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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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892 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2024

Surface science insight note: Optimizing XPS instrument performance for quantification of spectra

X-ray photoelectron spectroscopy (XPS) provides quantitative information from photoemission peaks and shapes observed within the background due to the inelastic scattering of photoelectrons. To quantify the signal, both photoemission peaks and background in spectra must be adjusted for instrumental transmission variations that are a consequence of changes in efficiency when recording electrons with different kinetic energy. While it is generally assumed that correcting spectroscopic data for transmission is a necessary part of quantification by XPS, there are consequences for the quantification of spectra measured using an instrument for which transmission has significant curvature. In this Insight, the implications of curvature in transmission characteristics are discussed and a method based on XPS microscopy is proposed that ensures the transmission response of an instrument is free from significant curvature. An example of an instrument for which a flat transmission response is presented is achieved through collecting spectra using lens modes designed to measure stigmatic images.

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

Effects of high-dose neutron irradiation at light-water reactor relevant temperature on the mechanical properties of SiC/SiC composites

For this study, the neutron dose-dependent evolutions and the underlying mechanisms of properties of SiC fiber-reinforced SiC matrix (SiC/SiC) composites at a temperature relevant to light-water reactors (∼600 K) were investigated and analyzed. Chemical vapor infiltrated (CVI) SiC/SiC composites reinforced with Hi-Nicalon Type S or Tyranno SA3 fiber were neutron-irradiated to doses up to 30 dpa. The irradiated composites retained their flexural strengths. The thermal diffusivity and dimensional changes were mostly retained from 2.0 to 30.2 dpa. Discrepancies in irradiation responses among CVI SiC/SiC composites from different sources were found. Additional microstructural analysis using Raman spectroscopy and numerical analysis on irradiation effect on residual stress were used to explain how the microstructural variables, especially of carbon interphases, affect the mechanical properties in the 30–40 dpa dose range.

Continuous fiber-reinforced ceramic matrix composi↗
DOE OSTI2024

Chiral kinematic theory and converse vortical effects

Response theories in condensed matter typically describe the response of an electron fluid to external electromagnetic fields, while perturbations on neutral particles are often designed to mimic such fields. Here, we study the response of fermions to a space-time-dependent velocity field, thereby sidestepping the issue of gauge charge. First, we use a semiclassical chiral kinematic theory to obtain the local density of current and extract the orbital magnetization. The theory immediately predicts a "converse vortical effect," defined as an orbital magnetization driven by linear velocity. It receives contributions from magnetic moments on the Fermi surface and the Berry curvature of the occupied bands. Then, transcending semiclassics via a complementary Kubo formalism reveals that the uniform limit of a clean system receives only the Berry curvature contribution while other limits sense the Fermi surface magnetic moments too. We propose CoSi as a candidate material and suggest magnetometry of a sample under a thermal gradient to detect the effect. Overall, our study sheds light on the effects of a space-time-dependent velocity field on electron fluids and paves the way for exploring quantum materials using new probes and perturbations.

Chen, Kai [University of Houston]↗
DOE OSTI2025

Entanglement Structure of Non-Gaussian States and How to Measure It

Rapidly growing capabilities of quantum simulators to probe quantum many-body phenomena require new methods to characterize increasingly complex states. Here, we present a protocol that constrains quantum states using experimentally measured correlation functions. This method enables measurement of a quantum state’s entanglement structure, opening a new route to study entanglement-related phenomena. Our approach extends Gaussian state parameterizations by systematically incorporating higher-order correlations. We show the protocol’s usefulness in conjunction with current and forthcoming experimental capabilities, focusing on weakly interacting fermions as a proof of concept. Here, the lowest nontrivial expansion quantitatively predicts early time thermalization dynamics, including signaling the onset of quantum chaos indicated by the entanglement Hamiltonian.

