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Wang, Xijun

Publications and source records attributed to Wang, Xijun.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photocatalytic Semi‐Hydrogenation of Acetylene to Polymer‐Grade Ethylene with Molecular and Metal–Organic Framework Cobaloximes

Abstract The semi‐hydrogenation of acetylene in ethylene‐rich gas streams is a high‐priority industrial chemical reaction for producing polymer‐grade ethylene. Traditional thermocatalytic routes for acetylene reduction to ethylene, despite progress, still require high temperatures and high H 2 consumption, possess relatively low selectivity, and use a noble metal catalyst. Light‐powered strategies are starting to emerge, given that they have the potential to use directly the abundant and sustainable solar irradiation, but are ineffective. Here an efficient, >99.9% selective, visible‐light powered, catalytic conversion of acetylene to ethylene is reported. The catalyst is a homogeneous molecular cobaloxime that operates in tandem with a photosensitizer at room temperature and bypasses the use of non‐environmentally friendly and flammable H 2 gas feed. The reaction proceeds through a cobalt‐hydride intermediate with ≈100% conversion of acetylene under competitive (ethylene co‐feed) conditions after only 50 min, and with no evolution of H 2 or over‐hydrogenation to ethane. The cobaloxime is further incorporated as a linker in a metal–organic framework; the result is a heterogeneous catalyst for the conversion of acetylene under competitive (ethylene co‐feed) conditions that can be recycled up to six times and remains catalytically active for 48 h, before significant loss of performance is observed.

Stone, Aaron E. B. S.↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2024

An active, stable cubic molybdenum carbide catalyst for the high-temperature reverse water-gas shift reaction

Although technologically promising, the reduction of carbon dioxide (CO 2 ) to produce carbon monoxide (CO) remains economically challenging owing to the lack of an inexpensive, active, highly selective, and stable catalyst. We show that nanocrystalline cubic molybdenum carbide (α-Mo 2 C), prepared through a facile and scalable route, offers 100% selectivity for CO 2 reduction to CO while maintaining its initial equilibrium conversion at high space velocity after more than 500 hours of exposure to harsh reaction conditions at 600°C. The combination of operando and postreaction characterization of the catalyst revealed that its high activity, selectivity, and stability are attributable to crystallographic phase purity, weak CO-Mo 2 C interactions, and interstitial oxygen atoms, respectively. Mechanistic studies and density functional theory (DFT) calculations provided evidence that the reaction proceeds through an H 2 -aided redox mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling

This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evidence of a Uranium‐Paddlewheel Node in a Catecholate‐Based Metal–Organic Framework

Abstract The interactions between uranium and non‐innocent organic species are an essential component of fundamental uranium redox chemistry. However, they have seldom been explored in the context of multidimensional, porous materials. Uranium‐based metal–organic frameworks (MOFs) offer a new angle to study these interactions, as these self‐assembled species stabilize uranium species through immobilization by organic linkers within a crystalline framework, while potentially providing a method for adjusting metal oxidation state through coordination of non‐innocent linkers. We report the synthesis of the MOF NU‐1700 , assembled from U 4+ ‐paddlewheel nodes and catecholate‐based linkers. We propose this highly unusual structure, which contains two U 4+ ions in a paddlewheel built from four linkers—a first among uranium materials—as a result of extensive characterization via powder X‐ray diffraction (PXRD), sorption, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA), in addition to density functional theory (DFT) calculations.

Knapp, Julia G.↗
DOE OSTI2023

Evidence of a Uranium‐Paddlewheel Node in a Catecholate‐Based Metal–Organic Framework

Abstract The interactions between uranium and non‐innocent organic species are an essential component of fundamental uranium redox chemistry. However, they have seldom been explored in the context of multidimensional, porous materials. Uranium‐based metal–organic frameworks (MOFs) offer a new angle to study these interactions, as these self‐assembled species stabilize uranium species through immobilization by organic linkers within a crystalline framework, while potentially providing a method for adjusting metal oxidation state through coordination of non‐innocent linkers. We report the synthesis of the MOF NU‐1700 , assembled from U 4+ ‐paddlewheel nodes and catecholate‐based linkers. We propose this highly unusual structure, which contains two U 4+ ions in a paddlewheel built from four linkers—a first among uranium materials—as a result of extensive characterization via powder X‐ray diffraction (PXRD), sorption, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA), in addition to density functional theory (DFT) calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Computational Screening of Supported Metal Oxide Nanoclusters for Methane Activation: Insights into Homolytic versus Heterolytic C–H Bond Dissociation

Since its discovery in zeolites, the [CuOCu] 2+ motif has played an important role in our understanding of selective methane activation over supported metal oxide nanoclusters. Although there are two known C-H bond dissoci-ation mechanisms, namely homolytic and heterolytic cleavage, most computational studies on optimizing metal oxide nanoclusters for improved methane activation reactivity have focused only on the homolytic mechanism. In this work, both mechanisms were examined for a set of 21 mixed metal oxide complexes of the form of [M 1 OM 2 ] 2+ (M 1 , M 2 = Mn, Fe, Co, Ni, Cu, Zn). Except for pure copper, heterolytic cleavage was found to be the dominant C-H bond activation pathway for all systems. Furthermore, mixed systems including [CuOMn] 2+ , [CuONi] 2+ , and [CuOZn] 2+ are predicted to possess similar methane activation activity as pure [CuOCu] 2+ . Furthermore, these results suggest that both homolytic and heterolytic mechanisms should be considered in computing methane activation energies on supported metal oxide nanoclusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