The Effect of Surface Oxygen Coverage on the Oxygen Evolution Reaction over a CoFeNiCr High-Entropy Alloy

Developing cost-effective and highly active electrocatalysts for the oxygen evolution reaction (OER) is crucial for advancing sustainable energy applications. High-entropy alloys (HEAs) made from earth-abundant transition metals, thanks to their remarkable stability and electrocatalytic performance, provide a promising alternative to expensive electrocatalysts typically derived from noble metals. While pristine HEA surfaces have been theoretically investigated, and the effect of oxygen coverage on conventional metal electrocatalysts has been examined, the impact of surface oxygen coverage on the electrocatalytic performance of HEAs remains poorly understood. To bridge this gap, we employ density functional theory (DFT) calculations to reconstruct the free energy diagram of OER intermediates on CoFeNiCr HEA surfaces with varying oxygen coverages, evaluating their impact on the rate-limiting step and theoretical overpotential. Our findings reveal that increased oxygen coverage weakens the adsorption of HO* and O*, but not HOO*. As a result, the theoretical overpotential for the OER decreases with higher oxygen coverage, and the rate-limiting step shifts from the third oxidation step (HOO* formation) at low coverage to the first oxidation step (HO* formation) at higher coverage.

Catalytic Activity Maps for Alloy Nanoparticles

To enable rational design of alloy nanoparticle catalysts, we develop an approach to generate catalytic activity maps of alloy nanoparticles on a grid of particle size and composition. The catalytic activity maps are created by using a quaternary cluster expansion to explicitly predict adsorbate binding energies on alloy nanoparticles of varying shape, size, and atomic order while accounting for interactions among the adsorbates. This cluster expansion is used in kinetic Monte Carlo simulations to predict activated nanoparticle structures and turnover frequencies on all surface sites. We demonstrate our approach on Pt-Ni octahedral nanoparticle catalysts for the oxygen reduction reaction (ORR), revealing that the specific activity is predicted to be optimized at an edge length of larger than 5.5 nm and a composition of about Pt_0.85Ni_0.15 and the mass activity is predicted to be optimized at an edge length of 3.3-3.8 nm and a composition of about Pt_0.8Ni_0.2.

Pt nanoparticles under oxidizing conditions - implications of particle size, adsorption sites and oxygen coverage on stability

Platinum nanoparticles are efficient catalysts for different reactions, such as oxidation of carbon and nitrogen monoxides. Adsorption and interaction of oxygen with the nanoparticle surface, taking place under reaction conditions, determine not only the catalytic efficiency but also the stability of the nanoparticles against oxidation. In this study, platinum nanoparticles in oxygen environment are investigated by systematic screening of initial nanoparticle-oxygen configurations and employing density functional theory and a thermodynamics-based approach. The structures formed at low oxygen coverages are described by adsorption of atomic oxygen on the nanoparticles whereas at high coverages oxide-like species are formed. The relative stability of adsorption configurations at different oxygen coverages, including the phase of fully oxidized nanoparticles, is investigated by constructing p-T phase diagrams for the studied systems.

Understanding the effect of mechanical strains on the catalytic activity of transition metals

The effect of elastic strains on the catalytic activity for the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) was analyzed on thirteen late transition metals: eight (111) surfaces of fcc metals (Ni, Cu, Pd, Ag, Pt, Au, Rh, Ir) and five (0001) surfaces of hcp metals (Co, Zn, Cd, Ru, and Os). The corresponding adsorption energies for the different intermediate reactions up to strains dictated by the mechanical stability limits were previously obtained by means of density functional theory calculations. It was found that the elastic strains can be used to tune the catalytic activity of different metals by reducing the energy barrier of the rate limiting step and even to reach the cusp of the volcano plot. The largest changes in catalytic activity with strain for the HER were found in Pt, Au, and Ir while Co and Ni were very insensitive to this strategy. In the case of the ORR, the catalytic activity of Au could be enhanced by the application of tensile strains while that of Cu, Ni, Pt, Pd, Rh, Co, Ru, and Os was improved by the application of compressive strains. However, the catalytic activity of Ir was rather insensitive to mechanical deformations. Elastic strains were able to modify the rate limiting reaction in Au, Pt, Ag, and Os and it was possible to achieve the cusp of the volcano plot in these metals. Final, mechanical instabilities were attained at small strains in Zn and Cd, which did not lead to significant changes in the catalytic activity for the HER and the ORR. These results provide a framework to systematically investigate the application of elastic strains in the design of new catalysts.

