Adsorption Preference Determines Segregation Direction: A Shortcut to More Realistic Surface Models of Alloy Catalysts

Chemical Orderings in CuCo Nanoparticles: Topological Modeling Using DFT Calculations

The orderings of atoms in bimetallic 1.6-2.1 nm-large CuCo nanoparticles, important as catalytic and magnetic materials, were studied using a combination of DFT calculations with a topological approach. The structure and magnetism of Cu50Co151, Cu101Co100, Cu151Co50, and Cu303Co102 nanoparticles; their resistance to disintegrating into separate Cu and Co species; as well as the exposed surface sites, were quantified and analyzed, showing a clear preference for Cu atoms to occupy surface positions while the Co atoms tended to form a compact cluster in the interior of the nanoparticles. The surface segregation of Co atoms that are encapsulated by less-active Cu atoms, induced by the adsorption of CO molecules, was already enabled at a low coverage of adsorbed CO, providing the energy required to displace the entire compact Co species inside the Cu matrices due to a notable adsorption preference of CO for the Co sites over the Cu ones. The calculated adsorption energies and vibrational frequencies of adsorbed CO should be helpful indicators for experimentally monitoring the nature of the surface sites of CuCo nanoparticles, especially in the case of active Co surface sites emerging in the presence of CO.

Visualizing the gas-sensitive structure of the CuZn surface in methanol synthesis catalysis

Methanol formation over Cu/ZnO catalysts is linked with a catalytically active phase created by contact between Cu nanoparticles and Zn species whose chemical and structural state depends on reaction conditions. Herein, we use variable-temperature scanning tunneling microscopy at elevated pressure conditions combined with X-ray photoelectron spectroscopy measurements to investigate the surface structures and chemical states that evolve when a CuZn/Cu(111) surface alloy is exposed to reaction gas mixtures. In CO2 hydrogenation conditions, Zn stays embedded in the CuZn surface, but once CO gas is added to the mixture, the Zn segregates onto the Cu surface. The Zn segregation is CO-induced, and establishes a new dynamic state of the catalyst surface where Zn is continually exchanged at the Cu surface. Candidates for the migrating few-atom Zn clusters are further identified in time-resolved imaging series. The findings point to a significant role of CO affecting the distribution of Zn in the multiphasic ZnO/CuZn/Cu catalysts.

Single-Atom Alloys Materials for CO2 and CH4 Catalytic Conversion

The catalytic conversion of greenhouse gases CH4 and CO2 constitutes an effective approach for alleviating the greenhouse effect and generating valuable chemical products. However, the intricate molecular characteristics characterized by high symmetry and bond energies, coupled with the complexity of associated reactions, pose challenges for conventional catalysts to attain high activity, product selectivity, and enduring stability. Single-atom alloys (SAAs) materials, distinguished by their tunable composition and unique electronic structures, confer versatile physicochemical properties and modulable functionalities. In recent years, SAAs materials demonstrate pronounced advantages and expansive prospects in catalytic conversion of CH4 and CO2. This review begins by introducing the challenges entailed in catalytic conversion of CH4 and CO2 and the advantages offered by SAAs. Subsequently, the intricacies of synthesis strategies employed for SAAs are presented and characterization techniques and methodologies are introduced. The subsequent section furnishes a meticulous and inclusive overview of research endeavors concerning SAAs in CO2 catalytic conversion, CH4 conversion, and synergy CH4 and CO2 conversion. The particular emphasis is directed toward scrutinizing the intricate mechanisms underlying the influence of SAAs on reaction activity and product selectivity. Finally, insights are presented on the development and future challenges of SAAs in CH4 and CO2 conversion reactions.

Theoretical Insights into Amido Group-Mediated Enhancement of CO2 Hydrogenation to Methanol on Cobalt Catalysts

Catalytic reduction of carbon dioxide into high-value-added products, such as methanol, is an effective approach to mitigate the greenhouse effect, and improving Co-based catalysts is anticipated to yield potential catalysts with high performance and low cost. In this study, based on first-principles calculations, we elucidate the promotion effects of surface *NHx (x = 1, 2, and 3) on the carbon dioxide hydrogenation to methanol from both activity and selectivity perspectives on Co-based catalysts. The presence of *NHx reduced the energy barrier of each elementary step on Co(100) by regulating the electronic structure to alter the binding strength of intermediates or by forming a hydrogen bond between surface oxygen-containing species and *NHx to stabilize transition states. The best promotion effect for different steps corresponds to different *NHx. The energy barrier of the rate-determining step of CO2 hydrogenation to methanol is lowered from 1.55 to 0.88 eV, and the product selectivity shifts from methane to methanol with the assistance of *NHx on the Co(100) surface. A similar phenomenon is observed on the Co(111) surface. The promotion effect of *NHx on Co-based catalysts is superior to that of water, indicating that the introduction of *NHx on a Co-based catalyst is an effective strategy to enhance the catalytic performance of CO2 hydrogenation to methanol.

Modeling CoCu Nanoparticles Using Neural Network-Accelerated Monte Carlo Simulations

The correct description of catalytic reactions happening on bimetallic particles is not feasible without proper accounting of the segregation process. In this study, we tried to shed light on the structure of large CoCu particles, for which quite controversial results were published before. However, density functional theory (DFT) is challenging to be directly used for the systematic study of nanometer-sized particles. Therefore, we constructed a neural network-based potential and further applied it to the Monte Carlo simulations for the description of the segregation phenomenon. The resulting approach shows high efficiency and can be used in systems with thousands of atoms. The accuracy and transferability of the model to other sizes and compositions make this methodology useful for solving segregation problems.

