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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Grain2mesh: A Python and cubit mesh generator from unprocessed mesoscale images

Predicting bulk behavior from microscale features constitutes a key objective in multiscale modeling research, often involving numerical models composed of finite elements that capture the diversity of constituent phases, shapes, and orientations within the material. The Grain2mesh toolbox allows the user to input unprocessed mesoscopic images for automatic segmentation, pre-processing, quality control, and numerical mesh generation. The numerical mesh generation incorporates Cubit routines to generate robust multi-phase mesh structure for use in computational mechanics solvers. The python classes developed contain detailed documentation and examples to support standard usage and case-specific alternative options.

58 GEOSCIENCES↗
DOE OSTI2025

Asymptotic coefficients of the attached-eddy model derived from an adiabatic atmosphere

The attached-eddy model (AEM) predicts that the mean streamwise velocity and streamwise velocity variance profiles follow a logarithmic shape, while the vertical velocity variance remains invariant with height in the overlap region of high Reynolds number wall-bounded turbulent flows. Moreover, the AEM coefficients are presumed to attain asymptotically constant values at very high Reynolds numbers. Here, the AEM predictions are examined using sonic anemometer measurements in the near-neutral atmospheric surface layer, with a focus on the logarithmic behaviour of the streamwise velocity variance. Utilizing an extensive 210-day dataset collected from a 62 m meteorological tower located in the Eastern Snake River Plain, Idaho, USA, the inertial sublayer is first identified by analysing the measured momentum flux and mean velocity profiles. The logarithmic behaviour of the streamwise velocity variance and the associated ‘−1’ scaling of the streamwise velocity energy spectra are then investigated. The findings indicate that the Townsend–Perry coefficient (A 1 ) is influenced by mild non-stationarity that manifests itself as a Reynolds number dependence. After excluding non-stationary runs, and requiring the bulk Reynolds number defined using the atmospheric boundary layer height to be larger than 4 × 10 7 , the inferred A 1 converges to values ranging between 1 and 1.25, consistent with laboratory experiments. Furthermore, nine benchmark cases selected through a restrictive quality control reveal a close relation between the ‘−1’ scaling in the streamwise velocity energy spectrum and the logarithmic behaviour of streamwise velocity variance. However, additional data are required to determine whether the plateau value of the pre-multiplied streamwise velocity energy spectrum is identical to A 1 .

atmospheric flows↗
DOE OSTI2026

Advancing Battery Manufacturing: Synchrotron Characterization for Industry

Large-scale battery manufacturing requires understanding the fundamental principles of materials and interfaces and relies on advanced techniques for detailed interrogation. Despite advancements in the industrial scale production and their associated quality control tools, challenges such as electrode heterogeneity, internal defects, and largescale material waste (e.g., scrap) can hamper manufacturing. Synchrotron X-ray characterization techniques offer spatial, temporal, and chemical resolution that can provide diagnostic insights for metrology across various manufacturing steps. This review examines the use of synchrotron tools to advance understanding of key steps in the battery manufacturing process. Recent examples demonstrate how synchrotron methods resolve manufacturing challenges and uncover degradation pathways that are otherwise inaccessible. Future directions for advancing battery manufacturing emphasize collaboration between academia and industry through the use of synchrotron X-ray techniques.

Batteries↗
DOE OSTI2026

Multinuclear Solid-State NMR and NMR Crystallography of Solid Forms of Creatine and Creatinine

Creatine is a performance-enhancing supplement with two widely available commercial solid forms, namely, creatine monohydrate (creatine·H 2 O) and creatine HCl, the latter of which does not have a reported crystal structure. Moreover, commercial formulations of creatine may contain creatinine, an undesired impurity phase resulting from the self-cyclization of creatine during manufacturing. Therefore, reliable methods for characterizing the different solid forms of creatine and detecting the presence of creatinine are essential. Herein, we address these challenges using 13 C, 15 N, and 35 Cl solid-state NMR (SSNMR) spectroscopy to obtain distinct spectral fingerprints for creatine·H 2 O and creatine HCl, along with creatinine and creatinine HCl. The acquisition of these SSNMR spectra offers a robust approach for both the rapid characterization of each solid form and the detection of the impurity phases. Additionally, quadrupolar NMR crystallography-guided crystal structure prediction (QNMRX-CSP) was applied for the de novo crystal structure determination of creatine HCl, which was validated by the subsequently determined single-crystal X-ray diffraction (SCXRD) structure. Finally, to investigate the relationship between NMR parameters and structural features, 13 C and 15 N chemical shifts and 35 Cl electric field gradient (EFG) tensors were computed from geometry-optimized structures of the four solid forms by using dispersion-corrected DFT-D2* methods. Finally, this integrative approach offers a powerful framework for advancing the structural understanding and quality control of creatine-based supplements and next-generation formulations, as well as a wide range of other solid pharmaceuticals and nutraceuticals.

