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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A spline-based method to obtain spatially dependent viscosity in confined flows

Coupling chemical physics to continuum theories is a critical step to understanding multi-scale phenomena. This paper will connect non-equilibrium molecular dynamics simulations to a continuum-based Navier-Stokes equation that has relaxed the assumption of spatial uniformity in viscosity. Using a form for viscosity based on spline interpolation, viscosity as a function of position is obtained from the least squares fit of the velocity profile measured from molecular simulations of flow in a nanochannel. Viscosity can vary widely, particularly near the channel boundaries, indicating that uniform viscosity is no longer appropriate. Variations of the viscosity near the channel surfaces imply that considering solution and surface chemistry could be necessary to rigorously understand molecular-scale flows in nanochannels.

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

Development of Industrial Scale Rare Earth Master Alloys from Their Native Oxides for Magnet Production

The objective of the project is to develop an energy-efficient, reduced cost, and single-step critical metal oxide reduction and alloying methodology for the production of NdFeB and SmCo magnets to facilitate the establishment of a sustainable domestic critical materials supply chain. The method consists of an immiscible molten salt flux layer and a higher density molten metal alloy pool (FeB or Co). The rare earth (RE) oxide and a reductant are added to the molten salt layer, where the reductant first strips the oxygen from the rare earth oxide. Subsequently, the separated RE metal diffuses into the molten metal pool below, creating a RE-saturated master alloy. Towards this end, thermodynamic calculations of the reactions between RE oxides, metallic reducing agent, and molten salt bath chemistry have been performed, and the ideal feeds and conditions for extraction and diffusion to produce master alloys were established. Small-scale and scaled experimentation was performed to validate and optimize the feasibility of viable reactions, temperatures, and process conditions with regards to yield, composition, and process efficiency. Finally, full-scale experiments for the production of NdFeB and SmCo magnets were performed, and their performance characteristics were established.

36 MATERIALS SCIENCE↗
DOE OSTI2025

28nm front end ASIC and 12” LGADs for 3D integration

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (≈10 μm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present the design and results from a 28 nm CMOS ASIC prototype, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. We also report on the co-design and characterization of reticle-scale LGAD sensors with 50 μm and 100 μm pixels and introduce the next 10k-pixel ASIC designed for full 3D integration. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗
DOE OSTI2026

Spin Diagnostics Using Mott Polarimeter for the EIC Pre-Injector

Polarization measurement and front-end commissioning are critical steps in the EIC preinjector program. We designed a spin-diagnostics beamline at around 4 MeV for front-end commissioning. This paper describes a high-energy Mott polarimeter, including Mott scattering, the Sherman function, and extrapolation toward single elastic scattering in finite-thickness foils. We summarize the key equations used to calculate essential design parameters, including detection time, effective Sherman function, differential cross section, and Mott polarimeter figure of merit. We also present the diagnostics beamline lattice design and kicker design for continuous spin-polarization monitoring. These notes provide concepts, formulas, and methods for diagnostic-beamline design and commissioning measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Evaluation of AI-enabled Digital Documented Safety Analysis

The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗
DOE OSTI2026

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗
DOE OSTI2025

ORNL Infrastructure: Status Update on BEA Research Reactor Cask Planning Activities Completed During FY 2025

This report documents Oak Ridge National Laboratory’s (ORNL’s) FY 2025 progress toward establishing readiness to use the Battelle Energy Alliance (BEA) Research Reactor (BRR) shipping cask in support of the Nuclear Science User Facilities (NSUF). The BRR cask will provide a new shared infrastructure capability for transporting irradiated fuels and materials between ORNL and Idaho National Laboratory (INL), thereby supporting NSUF’s mission of enabling user access to advanced nuclear research facilities. In FY 2025, ORNL advanced the regulatory, contractual, safety review, and planning activities necessary to qualify its facilities and staff for BRR cask handling. Although loaded shipments were delayed due to fabrication lead times for Orano Federal Services LLC’s internal basket hardware, the program progressed to the point where an empty cask dry run is scheduled for October 2025. This dry run represents a critical step in demonstrating ORNL’s ability to receive, handle, and return the BRR cask.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Dataset for "Enrichment of phosphates, lead, and mixed soil-organic particles in INPs at the Southern Great Plains site"

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particle concentrations were primarily carbonaceous or secondary aerosol, while dust particles were enhanced in the residual particles. Dust particles measured during residual sampling contained elevated signals for phosphate ($^{63}$PO$_2^-$ and $^{79}$PO$_3^-$) and lead ($^{208}$Pb$_2^+$). Nitrate was slightly depleted relative to ambient dust, and strong sulfate signals were not seen in the residual particles measured by the miniSPLAT. The residual and ambient particles measured by the miniSPLAT were approximately the same size, though this may be a reflection of the SPMS size-dependent transmission efficiency and not that of physical truth. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

