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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tethys Water Demand Data

U.S. water demand varies sharply by sector and region as land use, population, weather patterns, and economic activity co-evolve. High-resolution water demand data is required to capture these dynamics, support integrated energy-water-land modeling, and local-to-regional water scarcity assessments. This dataset contains gridded (1/8 degree), monthly, multi-sector water demand dataset for the contiguous United States (CONUS) covering 1980-2100 across eight future scenarios of human-Earth system change. The dataset covers irrigation, thermoelectric, municipal (public-supply and domestic), livestock, manufacturing, and mining demands, separately for withdrawals and consumption, and includes per-cell renewable vs. non-renewable water source attributions. The dataset is validated against the latest USGS 2010-2020 water-use data for the three largest water demand sectors (Domestic, Electricity, and Irrigation), with correlations ranging from 0.73-0.95 at the HUC6 scale. The two datasets largely agree on an aggregate basis with per-sector bias falling within +/-7%, but they disagree on the spatial allocation of water with individual HUC6 basins having normalized RMSE from 68-171% and median absolute percent difference from 37-86%. This dataset advances prior global products by combining state-resolved sectoral demands from GCAM-USA, future power-plant siting from the CERF model, and scenario-consistent high-resolution climate and population forcing data across the eight scenarios.

GCAM-USA↗
DOE OSTI2024

First-principles investigation of near-field energy transfer between localized quantum emitters in solids

We present a predictive and general approach to investigate near-field energy transfer processes between localized defects in semiconductors, which couples first-principles electronic structure calculations and a nonrelativistic quantum electrodynamics description of photons in the weak-coupling regime. The approach is general and can be readily applied to investigate broad classes of defects in solids. We apply our approach to investigate an exemplar point defect in an oxide, the F center in MgO, and we show that the energy transfer from a magnetic source, e.g., a rare-earth impurity, to the vacancy can lead to spin nonconserving long-lived excitations that are dominant processes in the near field, at distances relevant to the design of photonic devices and ultrahigh dense memories. We also define a descriptor for coherent energy transfer to predict geometrical configurations of emitters to enable long-lived excitations, that are useful to design optical memories in semiconductor and insulators. Published by the American Physical Society 2024

Chattaraj, Swarnabha (ORCID:0000000329333581)↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗
DOE OSTI2025

Unraveling Exciton Trap Dynamics and Nonradiative Loss Pathways in Quantum Dots via Atomistic Simulations

Surface defects in colloidal quantum dots are a major source of nonradiative losses, yet the microscopic mechanisms underlying exciton trapping and recombination remain elusive. Here, we develop a model Hamiltonian based on atomistic electronic calculations to investigate exciton dynamics in CdSe/CdS core/shell QDs containing a single hole trap introduced by an unpassivated sulfur atom. By systematically varying the defect depth and reorganization energy, we uncover how defect-induced excitonic states mediate energy relaxation pathways. Our simulations reveal that a single localized defect can induce a rich spectrum of excitonic states, leading to multiple dynamical regimes, from slow, energetically off-resonant trapping to fast, cascaded relaxation through in-gap defect states. Crucially, we quantify how defect-induced polaron shifts and exciton-phonon couplings govern the balance between efficient radiative emission and rapid nonradiative decay. These insights clarify the microscopic origin of defect-assisted loss channels and suggest pathways for tailoring QD optoelectronic properties via surface and defect engineering.

Defects↗
DOE OSTI2026

Data for "Plasmon-driven exciton formation in a non-equilibrium Fermi liquid"

This repository contains source data for key plots presented in the manuscript "Plasmon-driven exciton formation in a non-equilibrium Fermi liquid." Experimental data that was analyzed in Igor Pro 8 are presented as the .pxp files used to generate individual sub-plots. Electronic spectral function calculations are provided as .txt files, in which consecutive rows refer to the meshgrid x coordinate, y coordinate, spectral function (and, where relevant, axis-projected local angular momentum). We additionally include the Wannier model and DFT-obtained bulk band structure on which the Wannier model was based. Files are named as the number of the figure in the manuscript to which they correspond, with additional details included where necessary. Details of file names: 2a_DOS_Lxz_Ek_KGM_40layer_xnum_800kpt_tot.txt: Density of states, xz-axis projected local orbital angular momentum, for 800 points along the K-Gamma-M path, for a 40-layer model. 2c_composite_y.pxp: ARPES (angle-resolved photoemission spectroscopy) spectra along the ky axis, including both a scan near the Fermi level and a scan at high kinetic energies. 2d_LCP_RCP_diff_Sect_20K.pxp: difference between ARPES constant energy cuts at T=20 K at E0 + 0.23 eV taken with left- and right-circularly polarized photons. The polarization-integrated intensity at the constant energy cut is also included. 2e_DOS_L45_E11pt79_m0pt25to0pt25_xnum_800kpt_tot.txt: Density of states, xz-projected local orbital angular momentum, and corresponding k-points in two dimensions from ab-initio electronic structure calculations for a constant-energy cut. 3a_[x]_[y]ps: ARPES cut under excitation at a fluence of x uJ/cm2, measured y ps after photoexcitation. Measurements were performed at 9 K. 3b_[x]: Energy distribution curves under excitation at a fluence x uJ/cm2 at selected delay times after photoexcitation. 4a_ImSigma_vs_temperature.pxp: Imaginary self energy (extracted from ARPES linewidths) at different energies above E0 for selected lattice temperatures. 4b_EELS_lowE.pxp: Electron energy loss spectrum over a low energy range 5b_diff_55m15.pxp: Difference between momentum-integrated Tr-ARPES traces at 55 uJ/cm2 and 15 uJ/cm2 photoexcitation. Time-dependent intensity at each energy level has been normalized to a maximum of 1 for each individual fluence prior to subtraction. 5d_invtau_at_EX_vs_fluence.pxp: decay rate at a specified energy EX for different excitation fluences, from single exponential fits. NOTE: Analyses based on the Wannier model presented here should cite both the associated Article and this dataset. For all other files in the repository, citing the dataset alone is sufficient.

