Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Dynamics of Elementary Steps on Metal Surfaces at High Coverages: The Prevalence and Kinetic Competence of Contiguous Bare-Atom Ensembles

The rate of elementary steps on densely-covered surfaces depends sensitively on repulsive interactions within dense adlayers, situations ubiquitous in practice and with kinetic consequences seldom captured by Langmuirian treatments of surface catalysis. This study develops an ensemble-based method that assesses how such repulsion influences the prevalence and kinetic competence of bare-atom ensembles of different size. Chemisorbed CO (CO*) is used as an example because it forms dense adlayers on metal nanoparticles during CO2 hydrogenation (CO2-H2) and other reactions, leading to significant repulsion that weakens the binding of CO* and kinetically-relevant transition states (TS). This approach is enabled by density functional theory and probability formalisms and describes the prevalence of ensembles of contiguous bare atoms from their formation energy (via CO* desorption); it then determines their competence in stabilizing the TS and mediating the reaction rates. The specific conclusions reflect the extent to which a given TS and CO* desorbed to form bare ensembles "sense" repulsion and the contribution of each ensemble size to each reaction channel mediated by distinct TS structures. These formalisms are illustrated by assessing the relative contributions, kinetic relevance, and ensemble size requirements for two CO2-H2 routes (direct and H-assisted CO2 activation to CO and H2O) on Ru nanoparticles, but they are not restricted to specific bound species or reaction channels. This method is essential to assess the kinetic relevance of elementary steps in a given catalytic sequence and to determine the contributions from parallel reaction channels at the crowded surfaces that prevail in the practice of surface catalysis.

Capturing the Coverage Dependence of Aromatics' Adsorption through Mean-Field Models

To capture the dominant interactions (surface-mediated and through-space) in catalytic hydrodeoxygenation systems, coverage-dependent mean-field models of aromatic adsorption are developed on Pt(111) and Ru(0001). We derive three key insights from this work: (1) we can universally apply mean-field models to capture the coverage-dependent behavior of oxygenated aromatics on transition-metal surfaces, (2) we can deconvolute surface-mediated and through-space interactions from the mean-field model, and (3) we can develop relatively accurate models that predict the adsorption energy of aromatics on transition-metal surfaces for the full coverage range using the work function at the lowest modeled coverage. Our approach enables the rapid prediction of the coverage-dependent behavior of oxygenated aromatics on transition-metal surfaces, reducing the computational cost associated with these studies by an order of magnitude.

Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.