Oxygen adsorption on high-index faceted Pt nanoparticles

High-index faceted Pt nanoparticles with excellent electrocatalytic performances are promising to efficiently accelerate the oxygen reduction reactions in fuel cells. By adopting the hybrid grand canonical Monte Carlo reactive molecular dynamics (GCMC/RMD) simulations, we examined the oxygen adsorption on three 24-facet nanoparticles respectively enclosed by {310}, {311}, and {331} high-index facets. The site-dependent adsorption energies on each open-structure surface are calculated. Meanwhile, the adsorption ratios under various pressures and temperatures are presented. It is revealed that the adsorption capacity of these high-index faceted nanoparticles is considerably higher than that of the ones terminated by low-index facets. Moreover, oxygen adsorption exerts a significant impact on their thermodynamic behaviors.

Density Functional Theory and Machine Learning Description and Prediction of Oxygen Atom Chemisorption on Platinum Surfaces and Nanoparticles

Elucidating chemical interactions between catalyst surfaces and adsorbates is crucial for understanding surface chemical reactivity. Herein, interactions between O atoms and Pt surfaces and nanoparticles are described as a linear combination of the properties of pristine surfaces and isolated nanoparticles. The energetics of O chemisorption onto Pt surfaces were described using only two descriptors related to surface geometrical features. The relatively high coefficient of determination and low mean absolute error between the density functional theory-calculated and predicted O binding energies indicate good accuracy of the model. For Pt nanoparticles, O binding is described by the geometrical features and electronic properties of isolated nanoparticles. Using a linear combination of five descriptors and accounting for nanoparticle size effects and adsorption site types, the O binding energy was estimated with a higher accuracy than with conventional single-descriptor models. Finally, these five descriptors were used in a general model that decomposes O binding energetics on Pt surfaces and nanoparticles. Good correlation was achieved between the calculated and predicted O binding energies, and model validation confirmed its accuracy. This is the first model that considers the nanoparticle size effect and all possible adsorption sites on Pt nanoparticles and surfaces.

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.

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Facile engineering of silk fibroin capped AuPt bimetallic nanozyme responsive to tumor microenvironmental factors for enhanced nanocatalytic therapy

Background: Reactive oxygen species (ROS), as a category of highly reactive molecules, are attractive for eliminating tumor cells in situ. However, the intrinsic tumor microenvironment (TME) always compromises treatment efficacy. In another aspect, silk fibroin (SF), as a category of natural biomacromolecules, is highly promising for synthesis of metallic nanocrystals via biomineralization.

Methods: As a proof-of-concept study, AuPt bimetallic nanozyme derived from bioinspired crystallization of chloroauric acid and chloroplatinic acid was facilely developed in the presence of silk fibroin (SF). Antitumor effects caused by the as-synthesized AuPt@SF (APS) nanozyme were demonstrated in 4T1 tumor cells in vitro and xenograft tumor models in vivo.

Results: APS nanozyme can decompose glucose to constantly supply H₂O₂ and deplete intracellular glutathione (GSH). APS nanozyme can simultaneously convert adsorbed O₂ and endogenic H₂O₂ into superoxide radicals (^•O₂^-) and hydroxyl radical (^•OH), respectively, upon highly efficient catalytic reaction. Subsequently, these cytotoxic ROS cause irreversible damage to the cell membrane, nucleic acid and mitochondria of tumors. Upon fluorescence/photoacoustic (FL/PA)-imaging guidance, remarkable tumor damage based on the current nanoplatform was confirmed in vivo.

Conclusion: The objective of our investigation is to supply more useful insights on the development of SF-based nanocatalysts, which are specifically responsive to TME for extremely efficient tumor theranostics.

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.

