Faraday Discussions最新文献

The activity and stability of an electrocatalyst is governed by the structural and chemical changes occurring at the solid-liquid interface. While single crystal electrodes have often served as model systems to elucidate structure-function relationships, extending these insights to more realistic polycrystalline electrodes requires experimental techniques with spatial resolution that can operate under electrochemical conditions. Herein, the electro-oxidation of a polycrystalline gold electrode is investigated in situ using two-dimensional surface optical reflectance, electrochemical near ambient pressure X-ray photoelectron spectroscopy, and tomographic surface X-ray diffraction. This combination of techniques provides surface-sensitive chemical and structural information. In the case of the polycrystalline gold, a surface orientation-dependent oxidation behaviour is found where both the onset and growth of oxides/hydroxides are affected. Our approach provides an enhanced understanding of the dynamic behaviour of complex electrodes under harsh environments, enabling grain-resolved insight into electrochemical processes.

Structural order and surface crystallinity of palladium nanoparticles (Pd NPs) significantly affect their chemical and mechanical stability, and impact their resistance to poisoning and efficiency in hydrogen (H₂) (de)sorption. This study investigates the impact of sulfuric acid (H₂SO₄) poisoning on pristine and annealed Pd NPs to identify the influence of structure on the density and stability of poisoners. Electron microscopy and diffraction analyses show that annealing transforms initially amorphous, rough, and aggregated particles into spherical and crystalline NPs with better-defined grain boundaries. Nanoscale AFM-IR analysis shows that pristine NPs rapidly decompose SO_x upon H₂ exposure, and SO_x is mainly located on the rim of the NP. Annealed NPs maintain stable, localized SO_x signatures across multiple N₂-H₂ cycles, and only after the second H₂ exposure cycle the SO_x is removed from the central part of the NP. Contact potential difference analysis shows the strong affinity of SO_x to the rim of the NP. Together, these results reveal that crystallinity and defect density dictate both the spatial distribution and chemical stability of sulfur species on Pd surfaces. Pristine, defect-rich NPs promote rapid H₂-induced SO_x reduction and selective desorption from their centers, whereas annealed, crystalline NPs stabilize SO_x more strongly and exhibit delayed desorption. This direct link between structural order, SO_x binding strength, and hydrogen (de)sorption affinity underscores the critical role of nanoscale morphology in controlling poisoning dynamics and highlights nanospectroscopy as a powerful tool for correlating local structure with chemical functionality.

The role of dynamic restructuring of catalytic interfaces under reaction conditions is of unquestionable importance. Sub-nanocluster catalysts, consisting of just a few atoms on a metal oxide support, are known to be highly fluxional. They are able to populate and interconvert between multiple isomers with similar energies. Computational cost has hitherto restricted first-principles treatment of sub-nanocluster fluxionality to the assumption of thermodynamic equilibrium, where the relative isomer populations are Boltzmann distributed. Here, we exploit the advent of fast and accurate machine-learned interatomic potentials (MLIPs), coupled with an automatic process explorer (APE) approach, to systematically evaluate the kinetics of fluxional behavior of sub-nanoclusters. Specifically, we explore the size-dependent restructuring kinetics of Pd₃-Pd₁₁ clusters on a fixed MgO(100) support. Propagation of the constructed comprehensive isomerization networks shows that the timescale for relaxation to thermodynamic equilibrium can vary by several orders of magnitude upon changing the number of Pd atoms in the cluster by only one. There is also no simple correlation between the overall relaxation timescale and the amount of time spent in individual (metastable) isomers. Comparison of the timescales of cluster restructuring to a typical rate-limiting catalytic barrier of 1 eV shows that typical fluxional restructuring occurs on comparable to (much) faster timescales, depending on cluster size and specific isomer geometry. Thus, it becomes clear that explicitly resolving the state-to-state dynamics of fluxional sub-nanoclusters is essential for the development of mechanistic understanding of their catalytic performance.

