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Atomic vacancies in oxides induce deviations from ideal stoichiometry, critically influencing their functional properties in applications such as energy storage-conversion, catalysis, and electronic devices. The dynamic behavior of these vacancies as main mass transport mediums to exchange chemical species with surroundings under operating conditions is central to oxide redox reactions running with the Mars-van Krevelen (MvK) mechanism; yet in-situ atomic-scale monitoring of the vacancy dynamics and vacancy-induced secondary defects within oxides remains challenging due to both their rapid transport kinetics at buried subsurface/interface and characterization difficulties, arising from the insulating nature of bulk oxides and the spatial-resolution requirement in reaction conditions. These challenges hinder precise defect engineering for the performance optimization of functional oxides. In this review, recent advancements in tracking oxygen vacancy and vacancy-induced secondary defects dynamics in oxides, including surface steps, cation vacancies, interfacial dislocations, ledges, and interfaces, have been summarized. The dynamic interconversion of defects and their synergistic effects on surface/subsurface/interface evolution are mainly discussed. The aim of this review is to enhance understanding of defect dynamics and their pivotal role in modulating structural dynamics and surface reaction reactivity, which is highly relevant to the catalyst activity/selectivity/stability evaluation of functional oxide catalysts for electroreduction and catalytic oxidation reactions. Finally, strategies to control buried subsurface and interfacial defects (interface engineering) through tailored surface reactions are proposed, offering new pathways to customize the performance of advanced oxide-based materials. 

The oxygen reduction reaction (ORR) is a critical process that often governs the overall performance of electrochemical systems such as proton conducting solid oxide fuel cells. Despite its significance, the current understanding about the ORR, especially when it involves H2O is still limited. In this study, the proton involved ORR (PI-ORR) on metal cathode (e.g., Pt) over BaZrO3 (BZO) electrolyte is investigated using density functional theory (DFT) under moist atmosphere. Two scenarios are considered: one at relatively high temperature (900 K) in the presence of oxygen vacancy in BZO substrate and one at relatively low temperature (700 K) without oxygen vacancy. We systematically explored oxygen vacancy effects on adsorption sites and proton transfer pathways, calculated energy profiles and Gibbs free energies for elementary steps along the O2 dissociated and the O2 concerted reaction pathways. We also evaluated H2O impacts on intermediate oxygen species (*O and *OH) through Ab Initio molecular dynamics. These analyses revealed pathway-dependent reaction mechanisms and hydration effects at triple-phase boundary, advancing fundamental understanding of PI-ORR in metal cathode/Ba-based electrolyte systems for energy applications. 

ABSTRACT Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a 3-dimensional array with elements that can be grouped according to 2 directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a full Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates. 

The oxygen reduction reaction (ORR) is a critical process in energy conversion systems, influencing the efficiency and performance of various devices such as fuel cells, batteries, and electrolyzers. Perovskite-supported metal materials (metal/perovskite) offer several advantages as ORR electrocatalysts, including strong metal-support interactions, oxygen vacancy formation in the perovskite lattice, and synergistic triple-phase boundary (TPB) activity at the interface. Despite their significance, the mechanistic understanding of ORR on metal/perovskite catalysts remains incomplete, particularly at metal/perovskite interfaces. This study investigates ORR on BaZrO3 (BZO) perovskite-supported metal clusters (Pt or Ag) using density functional theory (DFT) to unravel critical insights into charge redistribution at the metal/BZO interface. Energy profiles for elemental steps along two different ORR pathways—oxygen adsorption on the metal cluster surface and direct oxygen adsorption at the TPB—were calculated to explore the effects of different active sites. The results provide a deeper understanding of ORR on metal/perovskite catalysts, emphasizing the role of interfacial interactions and pathway-dependent reaction mechanisms. This work paves the way for guiding the design of high-performance electrocatalysts for ORR in terms of composition, interface design, and local environment modification for a broad range of energy applications. 

The transition to hydrogen as a green reductant in metal production is critical for decarbonizing the metallurgical industry, yet atomic-scale mechanisms governing reduction pathways and phase evolution remain unresolved. Using in-situ environmental transmission electron microscopy, we identify a hidden pathway that reveals dynamic formation of amorphous metallic iron (Fe) during the hydrogen-driven reduction of ferrous oxides of Fe3O4 and FeO. Real-time imaging uncovers three coexisting transformation routes: (i) Fe3O4 → FeO, (ii) Fe3O4 → amorphous Fe, and (iii) FeO → amorphous Fe. The resulting amorphous Fe exhibits fluid-like mobility, enabling its rapid aggregation and crystallization into core-shell nanostructures, with a crystalline core enveloped by an amorphous shell. Complementary ab initio molecular dynamics simulations trace the amorphous Fe formation to interfacial strain at the metal/oxide interfaces, where large lattice mismatches destabilize the metal lattice during initial metallization. This interplay between thermodynamics and kinetics governs phase evolution: thermodynamics favors a self-limiting amorphous Fe overlayer, while rapid oxide reduction kinetics drives amorphous overgrowth. Our findings demonstrate that amorphous intermediates bypass rate-limiting crystalline steps, providing mechanistic insights to optimize H2-based processes for sustainable steelmaking. These insights bridge the gap between macroscopic process engineering and atomic-scale dynamics, with broader implications for catalysis and nanostructured material synthesis, where oxide reduction pathways critically shape functional phases and microstructures. 

The Transparent Research Object Vocabulary (TROV) is a key element of the Transparency Certified (TRACE) approach to ensuring research trustworthiness. In contrast with methods that entail repeating computations in part or in full to verify that the descriptions of methods included in a publication are sufficient to reproduce reported results, the TRACE approach depends on a controlled computing environment termed a Transparent Research System (TRS) to guarantee that accurate, sufficiently complete, and otherwise trustworthy records are captured when results are obtained in the first place. Records identifying (1) the digital artifacts and computations that yielded a research result, (2) the TRS that witnessed the artifacts and supervised the computations, and (3) the specific conditions enforced by the TRS that warrant trust in these records, together constitute a Transparent Research Object (TRO). Digital signatures provided by the TRS and by a trusted third-party timestamp authority (TSA) guarantee the integrity and authenticity of the TRO. The controlled vocabulary TROV provides means to declare and query the properties of a TRO, to enumerate the dimensions of trustworthiness the TRS asserts for a TRO, and to verify that each such assertion is warranted by the documented capabilities of the TRS. Our approach for describing, publishing, and working with TROs imposes no restrictions on how computational artifacts are packaged or otherwise shared, and aims to be interoperable with, rather than to replace, current and future Research Object standards, archival formats, and repository layouts.