Themed collection Bridging the Gap from Surface Science to Heterogeneous Catalysis Faraday Discussion

A model Rh/Fe₃O₄(001) single-atom catalyst enables site-resolved assignment of CO stretching frequencies.

In this comparative study of size-selected Pt clusters on single crystalline oxide supports, we consider the effects of support doping and oxidation state, gas composition and pressure, temperature and cluster size using NAP-XPS and STM.

Aging Pt catalysts in air leads to anomalously large particles due to the high vapor pressure of PtO₂. A physical mixture of the Pt catalyst with Pd, Rh or ceria allows the mobile atoms to be trapped, creating self-healing behavior.

Changes in the chemical composition of size-selected Pd nanoparticles were studied by near-ambient pressure XPS under dry and wet reaction conditions for methane oxidation (CH₄ + O₂ [+H₂O]) in the temperature range between 150 °C and 450 °C.

Operando electron microscope reveals how Pt nanoparticles self-entangle thermally to form catalytic hot-spots in the CO oxidation reaction.

The initial hydrogenation of CO₂ to formate is investigated on Pd-based alloy catalysts using density functional theory together with subgroup discovery analysis.

We introduce an interpretable artificial intelligence approach that identifies key physicochemical parameters correlated with the measured catalytic performance.

We show the need for quantum-accurate dynamics across scales within catalysis and explore machine learning potentials for reaction–diffusion modelling.

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.

We introduce a 2-step microkinetic model involving a single reaction intermediate and show that its bias-dependent Arrhenius signatures closely match our measurements of the oxygen reduction reaction on platinum and ruthenium nanoparticles.

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.

The evolution of the reactivity and structure of a series of Rh/rTiO₂catalysts under RWGS conditions at high conversions (400–600 °C, 1 atm, 75% H₂, 25% CO₂) as a function of time on stream (16 h) is presented.

The use of error-cancellation reactions enables the derivation of enthalpies of formation of adsorbates on various catalytic surfaces from DFT data to within experimental accuracy, regardless of the chosen GGA exchange–correlation functional.

H₂O enables the accumulation of hydronium–electron pairs after H₂ adsorption on Pt without affecting initial adsorption enthalpies.

Adsorption of a single oxygen atom on Pt(111) displaces the Pt surface atoms within 7 Å of the oxygen atom. These displacements result in long-range repulsion between adsorbed oxygen atoms and lead to a reorganization of the adsorbed layer at elevated coverages.

Catalytic activity and selectivity of 2-propanol dissociation is investigated over pristine and vanadium-oxide loaded Co3O4(111), forming acetone at low V loadings while increasing V coverage results in C–O bond scission and the formation of propene.

Binding energies of CO and H are determined using temperature programmed desorption of CO and H₂ from Pd(111), PdH/Pd(111), and Cu/PdH/Pd(111) under ultra-high vacuum conditions and are correlated with electrocatalytic selectivity.

We combine GCGA global optimization with GCDFT calculations to find optimal accessible CO coverages under CO2RR conditions, and investigate how surface roughness, facets, and CO adsorption sites influence the Raman spectra, and the intensity ratio of interest.

We present a computational workflow, the Conformal Sampling of Catalytic Processes approach, and its application to the case of heterogeneous hydrogenation/reduction of carbon dioxide on copper and nickel catalysts.

We present a highly interpretable and efficient machine learning framework for predictive and generative modeling of adsorption energies on surfaces using subgraph isomorphic decision trees (SIDTs).

Symbolic regression guided adaptive feature selection improves the interpretability and performance of Bayesian optimization for catalyst discovery.

MACE-APE reveals that the timescale of restructuring of Pd₃–Pd₁₁/MgO clusters overlaps with catalytic timescales.

The hydrogen evolution reaction (HER) is modeled through both Volmer–Heyrovsky and Volmer–Tafel mechanisms to determine under which conditions compensation effects emerge.

Accurate prediction of single-atom catalyst activity in the hydrogen evolution reaction via DFT demands methods that go beyond the traditional computational hydrogen electrode framework.