Adsorbate-Induced Segregation of Cobalt from PtCo Nanoparticles: Modeling Au Doping and Core AuCo Alloying for the Improvement of Fuel Cell Cathode Catalysts

Platinum, when used as a cathode material for the oxygen reduction reaction, suffers from high overpotential and possible dissolution, in addition to the scarcity of the metal and resulting cost. Although the introduction of cobalt has been reported to improve reaction kinetics and decrease the precious metal loading, surface segregation or complete leakage of Co atoms causes degradation of the membrane electrode assembly, and either of these scenarios of structural rearrangement eventually decreases catalytic power. Ternary PtCo alloys with noble metals could possibly maintain activity with a higher dissolution potential. First-principles-based theoretical methods are utilized to identify the critical factors affecting segregation in Pt-Co binary and Pt-Co-Au ternary nanoparticles in the presence of oxidizing species. With a decreasing share of Pt, surface segregation of Co atoms was already found to become thermodynamically viable in the PtCo systems at low oxygen concentrations, which is assigned to high charge transfer between species. While the introduction of gold as a dopant caused structural changes that favor segregation of Co, creation of CoAu alloy core is calculated to significantly suppress Co leakage through modification of the electronic properties. The theoretical framework of geometrically different ternary systems provides a new route for the rational design of oxygen reduction catalysts.

Metal-Organic Framework Nanoparticles Induce Pyroptosis in Cells Controlled by the Extracellular pH

Ion homeostasis is essential for cellular survival, and elevated concentrations of specific ions are used to start distinct forms of programmed cell death. However, investigating the influence of certain ions on cells in a controlled way has been hampered due to the tight regulation of ion import by cells. Here, it is shown that lipid-coated iron-based metal-organic framework nanoparticles are able to deliver and release high amounts of iron ions into cells. While high concentrations of iron often trigger ferroptosis, here, the released iron induces pyroptosis, a form of cell death involving the immune system. The iron release occurs only in slightly acidic extracellular environments restricting cell death to cells in acidic microenvironments and allowing for external control. The release mechanism is based on endocytosis facilitated by the lipid-coating followed by degradation of the nanoparticle in the lysosome via cysteine-mediated reduction, which is enhanced in slightly acidic extracellular environment. Thus, a new functionality of hybrid nanoparticles is demonstrated, which uses their nanoarchitecture to facilitate controlled ion delivery into cells. Based on the selectivity for acidic microenvironments, the described nanoparticles may also be used for immunotherapy: the nanoparticles may directly affect the primary tumor and the induced pyroptosis activates the immune system.

Low-Temperature Catalytic NO Reduction with CO by Subnanometric Pt Clusters

The catalytic subnanometric metal clusters with a few atoms can be regarded as an intermediate state between single atoms and metal nanoparticles (>1 nm). Their molecule-like electronic structures and flexible geometric structures bring rich chemistry and also a different catalytic behavior, in comparison with the single-atom or nanoparticulate counterparts. In this work, by combination of operando IR spectroscopy techniques and electronic structure calculations, we will show a comparative study on Pt catalysts for CO + NO reaction at a very low temperature range (140–200 K). It has been found that single Pt atoms immobilized on MCM-22 zeolite are not stable under reaction conditions and agglomerate into Pt nanoclusters and particles, which are the working active sites for CO + NO reaction. In the case of the catalyst containing Pt nanoparticles (∼2 nm), the oxidation of CO to CO₂ occurs in a much lower extension, and Pt nanoparticles become poisoned under reaction conditions because of a strong interaction with CO and NO. Therefore, only subnanometric Pt clusters allow NO dissociation at a low temperature and CO oxidation to occur well on the surface, while CO interaction is weak enough to avoid catalyst poisoning, resulting in a good balance to achieve enhanced catalytic performance.

Perspectives on the design of nanoparticle systems for catalysis

An overview of the Faraday Discussion, "Designing Nanoparticle Systems for Catalysis", is presented. Examples are taken from the papers presented at the meeting and from the literature to illustrate the main discussion points.

Ethanol, O, and CO adsorption on Pt nanoparticles: effects of nanoparticle size and graphene support

DFT calculations reveal aspects of size and support effects for Pt nanoparticles on graphene interacting with O, CO and ethanol.