Adsorption of simple gas phase molecules (e.g., H₂) on metal nanoparticles in the aqueous phase link thermo- and electrocatalysis communities through common elementary steps. Yet, a key facet of this linkage remains incomplete: the effects of water solvation on coverage-dependent adsorption thermodynamics, the migration and speciation of adsorbates across surfaces, and related electrical polarization, eludes current understanding but represents a necessary benchmark to relate computational and experimental investigations of such systems. Here, we describe an experimental approach to quantify adsorption thermodynamics of hydrogen, a species relevant for both thermo- and electrocatalysis in the condensed phase, utilizing volumetric adsorption uptakes, microcalorimetric assessments of adsorption enthalpies, and in situ measurements of catalyst open circuit potentials (E_cat) for water-wetted Pt nanoparticles dispersed on carbon supports as a model system. Precise control of H₂O thermodynamic activity and hydrogen fractional coverages reveals nearly constant molar enthalpies of adsorption (ΔH_ads = -32 vs. -27 kJ per mol-H) coupled with greater entropy losses (ΔS_ads = -100 vs. -62 J per mol-H per K) upon introducing H₂O. Additionally, hydrogen uptakes increase drastically in the presence of coadsorbed water and exceeds 20 mol-H per mol-Pt_surf, which indicates chemical species migrate from Pt nanoparticles to the carbon support. Analysis of adsorption free energies and E_cat measurements indicate that these migrated species remain bound as hydronium-electron pairs dispersed across the carbon support following the equilibrium of Tafel (H₂ + 2* ⇌ 2H*) and Volmer (H* + H₂O ⇌ H₃O⁺ + e^- + *) elementary steps commonly invoked in hydrogen evolution electrocatalysis alongside an electrostatic capacitive interaction step (H₃O⁺ + C^- ⇌ H₃O⁺⋯C^-). Dissociative adsorption of H₂ proceeds more rapidly in the presence of co-adsorbed water as a consequence of hydronium shuttling enabled by the Volmer step. This case study illustrates a generalizable methodology to directly measure thermodynamic quantities for molecular and dissociative adsorption at solid-liquid interfaces at controlled thermodynamic activities of all species. We anticipate this form of measurement will serve as a foundation for connections between theory and experiment in pursuit of increasingly complex descriptions of chemical reactions in these environments.

Spin-mediated promotion is a newly discovered electronic promotion mechanism for magnetic metal-catalyst surfaces. A promoter like Ba locally quenches the spin density of the surface atoms at the active site, which stabilizes nearby adsorbates. As this effect is geometrically constrained to atoms neighboring the promoter, and since adsorbates are stabilized to different degrees by this effect, this can break scaling relations, and allows for the design of more efficient catalysts. In this review, we give a detailed explanation of the spin-mediated promotion mechanism. We then illustrate the effect for three different reactions: ammonia synthesis, ammonia decomposition, and CO methanation. We conclude by discussing how spin-mediated promotion can be utilized for other reactions and classes of catalysts, and how it can guide new catalyst development.

This paper demonstrates the need for a consistent dynamics view of all processes from the nano- to mesoscale with quantum accuracy to design industrial catalysts. This stems from the observation that when a feed of molecules passes through a crystalline nanoporous framework, the following events occur: molecules enter the crystal, diffuse to active sites where they adsorb, desorb, or react, and finally products exit. These events span vastly different time and length scales-from picoseconds to hours and nanometers to micrometers. Length and time phenomena are entangled-the dynamics is altered by modifications at the length scale-leading to spatiotemporal phenomena. Current modeling practices treat reactions and diffusion inconsistently, often combining quantum methods for reactions with classical force fields for diffusion, and frequently neglect true dynamics under operating conditions. Industrial catalysts are highly sensitive to process conditions; some active sites form only at elevated temperatures, and reactive intermediates can change nature under these conditions. First-principles molecular dynamics captures these effects but becomes prohibitively expensive when relying on quantum methods like Density Functional Theory for force evaluations. In this paper, we demonstrate-through the study of key intermediates in CO₂-to-hydrocarbon conversion within small-pore zeolites-that diffusion must also be treated at the quantum level, as molecules can interact with active sites and even react while diffusing. Diffusion may become strongly hindered and compete with reactive events. In later case studies, we present proof-of-concept results for deriving diffusion coefficients in small-pore zeolites using machine learning potentials (MLPs) trained on quantum data. For benzene methylation in large-pore zeolites, we develop reactive MLPs to construct multidimensional free-energy surfaces, revealing competing pathways. Overall, the various cases underscore the need for a consistent dynamic description with quantum accuracy across scales. MLPs offer a promising route to obtain kinetic and transport properties from the nano- to mesoscale at reduced cost, however their integration in overall multiscale models and reaction-diffusion frameworks will be necessary to reach industrially relevant scales.

