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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dataset of tensile properties for sub-sized specimens of nuclear structural materials

Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the “specimen size effect.” Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 55 columns including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mapping the nonequilibrium order parameter of a quasi-two dimensional charge density wave system

The driving force of a charge density wave (CDW) transition in quasi-two dimensional systems is still debated, while being crucial in understanding electronic correlation in such materials. Here we use femtosecond time- and angle-resolved photoemission spectroscopy combined with computational methods to investigate the coherent lattice dynamics of a prototypical CDW system. The photo-induced temporal evolution of the periodic lattice distortion associated with the amplitude mode reveals the dynamics of the free energy functional governing the order parameter. Our approach establishes that optically-induced screening rather than CDW melting at the electronic level leads to a transiently modified potential which explains the anharmonic behaviour of the amplitude mode and discloses the structural origin of the symmetry-breaking phase transition.

electronic properties and materials↗
DOE OSTI2025

Pausing ultrafast melting by timed multiple femtosecond-laser pulses

An intense femtosecond-laser excitation of a solid induces highly nonthermal conditions. In materials like silicon, laser-induced bond-softening leads to a highly incoherent ionic motion and eventually nonthermal melting. But is this outcome an inevitable consequence, or can it be controlled? Here, we performed ab initio molecular dynamics simulations of crystalline silicon after timed multiple femtosecond-laser pulse excitations with fluence above the nonthermal melting threshold. Our results demonstrate an excitation mechanism that pauses nonthermal melting and creates a metastable state instead, with an electronic structure similar to the ground state. This mechanism can be generalized to other materials, potentially enabling structural and/or electronic transitions to metastable phases in the high-excitation regime. In addition, our approach could be used to switch off nonthermal contributions in experiments, allowing reliable electron-phonon coupling constants to be obtained more easily.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Atomic-scale secondary electron imaging for heterogeneous catalysis research

Surface characterization at the atomic scale is essential for understanding the catalytic properties of supported metal nanoparticles. Secondary electron (SE) imaging in scanning transmission electron microscopy (STEM) provides three-dimensional surface topographic information, enabling the characterization of the size, morphology, and distribution of supported nanoparticles. Furthermore, real-time observation of catalyst materials in a gaseous environment would enhance the understanding of catalyst dynamics under operational conditions. Ongoing technical developments in SE-STEM and advancements in computational methods are expected to facilitate atomic-scale surface observations and enable more quantitative and statistical analyses. This progress will not only elucidate fundamental mechanisms at the atomic level but also provide comprehensive and universal insights into catalyst performances. Here, this minireview showcases the recent advancements and research findings in surface-sensitive SE imaging in STEM for the characterization of active catalyst materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Quantifying how the cis/trans ratio of N,N -dimethyl-3,5-dimethylpiperidinium hydroxide impacts the growth kinetics, composition and local structure of SSZ-39

This work integrates experiments and computational methods to quantify how the cis/trans ratio of the OSDA used in SSZ-39 synthesis impacts the crystallization kinetics, material properties, and final product composition. The crystallization kinetics increase by 30% when increasing the trans isomer content from 14% to 80%. Per prior work, in all cases based on the synthesis gel composition and product yield aluminum is the limiting reagent, and the absence of any amorphous material detected in the time resolved PXRD studies leads us to conclude that FAU dissolution is the rate limiting step in the formation of SSZ-39 in this synthesis protocol. The TGA and NMR results suggest that the trans isomer of OSDA is selectively incorporated into the product. The NMR binding studies, and corresponding DFT-based results show that the trans isomer binds to FAU more strongly than the cis isomer, providing one possible explanation for this enhancement in kinetics and preferential uptake of the trans isomer. The EDS analysis indicates that the Si/Al ratios are between 7.7 and 8.6 at low and high trans OSDA content, indicating zeolite composition is mildly sensitive to the trans isomer content. EDS results show this decrease in aluminum content leads to a corresponding decrease in sodium uptake. DFT-based calculations confirm OSDA–sodium interactions cannot explain any decrease in sodium uptake, reinforcing lower aluminum content as the cause of lower sodium uptake. Preliminary cobalt titration experiments show a surprisingly low cobalt uptake but also show a clear dependence of the cobalt uptake on the solution pH.