NMR↗
DOE OSTI2025

Multiphoton and Harmonic Imaging of Microarchitected Materials

Microadditive manufacturing has revolutionized the production of complex, nano- to microscale components across various fields. This work investigates two-photon (2P) and three-photon (3P) fluorescence imaging, as well as third-harmonic generation (THG) microscopy, to examine periodic microarchitected lattice structures fabricated using multiphoton lithography (MPL). By immersing the structures in refractive index matching fluids, we demonstrate high-fidelity 3D reconstructions of both fluorescent structures using 2P and 3P microscopy as well as low-fluorescence structures using THG microscopy. These results show that multiphoton fluorescence (MPF) imaging offers reduced signal decay with respect to depth compared to single-photon techniques in the examined structures. We further demonstrate the ability to nondestructively identify intentional internal modifications of the structure that are not immediately visible with scanning electron microscope (SEM) images and compression-induced fractures, highlighting the potential of these techniques for quality control and defect detection in microadditively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning

Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R 2 of up to 0.63 and an overall R 2 of 0.92, with a mean absolute percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.

battery electrodes↗
DOE OSTI2025

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.

Chalcogenides↗
DOE OSTI2024

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Visible-to-THz near-field nanoscopy

Optical microscopy has a key role in research, development and quality control across a wide range of scientific, technological and medical fields. However, diffraction limits the spatial resolution of conventional optical instruments to about half the illumination wavelength. A technique that surpasses the diffraction limit in the wide spectral range between visible and terahertz frequencies is scattering-type scanning near-field optical microscopy (s-SNOM). The basis of s-SNOM is an atomic force microscope in which the tip is illuminated with light from the visible to the terahertz spectral range. By recording the elastically tip-scattered light while scanning the sample below the tip, s-SNOM yields near-field optical images with a remarkable resolution of 10 nm, simultaneously with the standard atomic force microscopic topography image. This resolution is independent of the illumination wavelength, rendering s-SNOM a versatile nanoimaging and nanospectroscopy technique for fundamental and applied studies of materials, structures and phenomena. This Review presents an overview of the fundamental principles governing the measurement and interpretation of near-field contrasts and discusses key applications of s-SNOM. We also showcase emerging developments that enable s-SNOM to operate under various environmental conditions, including cryogenic temperatures, electric and magnetic fields, electrical currents, strain and liquid environments. Furthermore, all these recent developments broaden the applicability of s-SNOMs for exploring fundamental solid-state and quantum phenomena, biological matter, catalytic reactions and more.

Hillenbrand, Rainer [CIC nanoGUNE BRTA and Departm↗
DOE OSTI2024

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

CHUWD-H v1.0: a comprehensive historical hourly weather database for U.S. urban energy system modeling

Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998-2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes

The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational method development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Intercomparison of sensible and latent heat flux measurements from combined eddy covariance, energy balance, and Bowen ratio methods above a grassland prairie

We present a comparison of four different methods of measuring sensible (H) and latent (LE) heat fluxes for a year over a mixed grass prairie ecosystem in the Nebraska SandHills [eddy covariance (EC), energy balance/Bowen ratio (EBBR), residual energy (RES), modified Bowen ratio (MBR) methods]. Additionally, we developed a set of quality control criteria for each method and present a simplification to the traditional EBBR setup. Using EC as reference, all methods yielded similar estimates of yearly H (regression slopes (m) ~ 2% from unity; H EC > H EBBR , H RES , and H MBR ). For yearly LE, EBBR and RES yielded similar estimates with EC (m ~ 2% from unity; LE EC < LE EBBR and LE RES ), while a larger bias was found from MBR (m ~ 8% from unity; LE EC > LE MBR ). At shorter time scales (~ hourly), moderate scatter was found about linear regression fits for H between EBBR and EC (R 2 = 0.81), with smaller scatter between RES and MBR, and EC (R 2 = 0.91). For LE, smaller scatter was also measured between EC, and EBBR and RES (R 2 = 0.89 and 0.87, respectively), with the larger scatter between EC and MBR (R 2 = 0.65). This suggests methods other than EC may be well suited to longer-term applications (≥ yearly), but have larger uncertainty on individual measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tumor-promoting UBR4 coordinates impaired mitophagy–associated senescence and lung adenocarcinoma pathogenesis

