Adsorption energy scaling relation on bimetallic magnetic surfaces: role of surface magnetic moments

Unravelling CO2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations

Establishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO2 reduction reaction (CO2 RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model-Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H2 CO (MAE: 0.24 eV) and *H3 CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state-of-the-art ML in catalysis towards better interpretability.

Defect-Driven Efficient Selective CO2 Hydrogenation with Mo-Based Clusters

Synthetic fuels produced from CO2 show promise in combating climate change. The reverse water gas shift (RWGS) reaction is the key to opening the CO2 molecule, and CO serves as a versatile intermediate for creating various hydrocarbons. Mo-based catalysts are of great interest for RWGS reactions featured for their stability and strong metal-oxygen interactions. Our study identified Mo defects as the intrinsic origin of the high activity of cluster Mo2C for CO2-selective hydrogenation. Specifically, we found that defected Mo2C clusters supported on nitrogen-doped graphene exhibited exceptional catalytic performance, attaining a reaction rate of 6.3 gCO/gcat/h at 400 °C with over 99% CO selectivity and good stability. Such a catalyst outperformed other Mo-based catalysts and noble metal-based catalysts in terms of facile dissociation of CO2, highly selective hydrogenation, and nonbarrier liberation of CO. Our study revealed that as a potential descriptor, the atomic magnetism linearly correlates to the liberation capacity of CO, and Mo defects facilitated product desorption by reducing the magnetization of the adsorption site. On the other hand, the defects were effective in neutralizing the negative charges of surface hydrogen, which is crucial for selective hydrogenation. Finally, we have successfully demonstrated that the combination of a carbon support and the carbonization process synergistically serves as a feasible strategy for creating rich Mo defects, and biochar can be a low-cost alternative option for large-scale applications.

Insights into Heterogeneous Catalysis on Surfaces with 3d Transition Metals: Spin-Dependent Chemisorption Models and Magnetic Field Effects

This Perspective provides an overview of recent developments in the field of 3d transition metal (TM) catalysts for different reactions, including oxygen-based reactions such as the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). The spin moments of 3d TMs can be exploited to influence chemical reactions, and recent advances in this area, including the theory of chemisorption based on spin-dependent d-band centers and magnetic field effects, are discussed. The Perspective also explores the use of scaling relationships and surface magnetic moments in catalyst design as well as the effect of magnetism on chemisorption and vice versa. In addition, recent studies on the influence of a magnetic field on the ORR and the OER are presented, demonstrating the potential of ferromagnetic catalysts to enhance these reactions through spin polarization.

Identifying the Critical Surface Descriptors for the Negative Slopes in the Adsorption Energy Scaling Relationships via Density Functional Theory and Compressed Sensing

Adsorption energy scaling relationships have progressed beyond their original form, which was primarily focused on optimizing catalytic sites and lowering computational costs in simulations. The recent rise in interest in adsorption energy scaling relations is to investigate surfaces other than transition metals (TMs) as well as interactions involving complex compounds. In this work, we report our extensive study on the scaling relation (SR) between oxygen (O), with elements of neighboring groups such as boron (B), aluminum (Al), carbon (C), silicon (Si), nitrogen (N), phosphorus (P), and fluorine (F) on magnetic bimetallic surfaces. We observed that only O versus N and F seems to have a positive slope; the other slopes are negative. We present new theoretical model in terms of multiple surface descriptors using density functional theory and compressed sensing, whereas the original scaling theory was based on a single adsorbate descriptor: adsorbate valency.

A Perspective on Modelling Metallic Magnetic Nanoparticles in Biomedicine: From Monometals to Nanoalloys and Ligand-Protected Particles

The focus of this review is on the physical and magnetic properties that are related to the efficiency of monometallic magnetic nanoparticles used in biomedical applications, such as magnetic resonance imaging (MRI) or magnetic nanoparticle hyperthermia, and how to model these by theoretical methods, where the discussion is based on the example of cobalt nanoparticles. Different simulation systems (cluster, extended slab, and nanoparticle models) are critically appraised for their efficacy in the determination of reactivity, magnetic behaviour, and ligand-induced modifications of relevant properties. Simulations of the effects of nanoscale alloying with other metallic phases are also briefly reviewed.