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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Density functional theory studies of transition metal carbides and nitrides as electrocatalysts

Transition metal carbides and nitrides are interesting non-precious materials that have been shown to replace or reduce the loading of precious metals for catalyzing several important electrochemical reactions. The purpose of this review is to summarize density functional theory (DFT) studies, describe reaction pathways, identify activity and selectivity descriptors, and present a future outlook in designing carbide and nitride catalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), nitrogen reduction reaction (N 2 RR), CO 2 reduction reaction (CO 2 RR) and alcohol oxidation reactions. Furthermore, this topic is of high interest to scientific communities working in the field of electrocatalysis and this review should provide theoretical guidance for the rational design of improved carbide and nitride electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A thermal evaporation–trapping strategy to synthesize flexible and robust oxygen electrocatalysts for rechargeable zinc–air batteries

Great efforts have been devoted to the development of bifunctional electrocatalysts to accelerate the sluggish kinetics of cathodic oxygen reduction/evolution reactions (ORR/OER) in zinc–air batteries (ZABs). Here we report a thermal evaporation–trapping synergistic strategy to fabricate a bifunctional electrocatalyst of flexible N-doped carbon fiber cloth loaded with both CoFe-oxide nanoparticles and single-atom Co/Fe–Nx sites, in which the thermal evaporation process functions in both downsizing CoFe-oxide nanoparticles and trapping the evaporated Co/Fe species to generate Co/Fe–Nx sites. The obtained flexible electrocatalyst, directly serving as an oxygen electrode, displays a small potential gap of 0.542 V for the OER/ORR, large peak power densities (liquid-state ZAB: 237.4 mW cm2 ; solid-state ZAB: 141.1 mW cm2 ), and excellent charge–discharge cycling stability without decay after working more than 770 hours. Furthermore, in situ Raman spectroscopy characterization and theoretical calculations reveal that CoFe2O4 species is responsible for the OER while atomic Fe/Co sites play a key role in the ORR

Zhang, Hong-Bo↗
DOE OSTI2025

Molecular catalyst and co-catalyst systems based on transition metal complexes for the electrochemical oxidation of alcohols

Molecular catalysts allow deeper study of underlying mechanisms relative to heterogeneous systems by offering a discrete active site to monitor. Mechanistic study with knowledge of key intermediates subsequently enables the development of design principles through an understanding of how improved reactivity or selectivity can be achieved through modification of the catalyst structure. The co-catalytic inclusion of redox mediators (RM), which are small molecules that can aid in the transfer of protons and electrons, has been shown to improve product conversion and selectivity in many molecular systems, through intercepting key intermediates to direct reaction pathways. The primary focus for the majority of molecular electrocatalysts has been on optimizing design for reductive reactions, such as the hydrogen evolution reaction (HER), the oxygen reduction reaction (ORR), and the carbon dioxide reduction reaction (CO 2 RR). By comparison, there has been much less focus on key oxidative reactions by molecular species, apart from the oxygen evolution reaction (OER). The focus of this review is to highlight molecular catalyst systems optimized for the electrochemical oxidation of alcohols. The electrochemical alcohol oxidation reaction (AOR) can serve a role in synthesizing value-added chemicals and can serve as the counterpart to the CO 2 RR by releasing electricity from energy-rich molecules. State-of-the-art molecular systems for the AOR are divided between single-site catalysts and co-catalytic systems with redox mediators. The AOR is contextualized as an energy relevant reaction, an overview of the area is provided, foundational improvements in catalyst systems are highlighted, and future development principles for incorporating redox mediators are suggested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Carbon nanodot precursors enable ultrahigh-loading copper single-atom catalysts for oxygen reduction reaction

Single-atom catalysts (SACs) have attracted significant attention in electrocatalysis due to their high metal utilization and tunable electronic structures; however, their practical implementation is often limited by poor atomic dispersion, low metal loadings (typically < 1 wt%), and insufficient anchoring of single metal atoms on catalyst supports. Herein, we report a simple and versatile strategy to synthesize copper single-atom catalysts (Cu-SACs) with ultrahigh metal loading using citric-acid-derived carbon nanodots as metal-capturing precursors. Through hydrothermal treatment followed by pyrolysis in the presence of urea, atomically dispersed copper atoms coordinated with nitrogen are incorporated into a carbon framework, achieving a Cu loading of up to 20 wt% while maintaining atomic dispersion. Notably, a significant fraction of the copper single atoms exists as Cu+–N2 coordination, which provides under-coordinated and catalytically active sites. The resulting Cu-SAC exhibits promising oxygen reduction reaction (ORR) activity in alkaline media, delivering a limiting current density comparable to that of commercial 20 wt% Pt/C with a predominant four-electron reduction pathway. Despite this high intrinsic activity, durability tests reveal gradual performance degradation under prolonged electrochemical operation, which is attributed to demetallation and aggregation of copper single atoms into metallic nanoparticles. These results demonstrate the potential of carbon nanodot-based platforms for synthesizing high-loading SACs, while underscoring the critical need to simultaneously control carbon porosity and metal–nitrogen coordination to enhance active-site accessibility and durability.