Cui, Zheng [Tulane University, New Orleans, LA (Un↗
DOE OSTI2026

Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A new quinoline-based cobalt( II ) catalyst capable of bifunctional water splitting

We report on a new water-soluble cobalt( II ) complex capable of water splitting bifunctionality, i.e., water reduction and water oxidation. The species [Co II (L Qpy )H 2 O]ClO 4 (1), where L Qpy is the deprotonated form of the new tripodal ligand N 1 ,N 1 -bis(pyridin-2-ylmethyl)-N 2 -(quinolin-8-yl)benzene-1,2-diamine, HL Qpy , was developed aiming to replace an oxidation prone methylene group by a sturdy and redox stable quinoline. The molecular and electronic structures of 1 were evaluated by multiple spectroscopic, spectrometric, electrochemical and computational methods, and detailed pre- and post-catalytic studies were conducted to ascertain the molecular nature of the conversions. Complex 1 performs water reduction at a low onset overpotential (η) of 0.65 V at pH 7, reaching TON 3h 2900 (TOF 970 h −1 ) and TON 18h 12 100 (TOF 672 h −1 ) with up to 98% faradaic efficiency (FE). Species 1 also promotes water oxidation at η = 0.34 V under pH 8, achieving TON 3h 193 (TOF 64 h −1 ) at 84% FE. Experimental and DFT results enabled us to propose reaction intermediates and mechanisms.

Lucecki, Carlos A. [Wayne State University, Detroi↗
DOE OSTI2026

Reduced salen in a monomeric Fe complex

We report the first monomeric formally M I transition metal salen complex without ion pairing. A combination of spectroscopy, crystallography, and computations indicates that reduction of an Fe II salen complex gives substantial reduced ligand character. We also present a computational method for accurately predicting 57 Fe Mössbauer parameters of Fe salen complexes.

Feldman, Dana M. [Yale University, New Haven, CT (↗
DOE OSTI2024

The physical chemistry of solar fuels catalysis

The demonstration of effective light-driven electrochemical water splitting at the surface of a TiO 2 electrode in the seminal 1972 Honda–Fujishima publication spurred global research efforts to understand this effect and design advanced systems for solar H 2 generation from water as a carbon-free and sustainable fuel. In the 50+ years since that discovery, research investigating approaches toward the catalytic generation of the so-called “solar fuels,” i.e., not only H 2 from water but also CO 2 reduction products and NH 3 from N 2 , has expanded dramatically in scope and scale. The development of new experimental and computational methods and construction and commissioning of large-scale facilities have paved the way for important insights into the machinery of natural photosynthesis, leading to a greater understanding of the complexity of light-to-chemical energy conversion with an exquisite temporal and spatial resolution. Finally, these studies have provided the foundation for biologically inspired molecular and hybrid designs with the goal to promote the photochemical steps critical to solar fuels generation while avoiding unproductive or unnecessary pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2026

Role of electron correlation on the adenine dimer interaction for non-equilibrium geometries: a benchmark Quantum Monte Carlo study

The accurate description of non-covalent interactions is critical for understanding the structure, dynamics, and eventual function of biomolecules. The adenine dimer serves as a benchmark system for computational methods due to its role in nucleic acid structures and its rich conformational landscape. In this study, we employ benchmark diffusion quantum Monte Carlo (DMC) methods to investigate the relative energies and role of electron correlation on a set of adenine dimer conformations generated via a search of the potential energy landscape using the global optimizer algorithm. Relative DMC energies are compared against a wide range of density functional theory (DFT) approximation results. We find that although most of the DFT functionals perform well for low-energy structures, their accuracy varies significantly for higher-energy conformations, including stacked and T-shaped structures. A large fraction of the variation is due to the treatment of the van der Waals interaction. BLYP, B3LYP, and PBE0 significantly improve with added D4 dispersion, while the recent r2SCAN-D4 and ωB97M-V functionals show the least scatter and closest agreement with the DMC. These findings highlight the delicate nature of these interactions in biomolecular systems and provide guidance for simulations of their structure and dynamics and for the development of machine learned interatomic potentials.

