalysts including their working mechanisms and the dynamic processes taking place in the course of a chemical reaction. More specifically, we will discuss applications of in situ and operando XAS to probe the catalyst's interactions with the environment (support, electrolyte, ligands, adsorbates, reaction products, and intermediates) and its structural, chemical, and electronic transformations as it adapts to the reaction conditions.

Dependency of solvation effects on metal identity in surface reactions

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O-H and C-H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Controllable Synthesis of Surface Pt-Rich Bimetallic AuPt Nanocatalysts for Selective Hydrogenation Reactions

Bimetallic nanocatalysts, with efficient and controllable catalytic performance, have a promising application in chemical production. In this study, surface Pt-rich bimetallic AuPt nanoparticles with different Pt/Au ratios were prepared and tested in selective hydrogenation reactions of substituted nitroaromatics. Au nanoparticles were first prepared with n-butyllithium as a rapid reducer, which were further used as seeds in the slow growth process of Pt atoms. Because of the employed sequential reduction method and the following atom diffusion, surface Pt-rich bimetallic AuPt nanoparticles were obtained. Compared with the uniform AuPt alloy nanocatalysts synthesized by the co-reduction method with n-butyllithium as the reducer and monometallic Pt nanocatalysts, the obtained surface Pt-rich AuPt bimetallic nanocatalysts presented an enhanced catalytic selectivity or activity. The performance enhancement is assigned to the optimized Au/Pt interaction in the surface Pt-rich bimetallic nanostructures. This work demonstrates that the optimization of the stoichiometry and construction of bimetallic materials is a feasible method to synthesize controllable and efficient nanocatalysts.

Solving the Structure and Dynamics of Metal Nanoparticles by Combining X-Ray Absorption Fine Structure Spectroscopy and Atomistic Structure Simulations

Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premiere method for analysis of the structure and structural transformation of nanoparticles. Extraction of analytical information about the three-dimensional structure and dynamics of metal-metal bonds from EXAFS spectra requires special care due to their markedly non-bulk-like character. In recent decades, significant progress has been made in the first-principles modeling of structure and properties of nanoparticles. In this review, we summarize new approaches for EXAFS data analysis that incorporate particle structure modeling into the process of structural refinement.

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning

Properties of mono- and bimetallic metal nanoparticles (NPs) may depend strongly on their compositional, structural (or geometrical) attributes, and their atomic dynamics, all of which can be efficiently described by a partial radial distribution function (PRDF) of metal atoms. For NPs that are several nanometers in size, finite size effects may play a role in determining crystalline order, interatomic distances, and particle shape. Bimetallic NPs may also have different compositional distributions than bulk materials. These factors all render the determination of PRDFs challenging. Here extended X-ray absorption fine structure (EXAFS) spectroscopy, molecular dynamics simulations, and supervised machine learning (artificial neural-network) method are combined to extract PRDFs directly from experimental data. By applying this method to several systems of Pt and PdAu NPs, we demonstrate the finite size effects on the nearest neighbor distributions, bond dynamics, and alloying motifs in mono- and bimetallic particles and establish the generality of this approach.

Spectroscopic characterization of a highly selective NiCu3/C hydrodeoxygenation catalyst

Formation of a NiO layer atop a NiO–Cu₂O bulk under reaction conditions is established via operando XAS.

Catalyst characterization in the presence of solvent: development of liquid phase structure-activity relationships

Due to the low volatility and highly oxygenated nature of biomass derived feedstocks, biomass upgrade reactions are frequently conducted in the presence of solvent to improve substrate mass transfer to the catalyst surface. However, relevant catalyst characterization techniques are most often performed in vacuum or inert gas environments, where the effect of solvent on the catalytic sites is ignored. Comparatively, characterization techniques in the presence of solvent are relatively rare, which poses challenges in developing structure-activity relationships for liquid phase reactions. In this perspective, commonly utilized techniques for probing the solid-liquid interface are briefly covered, with a focus on the role of solvent on zeolite and solid acid catalysis. New applications of techniques are proposed, most notably with ATR-FTIR, in the context of extracting thermodynamic information for the further understanding of the role of solvent on broadly applicable catalyst properties, such as acidity, and to develop structure-activity relationships for solid catalysts in solvent.