Sharma, Prakhar [University of Kentucky]↗
DOE OSTI2021

On closures for reduced order models - A spectrum of first-principle to machine-learned avenues

For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr–Sommerfeld stability equation and numerous vortex models, of which the von Karman vortex street is one of the most prominent. Subsequent ROMs typically relied on first principles, like mathematical Galerkin models, weakly nonlinear stability theory, and two- and three-dimensional vortex models pioneered the data-driven proper orthogonal decomposition (POD) modeling. In early POD modeling, available data were used to build an optimal basis, which was then utilized in a classical Galerkin procedure to construct the ROM, but data have made a profound impact on ROMs beyond the Galerkin expansion. In this paper, we take a modest step and illustrate the impact of data-driven modeling on one significant ROM area. Specifically, we focus on ROM closures, which are correction terms that are added to the classical ROMs in order to model the effect of the discarded ROM modes in under-resolved simulations. Through simple examples, we illustrate the main modeling principles used to construct the classical ROMs, motivate and introduce modern ROM closures, and show how data-driven modeling, artificial intelligence, and machine learning have changed the standard ROM methodology over the last two decades. Finally, we outline our vision on how the state-of-the-art data-driven modeling can continue to reshape the field of reduced order modeling.

42 ENGINEERING↗
DOE OSTI2022

Ecophysiological and genomic analyses of a representative isolate of highly abundant Bacillus cereus strains in contaminated subsurface sediments

Abstract Bacillus cereus strain CPT56D‐587‐MTF (CPTF) was isolated from the highly contaminated Oak Ridge Reservation (ORR) subsurface. This site is contaminated with high levels of nitric acid and multiple heavy metals. Amplicon sequencing of the 16S rRNA genes (V4 region) in sediment from this area revealed an amplicon sequence variant (ASV) with 100% identity to the CPTF 16S rRNA sequence. Notably, this CPTF‐matching ASV had the highest relative abundance in this community survey, with a median relative abundance of 3.77% and comprised 20%–40% of reads in some samples. Pangenomic analysis revealed that strain CPTF has expanded genomic content compared to other B. cereus species—largely due to plasmid acquisition and expansion of transposable elements. This suggests that these features are important for rapid adaptation to native environmental stressors. We connected genotype to phenotype in the context of the unique geochemistry of the site. These analyses revealed that certain genes (e.g. nitrate reductase, heavy metal efflux pumps) that allow this strain to successfully occupy the geochemically heterogenous microniches of its native site are characteristic of the B. cereus species while others such as acid tolerance are mobile genetic element associated and are generally unique to strain CPTF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metamodeling Time-Temperature Creep Parameters

Abstract There exist many time-temperature parameter (TTP) models for creep rupture prediction of components including the Larson–Miller (LM), Manson–Haferd (MH), Manson–Brown (MB), Orr–Sherby–Dorn (OSD), Manson–Succop (MS), Graham–Walles (GW), Chitty–Duval (CD), Goldhoff–Sherby (GS) models. It remains a challenge to determine which model is “best”, capable of accurate interpolation and physically realistic extrapolation of creep rupture data for a given material. In this study, metamodeling is applied to create a unified TTP metamodel that combines and regresses into twelve TTP models (eight existing and four newly derived). An analysis of the mathematical problems that exist in TTP models is provided. A matlab code is written that can: (1) calibrate the material constants of any of the twelve TTP models (using the metamodel); (2) determine the most suitable stress-parameter function; (3) and report the normalized mean square error (NMSE) of rupture predictions for a given material database. Using the metamodel, and code, a design engineer can make an intelligent selection of the “best” TTP model for creep resistant design. This process is demonstrated using four isotherms of alloy P91 creep rupture data. To assess the influence of material, further validation is performed on alloys Hastelloy X, 304SS, and 316SS. It is determined that the “best” model is dependent on material type and the quality and quantity of available data.

Engineering↗