The importance of cluster-size effects in heterogeneous catalysis is now well recognized. X-ray photoelectron spectroscopy (XPS) is an obvious technique to study size-dependent changes in the chemical composition and electronic structure of catalyst nanoparticles. However, as XPS is an averaging technique based on the detection of electrons, experiments require a narrow distribution of cluster size and a conducting homogeneous support in order to avoid sample charging, which would prevent accurate measurements of chemical shifts. Traditional methods of catalyst synthesis by impregnation/calcination of support powders lead to very large particle size distributions (typically ±50%) and insulating samples. They therefore fail both of the above criteria and make it extremely difficult to extract precise sample characterisation. Here we present an alternative approach designed to enable XPS analysis in vacuum and under reaction conditions, whereby: (i) nanoparticles are synthesized by gas condensation and passed through a mass filter, which allows size selection in the range of 1 to 10 000 atoms with typically ±4% accuracy; (ii) these particles are deposited onto a thin Al₂O₃ film grown on Al foil, which mimics the properties of conventional alumina supports while being conductive enough to avoid any charging-related artefacts in the XPS spectra. In vacuum, size-dependent Pd 3d binding-energy shifts up to 1.65 eV were recorded for supported Pd nanoparticles. Changes in the chemical composition of Pd nanoparticles were studied by near-ambient pressure (NAP)-XPS under dry and wet reaction conditions for methane oxidation (CH₄ + O₂ [+H₂O]) in the temperature range between 150 °C and 450 °C. Under dry reaction conditions large Pd particles appeared to oxidise almost fully to Pd(II), whereas smaller clusters showed a mix of Pd(0) and Pd(II) oxidation states. Under wet conditions, oxidation starts at lower temperatures and particles of all sizes were fully oxidised when the highest temperature was reached. Sintering during the temperature ramp cannot be excluded, especially for the smaller particles, and may be part of the reason for the different behaviour under wet conditions. This study clearly shows composition changes which are particle-size dependent and demonstrates the possibilities of fine-tuning catalytic activity if better size-control can be achieved in catalyst synthesis.

Electrocatalytic reactions often involve several steps and intermediates, along with changes in the catalyst structure and chemistry under reaction conditions, making their mechanistic understanding very challenging. As a way to extract maximum information about the kinetics, temperature-dependent electrochemistry enables access to the apparent activation energy and pre-exponential factor as a function of the electrochemical bias. Recently, many reactions were shown to exhibit rich structures in their bias-dependent activation parameters, structures which cannot be accounted for by the traditional, single-step Butler-Volmer theory. Here, we study the overpotential-dependent activation parameters of a 2-step microkinetic model featuring an electrochemical adsorption followed by a chemical recombination step. We show that the electrochemical bias drives transitions across several kinetic regimes where the degree of rate control of each step varies. A key finding is that the bias dependent Arrhenius signatures constrain the underlying phase space of intermediate binding and activation enthalpies, even for such a seemingly simple model. From the close fit of our model with our experiments on the oxygen reduction reaction, we find that for a wide overpotential range, one kinetically relevant intermediate - and thus two partially rate determining steps - are controlling the kinetics on platinum and ruthenium nanoparticles. From the fits, we extract the corresponding binding and activation energies along with bias-dependent coverage. We argue that combining temperature dependent electrochemistry with minimalistic micro-kinetic models allows a direct comparison with DFT calculations and operando spectroscopy measurements.

The rational design of catalysts from first principles remains a central but still elusive goal of modern quantum chemistry. Although advances in computing power and electronic-structure methods have made predictive modelling increasingly feasible, accurately forecasting catalytic activity from theory alone remains challenging. Single-atom catalysts (SACs), with their more defined active sites, offer in principle simplified models compared with conventional heterogeneous systems, yet in many cases discrepancies between theoretical predictions and experimental results persist. Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current computational approaches, particularly those based on the computational hydrogen electrode (CHE), and the problems arising when theoretical predictions are compared with experimental evidence. Factors contributing to these discrepancies include the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, and the neglect of reaction intermediates that form on SACs and not on extended metal surfaces. Additional challenges arise from solvent effects, catalyst evolution under operating conditions, and the potential instability of theoretically designed materials under realistic electrochemical environments. Furthermore, intrinsic approximations in density functional theory introduce uncertainties that hinder quantitative accuracy. Overall, the CHE model, while valuable for identifying general trends, does not include many critical terms that contribute to the activity of untested catalysts. Progress toward the true rational design of catalysts will require integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-driven methods, to develop more robust descriptors and predictive frameworks.

Catalytic reactions over nanoparticle ensembles are often interpreted by intrinsic nanoparticle descriptors. However, the collective behavior that emerges as nanoparticles are densely packed remains rarely addressed. Here, we use operando transmission electron microscopy and spectroscopy to examine the CO oxidation over Pt nanoparticles in a unidirectional gas-flow reactor. The experiments show that regions with high nanoparticle loading develop catalytic hot-spots, sustaining full CO conversion independent of the position in the reactor, whereas low-loaded regions display gradual conversion downstream. The hot-spots correlate with distinct shape-activity relationships and indicate that thermally mediated entanglement among densely packed nanoparticles governs self-sustained activity. Identifying such entanglement of nanoparticles opens up new pathways for engineering nanoparticle ensembles with spatially enhanced catalytic functionalities.