Cellular senescence, an irreversible cell cycle arrest, plays a pivotal role in development, aging, and tumor suppression. However, the fundamental pathway coordinating senescence and neoplastic transformation remains unclear. Here, we describe the tumorigenic involvement of ubiquitin protein ligase E3 component n-recognin 4 (UBR4), an E3 ubiquitin ligase of the N-degron pathway, in lung adenocarcinoma (LUAD). Public genome databases revealed high UBR4 expression in LUAD patients, associated with a dysregulated cell cycle and impaired mitochondrial homeostasis.UBR4knockout (ΔUBR4) in A549 lung cancer cells induced cellular senescence with defective mitochondria. Restoration of UBR4 or antioxidant treatment reversed the ΔUBR4 phenotypes caused by impaired mitophagy. Mitochondrial stress exacerbated mitochondrial dysfunction in ΔUBR4 cells, contributing to diverse cellular phenotypes. Additionally, ΔUBR4 cells exhibited substantially slow tumor growth in mouse xenograft models. In LUAD patients, UBR4 levels correlated with tumor stage, mitophagy markers, and poor survival. These findings suggest a tumor-promoting function of UBR4 in LUAD by regulating mitochondrial quality control. Further research into the pharmacological inhibition of UBR4 could open promising avenues for developing effective antitumor therapies targeting LUAD.

Science & Technology - Other Topics↗
DOE OSTI2025

Computational Optimization of 133m Xe Production via Neutron Irradiation in a TRIGA Reactor

Here, the Comprehensive Nuclear-Test-Ban Treaty bans all nuclear tests worldwide. As part of treaty compliance, the concentration of radioactive nuclides in the atmosphere is monitored to detect nuclear weapons tests. Radioactive noble gas fission products, specifically radioxenon, can vent into the atmosphere after a nuclear weapons test, even if the test is well contained underground or underwater. Radioxenon thus serves as a signal for nuclear weapons tests. All atmospheric monitoring systems require samples of radioxenon isotopes for detector calibration, quality control, and certification. Here, we present a novel, improved method for creating samples of 133m Xe via neutron irradiation of 132 Xe in the Washington State University TRIGA reactor. 132 Xe neutron absorption results in either 133 Xe or 133m Xe—thermal neutron absorption results in 133m Xe 12% of the time, while fast neutron absorption (above ~1 MeV) results in 133m Xe ~50% of the time. To optimize the production of 133m Xe via neutron absorption in 132 Xe in the thermal TRIGA reactor, spectral tuning using an irradiation chamber is required to maximize the fraction of fast neutrons being absorbed and minimize the number of thermal neutrons interacting with the 132 Xe. We used MCNP to tally 132 Xe absorptions with the isotopic tally function, flux tallies and neutron attenuation to estimate the number of neutrons reaching the 132 Xe through the irradiation chamber, and the adjoint importance function to improve the source strength estimate. Additionally, we performed a heat transfer analysis for safety considerations. It was determined that the use of a 96% enriched 10 B boron carbide chamber, placed next to the fuel elements in reactor position D8, increases the 133m Xe/ 133 Xe activity ratio from a baseline value of 0.3 to 1.0, a 233% increase. Additionally, it was determined that the alpha heating produced in the boron does not become an unmanageable problem in the Washington State University reactor.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2026

In-line measurements of below-the-surface food deformation during drying with an interference-based optical fiber strain sensor

Real-time measurements of food deformation are important for quality control in drying, yet they pose significant challenges. In this study, we developed an interference-based optical fiber strain sensor to enable in-line, continuous, below-the-surface strain measurements in drying of soft food samples. Compared to a strain resolution of 8 × 10 -4 at zero strain and 7.2 × 10 -3 at 0.20 strain reported in our previously published work, the present study achieves markedly improved resolutions of 1.3 × 10 -4 at zero strain and 7.8 × 10 -4 at 0.25 strain, which strains are the lower and higher boundaries of the dynamic range, respectively. This nearly order-of-magnitude improvement is attributed to the unique interference-based sensing mechanism, no need for calibration to convert optical signals to strain, and the system-design-enabled immunity to fiber-disk misalignments and light source intensity fluctuations. To demonstrate the in-line process monitoring, deformation measurements of fresh banana slices and sugar cookie doughs were carried out in a benchtop oven and an industrial-scale hot-air pilot dryer, respectively. In both dryers, strain measurements were continuously measured during the whole drying process at various depths and radii below the sample surfaces, with the strains up to 25%. Computer vision was used only in the benchtop drying to confirm the faithfulness of the fiber sensor measurements and cannot provide below-the-surface measurements. The measured spatiotemporal deformation allowed us to confirm the shell-hardening effect and to determine the speed and location of large deformation changes in the whole drying process, the latter of which is related to the sample cracking. To the best of the authors’ knowledge, this study is the first to report on an interferometry-based fiber sensor to measure food deformation. This sensor and the sensing mechanism have high potential for real-time process monitoring and control to prevent over-drying or cracking during drying processes.

47 OTHER INSTRUMENTATION↗