Washburn, Laurel [ORNL] (ORCID:0000000324179335)↗
DOE OSTI2026

Perspective of Fermi’s golden rule and its generalizations in chemical physics

This perspective provides a succinct history of Fermi’s golden rule (FGR), an overview of its derivation, assumptions, and representative forms. Major applications of FGR, mostly in the field of chemical physics, are reviewed. These illustrate the broad applicability and success of FGR. Ambiguities and open issues encountered in practical applications of FGR are clarified. Recent advances in generalizations of FGR and computational methods for practical applications are addressed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Extending the Nuclide Inventory Validation Basis for High-Burnup Fuel with New Radiochemical Assay Data

Efforts are underway at Oak Ridge National Laboratory to improve the nuclide inventory validation basis for spent nuclear fuel at high burnups. Recently conducted radiochemical assay experiments provided new measurement data for nine samples of fuel irradiated in a pressurized water reactor, with estimated sample burnups in the 30 to 70 GWd/t range. This type of destructive assay data is essential for validating computational methods, tools, and nuclear data applied in nuclear safety analyses and for improving our understanding of the bias and uncertainty in code predictions. The measurement data include key actinides and fission products that span a gamut of needs and interests for nuclear science and engineering applications in criticality safety, reactor physics, nuclide inventory, decay heat, and radiation shielding. The SCALE 6.3 code system with ENDF/B-VII.1 cross-section libraries was used to simulate the irradiation histories of the measured fuel samples. The calculated nuclide concentrations are compared to corresponding measurement data. The significance of the comparisons is discussed, emphasizing how the addition of the new measurement data fills gaps in the validation basis at high burnups and contributes to the decrease in bias and uncertainty for predicted nuclide concentrations. The discussion addresses the effect of the sample burnup used in the simulation—which is based on reactor operator records or on calibration to measured data for burnup indicator fission products—on the validation results.

Nuclide inventory↗
DOE OSTI2026

Transition Metals Separation with Commercial Neutral Extractants – A Review

The increasing use of extraction chromatography resins across fields such as hydrometallurgy, nuclear medicine, and environmental analysis has created a need for a deeper understanding of their interactions with transition metals. Despite extensive research on f-element separations, the behavior of transition metals in these systems remains relatively understudied. This review provides a comprehensive overview of the current state of knowledge on the extraction behavior of transition metals with neutral extractants, including TODGA, TEHDGA, TBP, and CMPO, and their corresponding resins, such as DGA, BDGA, UTEVA, TBP, and TRU. The review summarizes extraction data, extracted complex coordination environments, separation reaction stoichiometries, and associated thermodynamics, highlighting inconsistencies and knowledge gaps in the literature. The study emphasizes the need for further research using spectroscopy and computational methods to elucidate extraction mechanisms and to improve the efficiency and selectivity of transition metal separations. By identifying areas for future research and development, this review aims to stimulate advancements in the field and promote the development of innovative separation technologies. The implications of this research are far-reaching, with potential applications in nuclear waste management, nuclear forensics, metal recovery, and environmental remediation. Overall, this review provides a foundation for future studies on the extraction of transition metals using neutral extractants and resins.

Wall, Nathalie A.↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2025

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations

Abstract Motivation Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. Results We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (×105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization. Availability and implementation It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

Du, Lin (ORCID:0009000289869812)↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