Catalytic Reforming of Oxygenates: State of the Art and Future Prospects

This Review describes recent advances in the design, synthesis, reactivity, selectivity, structural, and electronic properties of the catalysts for reforming of a variety of oxygenates (e.g., from simple monoalcohols to higher polyols, then to sugars, phenols, and finally complicated mixtures like bio-oil). A comprehensive exploration of the structure-activity relationship in catalytic reforming of oxygenates is carried out, assisted by state-of-the-art characterization techniques and computational tools. Critical emphasis has been given on the mechanisms of these heterogeneous-catalyzed reactions and especially on the nature of the active catalytic sites and reaction pathways. Similarities and differences (reaction mechanisms, design and synthesis of catalysts, as well as catalytic systems) in the reforming process of these oxygenates will also be discussed. A critical overview is then provided regarding the challenges and opportunities for research in this area with a focus on the roles that systems of heterogeneous catalysis, reaction engineering, and materials science can play in the near future. This Review aims to present insights into the intrinsic mechanism involved in catalytic reforming and provides guidance to the development of novel catalysts and processes for the efficient utilization of oxygenates for energy and environmental purposes.

Patched bimetallic surfaces are active catalysts for ammonia decomposition

Ammonia decomposition is often used as an archetypical reaction for predicting new catalytic materials and understanding the very reason of why some reactions are sensitive on material's structure. Core-shell or surface-segregated bimetallic nanoparticles expose outstanding activity for many heterogeneously catalysed reactions but the reasons remain elusive owing to the difficulties in experimentally characterizing active sites. Here by performing multiscale simulations in ammonia decomposition on various nickel loadings on platinum (111), we show that the very high activity of core-shell structures requires patches of the guest metal to create and sustain dual active sites: nickel terraces catalyse N-H bond breaking and nickel edge sites drive atomic nitrogen association. The structure sensitivity on these active catalysts depends profoundly on reaction conditions due to kinetically competing relevant elementary reaction steps. We expose a remarkable difference in active sites between transient and steady-state studies and provide insights into optimal material design.

Surface Segregation in Bimetallic Nanoparticles: A Critical Issue in Electrocatalyst Engineering

Bimetallic nanoparticles are a class of important electrocatalyst. They exhibit a synergistic effect that critically depends on the surface composition, which determines the surface properties and the adsorption/desorption behavior of the reactants and intermediates during catalysis. The surface composition can be varied, as nanoparticles are exposed to certain environments through surface segregation. Thermodynamically, this is caused by a difference in surface energy between the two metals. It may lead to the enrichment of one metal on the surface and the other in the core. The external conditions that influence the surface energy may lead to the variation of the thermodynamic steady state of the particle surface and, thus, offer a chance to vary the surface composition. In this review, the most recent and important progress in surface segregation of bimetallic nanoparticles and its impact in electrocatalysis are introduced. Typical segregation inducements and surface characterization techniques are discussed in detail. It is concluded that surface segregation is a critical issue when designing bimetallic catalysts. It is necessary to explore methods to control it and utilize it as a way towards producing robust, bimetallic electrocatalysts.

X-ray absorption spectroscopy on magnetic nanoscale systems for modern applications

X-ray absorption spectroscopy facilitated by state-of-the-art synchrotron radiation technology is presented as a powerful tool to study nanoscale systems, in particular revealing their static element-specific magnetic and electronic properties on a microscopic level. A survey is given on the properties of nanoparticles, nanocomposites and thin films covering a broad range of possible applications. It ranges from the ageing effects of iron oxide nanoparticles in dispersion for biomedical applications to the characterisation on a microscopic level of nanoscale systems for data storage devices. In this respect, new concepts for electrically addressable magnetic data storage devices are highlighted by characterising the coupling in a BaTiO(3)/CoFe(2)O(4) nanocomposite as prototypical model system. But classical magnetically addressable devices are also discussed on the basis of tailoring the magnetic properties of self-assembled ensembles of FePt nanoparticles for data storage and the high-moment material Fe/Cr/Gd for write heads. For the latter cases, the importance is emphasised of combining experimental approaches in x-ray absorption spectroscopy with density functional theory to gain a more fundamental understanding.