Fermi gases↗
DOE OSTI2023

Shear and combustion characterization of printable ammonium perchlorate composite propellant formulations

Seven formulations of ammonium perchlorate composite propellant (APCP) are developed and their properties relevant for successful additive manufacture are characterized. Extrusion in a custom-built 3D printing system and spindle viscometry are used to collect viscosity measurements of curing primary and non-curing secondary versions of the formulations. The formulations that behave similarly to a Bingham plastic, with apparent viscosities between 4 and 8 thousand Pa*s at 30 minutes post-mix, are determined to be most suitable for printing applications. Measurements of one material show a yield stress of 1 kPa. Ambient pressure burn rates of 2.0–2.9 mm/s were measured for the compositions tested. All measured burn rates were comparable to the lower end of typical burn rates for APCP. In conclusion, the results of the characterization demonstrate a propellant suitable for use in a 3D printing system.

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

All-Polyester Multilayer Plastics (‘All-Polyester MLPs’): A Redesign for Inherently Recyclable Plastics

Multilayer plastics (MLPs) are widely used in modern packaging because they combine multiple functions such as oxygen and moisture barriers, mechanical strength, puncture resistance, and heat sealability into lightweight and cost-effective packaging solutions. These attributes are essential for food, beverage, pharmaceutical, and consumer goods packaging. However, conventional commercial MLPs typically consist of five to twelve layers made from chemically incompatible materials, including polyolefins, polyethylene terephthalate, nylon, ethylene-vinyl alcohol (EVOH), adhesives, and tie layers. This complexity makes MLPs extremely difficult to recycle. As a result, the vast majority of multilayer plastics (MLPs) are disposed of through landfilling or incineration. This disposal pathway perpetuates demand for virgin material production, thereby driving high industrial energy consumption, increasing greenhouse gas (GHG) emissions, and contributing to the long-term accumulation of plastic waste. The scope of work included: (1) Design and fabrication of all-polyester multilayer structures using commercially relevant processing methods; (2) Experimental validation of barrier, mechanical, and sealing performance; (3) Demonstration of both mechanical and chemical recycling pathways; and (4) Comprehensive techno-economic analysis (TEA) and life-cycle assessment (LCA) to quantify cost, energy, and environmental impacts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development and Validation of Low-Cost, High-Reflectance Composite CSP Facets: SIPS Final Report

This work investigates the various challenges associated with developing heliostat structural composite facets using 1 mm glass mirrors. Such facets are desirable for Concentrating Solar Power because 1 mm glass mirrors provide an absolute increase in reflectivity of 2-3 % over the industry standard of 4 mm glass mirrors. Prototypes of paraboloid composite facets with 1 mm glass mirrors were constructed that have a root mean square slope error on the order of 2 mrad while achieving greater than 96% reflectivity. These facets were constructed using a low-quality aluminum mold and a bill of materials that show potential to achieve cost parity with existing 4 mm glass mirrors supported by structural steel. The facets produced were able to survive up to 50 mm hail ball impacts and were robust against accelerated environmental cycling designed to expose durability concerns. Further work is needed to develop a scalable and cost-effective manufacturing process with a similar bill of materials.

14 SOLAR ENERGY↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
DOE OSTI2026

Bacterial Bioleaching and Biorecovery for Biomining Unconventional Rare Earth Element Feedstocks

Bacterial metabolic interactions with rare earth elements (REEs) can be harnessed for biomining unconventional feedstocks like abandoned coal-mine drainage (AMD). Pennsylvania has ~500 AMD passive remediation systems that can precipitate REE rich solids. REEs include yttrium and the lanthanide series that are used in modern energy and technology. Bacteria that metabolically interact with REEs can be used for biomining in an affordable efficient process that does not require hazardous chemical additives. Currently, the microbial metal mechanisms that contribute to REE biorelease and biorecovery are poorly understood. Our work shows acidogenic bacterial isolates (Bacillus mycoides JR07 and Bacillus pseudomycoides KB7) successfully bioleach a mixed REE solution from AMD solids by their organic acid production and biofilm formation. Further, our work shows the potential for bacterial lanthanide-dependent enzymes to recover lanthanides from a mixed REE solution; here we have bacterial isolate Methylobacterium sp. B3 that can recover soluble lanthanum. Whole genome sequencing of Methylobacterium sp. B3 predict lanthanide-dependent methanol dehydrogenase XoxF. Understanding the microbial metabolism and genes involved in the REE release and recovery is crucial to optimize the biomining of AMD solids. Our work addresses the growing need to develop novel REE mining methods from unconventional feedstocks.