In situ identification of surface sites in Cu-Pt bimetallic catalysts: Gas-induced metal segregation

The effect of gases on the surface composition of Cu-Pt bimetallic catalysts has been tested by in situ infrared (IR) and x-ray absorption spectroscopies. Diffusion of Pt atoms within the Cu-Pt nanoparticles was observed both in vacuum and under gaseous atmospheres. Vacuum IR spectra of CO adsorbed on CuPt_x/SBA-15 catalysts (x = 0-∞) at 125 K showed no bonding on Pt regardless of Pt content, but reversible Pt segregation to the surface was seen with the high-Pt-content (x ≥ 0.2) samples upon heating to 225 K. In situ IR spectra in CO atmospheres also highlighted the reversible segregation of Pt to the surface and its diffusion back into the bulk when cycling the temperature from 295 to 495 K and back, most evidently for diluted single-atom alloy catalysts (x ≤ 0.01). Similar behavior was possibly observed under H₂ using small amounts of CO as a probe molecule. In situ x-ray absorption near-edge structure data obtained for CuPt_0.2/SBA-15 under both CO and He pointed to the metallic nature of the Pt atoms irrespective of gas or temperature, but analysis of the extended x-ray absorption fine structure identified a change in coordination environment around the Pt atoms, from a (Pt-Cu):(Pt-Pt) coordination number ratio of ∼6:6 at or below 445 K to 8:4 at 495 K. The main conclusion is that Cu-Pt bimetallic catalysts are dynamic, with the composition of their surfaces being dependent on temperature in gaseous environments.

Challenges of modeling nanostructured materials for photocatalytic water splitting

Understanding the water splitting mechanism in photocatalysis is a rewarding goal as it will allow producing clean fuel for a sustainable life in the future. However, identifying the photocatalytic mechanisms by modeling photoactive nanoparticles requires sophisticated computational techniques based on multiscale modeling. In this review, we will survey the strengths and drawbacks of currently available theoretical methods at different length and accuracy scales. Understanding the surface-active site through Density Functional Theory (DFT) using new, more accurate exchange-correlation functionals plays a key role for surface engineering. Larger scale dynamics of the catalyst/electrolyte interface can be treated with Molecular Dynamics albeit there is a need for more generalizations of force fields. Monte Carlo and Continuum Modeling techniques are so far not the prominent path for modeling water splitting but interest is growing due to the lower computational cost and the feasibility to compare the modeling outcome directly to experimental data. The future challenges in modeling complex nano-photocatalysts involve combining different methods in a hierarchical way so that resources are spent wisely at each length scale, as well as accounting for excited states chemistry that is important for photocatalysis, a path that will bring devices closer to the theoretical limit of photocatalytic efficiency.

Designing Sites in Heterogeneous Catalysis: Are We Reaching Selectivities Competitive With Those of Homogeneous Catalysts?

A critical review of different prominent nanotechnologies adapted to catalysis is provided, with focus on how they contribute to the improvement of selectivity in heterogeneous catalysis. Ways to modify catalytic sites range from the use of the reversible or irreversible adsorption of molecular modifiers to the immobilization or tethering of homogeneous catalysts and the development of well-defined catalytic sites on solid surfaces. The latter covers methods for the dispersion of single-atom sites within solid supports as well as the use of complex nanostructures, and it includes the post-modification of materials via processes such as silylation and atomic layer deposition. All these methodologies exhibit both advantages and limitations, but all offer new avenues for the design of catalysts for specific applications. Because of the high cost of most nanotechnologies and the fact that the resulting materials may exhibit limited thermal or chemical stability, they may be best aimed at improving the selective synthesis of high value-added chemicals, to be incorporated in organic synthesis schemes, but other applications are being explored as well to address problems in energy production, for instance, and to design greener chemical processes. The details of each of these approaches are discussed, and representative examples are provided. We conclude with some general remarks on the future of this field.

Moderate Surface Segregation Promotes Selective Ethanol Production in CO2 Hydrogenation Reaction over CoCu Catalysts

Cobalt-copper (CoCu) catalysts have industrial potential in CO/CO₂ hydrogenation reactions, and CoCu alloy has been elucidated as a major active phase during reactions. However, due to elemental surface segregation and dealloying phenomena, the actual surface morphology of CoCu alloy is still unclear. Combining theory and experiment, the dual effect of surface segregation and varied CO coverage over the CoCu(111) surface on the reactivity in CO₂ hydrogenation reactions is explored. The relationship between C-O bond scission and further hydrogenation of intermediate *CH₂ O was discovered to be a key step to promote ethanol production. The theoretical investigation suggests that moderate Co segregation provides a suitable surface Co ensemble with lateral interactions of co-adsorbed *CO, leading to promoted selectivity to ethanol, in agreement with theory-inspired experiments.

Taming the butterfly effect: modulating catalyst nanostructures for better selectivity control of the catalytic hydrogenation of biomass-derived furan platform chemicals

This minireview highlights versatile routes for catalyst nanostructure modulation for better hydrogenation selectivity control of typical biomass-derived furan platform chemicals to tame the butterfly effect on the catalytic selectivity.

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.