Cornwell, Gavin C↗
DOE OSTI2026

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175–250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol–water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol–water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol–water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

biorefining↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Stability of Floodplain Subsurface Microbial Communities Through Seasonal Hydrological and Geochemical Cycles

Riparian floodplains represent an interaction zone between the terrestrial subsurface and rivers, where regional groundwater flows, infiltration, and evapotranspiration drive mixing of water and import/export of nutrients and contaminants. These dynamics create seasonally transient redox conditions that drive biogeochemical transformations, which strongly modify groundwater quality. Microbial responses to changing hydrological conditions are perhaps the critical step connecting hydrology to geochemical transformations and groundwater quality, yet are not well understood. We aimed to address this knowledge gap by monitoring seasonal transitions at the U.S. Department of Energy legacy uranium ore processing site in Riverton, WY, through spring-summer-fall hydrological transitions. Our goal was to characterize the microbial community throughout the soil profile, down to the saturated aquifer, and observe its response to wet-dry transitions across a full season and compare to changes in geochemistry and hydrology. Next-generation sequencing was employed to identify biogeochemically-relevant microbial taxa based on the 16S rRNA gene; we found a broad diversity of microbial clades including taxa involved in sulfur and metal cycling, as well as nitrification. These data were paired with measurements of soil moisture, major nutrients and cations, and trace elements. Overall microbial community composition was dependent on soil depth or type, with seasonal effects only observed in the topsoil or subsurface aquifer. This finding indicates that microbial communities in the transiently-reduced center of the soil profile at the Riverton, WY site are remarkably stable, despite moisture and redox inversions. In addition, these communities likely impact the communities in surrounding soil horizons through export of metabolites and solutes as the water table rises and falls throughout the season.

16S rRNA↗
DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗
DOE OSTI2022

Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification

Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Enzyme Complexes of Ptr4CL and PtrHCT Modulate Co-enzyme A Ligation of Hydroxycinnamic Acids for Monolignol Biosynthesis in Populus trichocarpa

Co-enzyme A (CoA) ligation of hydroxycinnamic acids by 4-coumaric acid:CoA ligase (4CL) is a critical step in the biosynthesis of monolignols. Perturbation of 4CL activity significantly impacts the lignin content of diverse plant species. In Populus trichocarpa, two well-studied xylem-specific Ptr4CLs (Ptr4CL3 and Ptr4CL5) catalyze the CoA ligation of 4-coumaric acid to 4-coumaroyl-CoA and caffeic acid to caffeoyl-CoA. Subsequently, two 4-hydroxycinnamoyl-CoA:shikimic acid hydroxycinnamoyl transferases (PtrHCT1 and PtrHCT6) mediate the conversion of 4-coumaroyl-CoA to caffeoyl-CoA. Here, we show that the CoA ligation of 4-coumaric and caffeic acids is modulated by Ptr4CL/PtrHCT protein complexes. Downregulation of PtrHCTs reduced Ptr4CL activities in the stem-differentiating xylem (SDX) of transgenic P. trichocarpa. The Ptr4CL/PtrHCT interactions were then validated in vivo using biomolecular fluorescence complementation (BiFC) and protein pull-down assays in P. trichocarpa SDX extracts. Enzyme activity assays using recombinant proteins of Ptr4CL and PtrHCT showed elevated CoA ligation activity for Ptr4CL when supplemented with PtrHCT. Numerical analyses based on an evolutionary computation of the CoA ligation activity estimated the stoichiometry of the protein complex to consist of one Ptr4CL and two PtrHCTs, which was experimentally confirmed by chemical cross-linking using SDX plant protein extracts and recombinant proteins. Based on these results, we propose that Ptr4CL/PtrHCT complexes modulate the metabolic flux of CoA ligation for monolignol biosynthesis during wood formation in P. trichocarpa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Implementation of a Practical Teaching Course on Protein Engineering

Protein Engineering is a highly evolved field of engineering aimed at developing proteins for specific industrial, medical, and research applications. Here, we present a practical teaching course to demonstrate fundamental techniques used to express, purify and analyze a recombinant protein produced in Escherichia coli—the enhanced green fluorescent protein (eGFP). The methodologies used for eGFP production were introduced sequentially over six laboratory sessions and included (i) bacterial growth, (ii) sonication (for cell lysis), (iii) affinity chromatography and dialysis (for eGFP purification), (iv) bicinchoninic acid (BCA) and fluorometry assays for total protein and eGFP quantification, respectively, and (v) sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for qualitative analysis. All groups were able to isolate the eGFP from the cell lysate with purity levels up to 72%. Additionally, a mass balance analysis performed by the students showed that eGFP yields up to 46% were achieved at the end of the purification process following the adopted procedures. A sensitivity analysis was performed to pinpoint the most critical steps of the downstream processing.

Gomes, Luciana (ORCID:0000000289921097)↗
DOE OSTI2023

Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

60 APPLIED LIFE SCIENCES↗