biogeochemistry↗
DOE OSTI2027

From zonal to nodal capacity expansion planning: Spatial aggregation impacts on a realistic test-case

Solving power system capacity expansion planning (CEP) problems at realistic spatial resolutions is computationally challenging. Thus, a common practice is to solve CEP over zonal models with low spatial resolution rather than over full-scale nodal power networks. Due to improvements in solving large-scale stochastic mixed integer programs, these computational limitations are becoming less relevant, and the assumption that zonal models are realistic and useful approximations of nodal CEP is worth revisiting. Here, this work is the first to conduct a systematic computational study on the assumption that spatial aggregation can reasonably be used for ISO-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8000 buses, we find that well-designed small spatial aggregations can yield good approximations but that coarser zonal models may result in large distortions of investment decisions, e.g., capacity under-investment of up to 41% for the lowest resolution model considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Cr plasma-material-interaction in PISCES-RF: D thermal release, retention, and erosion

Pure chromium (Cr) targets were exposed to high-flux deuterium (D) plasmas in the Pisces-RF linear plasma device, and measurements of D retention, release, and erosion were subsequently performed. Post-exposure D retention was quantified using temperature-programmed desorption on Cr targets irradiated by 50 eV ions over a broad range of exposure temperatures (423–873 K) and ion fluences (3 × 10 24 – 3 × 10 26 m −2 ). The retained D inventory was observed to decrease rapidly with increased exposure temperature, from approximately ∼7 × 10 20 m −2 at ∼ 420 K, to then saturate near ∼ 10 20 m −2 for exposure temperatures above ∼550 K. Separately, at fixed exposure temperature (∼450 K), D retention was found to have only a weak dependence on increasing ion fluence. Lastly, the erosion of Cr in D plasma was investigated for ion impact energies in the range 40 ≤ E i ≤ 250 eV. Erosion was inferred using optical emission spectroscopy (OES) from the ratio of emission lines (Cr I (425.4 nm)/D I (656.1 nm)) measured close to the target. Conversion of the OES yield data to net erosion yield was made with singular ion energy target mass-loss measurements. These net erosion yield data were then further corrected to obtain gross erosion yield by accounting for a re-deposition factor, computed using a simple model. The gross erosion yield is found to be 2–4 times lower than predicted by SDTrimSP, consistent with that typically observed for light-ion sputtering under high-flux plasma conditions.

Chromium↗
DOE OSTI2024

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Environmental Sciences & Ecology↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2024

Compressed baryon acoustic oscillation analysis is robust to modified-gravity models

Abstract We study the robustness of the baryon acoustic oscillation (BAO) analysis to the underlying cosmological model. We focus on testing the standard BAO analysis that relies on the use of a template. These templates are constructed assuming a fixed fiducial cosmological model and used to extract the location of the acoustic peaks. Such “compressed analysis” had been shown to be unbiased when applied to the ΛCDM model and some of its extensions. However, it has not been known whether this type of analysis introduces biases in a wider range of cosmological models where the template may not fully capture relevant features in the BAO signal. In this study, we apply the compressed analysis to noiseless mock power spectra that are based on Horndeski models, a broad class of modified-gravity theories specified with eight additional free parameters. We study the precision and accuracy of the BAO peak-location extraction assuming DESI, DESI II, and MegaMapper survey specifications. We find that the bias in the extracted peak locations is negligible; for example, it is less than 10% of the statistical error for even the proposed future MegaMapper survey. Our findings indicate that the compressed BAO analysis is remarkably robust to the underlying cosmological model.

Astronomy & Astrophysics↗