Sailing into uncharted waters: recent advances in the in situ monitoring of catalytic processes in aqueous environments

This review presents recent advances inin situstudies of catalytic processes in the aqueous environment with an outlook of mesoscale imaging.

Synergistic Effects of Alloying and Thiolate Modification in Furfural Hydrogenation over Cu-Based Catalysts

Control of bimetallic surface composition and surface modification with self-assembled monolayers (SAMs) represent two methods for modifying catalyst activity and selectivity. However, possible synergistic effects of employing these strategies in concert have not been previously explored. We investigated the effects of modifying Cu/Al2O3 catalysts by alloying with Ni and modifying with octadecanethiol (C18) SAMs, using furfural hydrogenation as a probe reaction. Incorporation of small amounts of Ni (Cu4Ni) improved catalytic activity while slightly reducing hydrogenation selectivity. Further incorporation of Ni resulted in high rates for decarbonylation and ring-opening. Modification of the Cu4Ni catalyst with C18-SAMs resulted in improvement in both the activity and hydrogenation selectivity. X-ray photoelectron spectroscopy experiments on bimetallic thin films and density functional theory calculations revealed that the C18-SAM kinetically stabilized Cu at the surface under hydrogenation conditions. These results indicate that thiolate monolayers can be used to control surface bimetallic composition to improve catalytic performance.

Aqueous solution synthesis of Pt-M (M = Fe, Co, Ni) bimetallic nanoparticles and their catalysis for the hydrolytic dehydrogenation of ammonia borane

Platinum-based bimetallic nanocatalysts have attracted much attention due to their high-efficiency catalytic performance in energy-related applications such as fuel cell and hydrogen storage, for example, the hydrolytic dehydrogenation of ammonia borane (AB). In this work, a simple and green method has been demonstrated to successfully prepare Pt-M (M = Fe, Co, Ni) NPs with tunable composition (nominal Pt/M atomic ratios of 4:1, 1:1, and 1:4) in aqueous solution under mild conditions. All Pt-M NPs with a small size of 3-5 nm show a Pt fcc structure, suggesting the bimetallic formation (alloy and/or partial core-shell), examined by transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray absorption fine structure (XAFS) analysis. The catalytic activities of Pt-M NPs in the hydrolytic dehydrogenation of AB reveal that Pt-Ni NPs with a ratio of 4:1 show the best catalytic activity and even better than that of pure Pt NPs when normalized to Pt molar amount. The Ni oxidation state in Pt-Ni NPs has been suggested to be responsible for the corresponding catalytic activity for hydrolytic dehydrogenation of AB by XAFS study. This strategy for the synthesis of Pt-M NPs is simple and environmentally benign in aqueous solution with the potential for scale-up preparation and the in situ catalytic reaction.

On factors controlling activity of submonolayer bimetallic catalysts: nitrogen desorption

We model N2 desorption on submonolayer bimetallic surfaces consisting of Co clusters on Pt(111) via first-principles density functional theory-based kinetic Monte Carlo simulations. We find that submonolayer structures are essential to rationalize the high activity of these bimetallics in ammonia decomposition. We show that the N2 desorption temperature on Co∕Pt(111) is about 100 K higher than that on Ni∕Pt(111), despite Co∕Pt(111) binding N weaker at low N coverages. Co∕Pt(111) has substantially different lateral interactions than single metals and Ni∕Pt. The lateral interactions are rationalized with the d-band center theory. The activity of bimetallic catalysts is the result of heterogeneity of binding energies and reaction barriers among sites, and the most active site can differ on various bimetallics. Our results are in excellent agreement with experimental data and demonstrate for the first time that the zero-coverage descriptor, used until now, for catalyst activity is inadequate due not only to lacking lateral interactions but importantly to presence of multiple sites and a complex interplay of thermodynamics (binding energies, occupation) and kinetics (association barriers) on those sites.

Bimetallic Pt–Ni composites on ceria-doped alumina supports as catalysts in the aqueous-phase reforming of glycerol

Adding Ni to a supported Pt catalyst for aqueous phase reforming allowed the Pt content to be reduced to one third, with a slight increase in H₂ yield.