Acharya, Rishi [University of Illinois] (ORCID:000↗
DOE OSTI2026

Observational Evidence for Wind‐Driven Low‐Pass Filtering of Infrasound at Short Range

Infrasound from controlled explosions provides a unique opportunity to isolate atmospheric effects on propagation. We report observations from two campaigns in May and October 2024, each featuring 10‐ton TNT‐equivalent controlled surface chemical explosions recorded by a dense network of 31 single‐sensor stations within 23 km. Despite identical sources, the observed wavefields were very different. October signals followed a near‐unimodal period–distance trend, whereas May signals exhibited a pronounced azimuthal bifurcation in both period and celerity. Downwind paths largely preserved the short‐period baseline observed in October, while upwind paths showed systematically longer periods caused by wind‐driven low‐pass filtering. This study provides the first direct observational evidence that tropospheric winds can impose azimuth‐dependent low‐pass filtering at local ranges, without the influence of measured temperature inversions. Thus, the structure of the atmosphere can modify the spectral characteristics of low‐frequency acoustic waves even at a distance of only a few kilometers.

Geosciences↗
DOE OSTI2025

Shoreline wave breaking strongly enhances the coastal sea spray aerosol population: Climate and air quality implications

Sea spray aerosol (SSA) emission is a major source of atmospheric aerosols, influencing global climate and coastal air quality. Much of our knowledge about SSA derives from coastal observations near shorelines, but whether and when these observations accurately represent open oceans remain unclear. Here, we show that strong nearshore SSA production during high-wave periods greatly enhances downwind cloud condensation nuclei (CCN) and aerosol mass concentrations. Strong shoreline wave breaking is widespread globally, and swell waves, which are decoupled from local winds, play a dominant role in many coastal regions. Therefore, extrapolating results based on coastal measurements to open oceans may significantly overestimate SSA concentration and its contribution to CCN and, by extension, the impact of SSA on clouds and climate. Additionally, the strong enhancement of coastal aerosol population by shoreline wave breaking and its environmental impact on coastal communities cannot be captured by current regional models, which do not parameterize nearshore SSA generation using wave energy or completely neglect it.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Strong Correlation DMRG and DFT

This project developed new ways to improve computer simulations of materials where electrons interact strongly with each other, a challenge for today’s most widely used method, density functional theory (DFT). We used an exact numerical method, the density matrix renormalization group (DMRG), to create highly accurate reference results for simple model systems, and used these to test DFT, prove when it will converge, and even train machine-learned functionals. We also invented new kinds of localized basis functions (“gausslets” and “multi-sliced gausslets”) and a “sliced-basis” approach that make high-accuracy simulations faster and more practical. These methods were applied to extended hydrogen systems, enabling the direct derivation of accurate low-energy models from first-principles calculations. We also introduced a new formalism, Conditional-Probability DFT, which could bypass traditional approximations. The tools and results from this work, including open-source software releases, will help scientists design and understand complex quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Biomolecular Films for Direct Air Capture of CO 2

Efficient utilization of CO 2 is amongst the most critical cost drivers in algal biomass production in open pond systems. CO 2 delivery costs represent approximately 20% of the final biomass selling price in algal mass cultivation systems. Technologies that enable direct air capture (DAC) of atmospheric CO 2 to decouple algae cultivation from CO 2 point sources thus present an opportunity to improve the economics and resource potential of algal biomass. Current DAC technologies typically employ amine- or caustic-based absorption, demanding significant water and/or energy inputs and incurring substantial capital expenditures. Conversely, bio-based approaches to DAC offer a means to bypass conventional technoeconomic and sustainability hurdles. We integrate recent advances in computational metabolic modeling, algal genetic engineering, algal cultivation, and algal biomass upgrading to enable directed localization and self-assembly of carbonic anhydrase molecular films to gas-liquid interfaces for enhanced CO 2 capture and conversion.

09 BIOMASS FUELS↗
DOE OSTI2025

Dichotomous Temperature Response in the Electronic Structure of Epitaxially Grown Altermagnet MnTe

The altermagnet candidate MnTe has recently gained significant interest due to its unconventional magnetic ordering. One of the key features of altermagnetism is the momentum-dependent spin-split band and its temperaturedependent evolution. Yet a fully momentum-resolved experimental investigation, including out-of-plane direction, is still lacking. Here, we systematically investigate the electronic structure of epitaxially grown MnTe by using angle-resolved photoemission spectroscopy (ARPES). Our photon-energy-dependent ARPES data reveal significant out-of-plane dispersions consistent with previous theoretical calculations. More interestingly, we identify two distinct temperature-dependent electronic band structure evolutions at different out-of-plane momentum positions: momentum-dependent energy shifts at the nodal plane and substantial spectral weight suppression at the off-nodal plane. These findings may suggest the importance of considering both the itinerant and localized nature of the magnetic ordering and momentum-dependent interactions. Our work provides crucial insights into the complex correlation between momentum, temperature, and electronic structure in MnTe, contributing to a deeper understanding of altermagnetism.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Revealing the electronic structure of van der Waals antiferromagnetic NiPS 3 through synchrotron-based 𝜇-ARPES and alkali metal dosing

Antiferromagnetic NiPS 3 has recently emerged as a quantum material of considerable interest, thanks to the discovery of multiple new couplings involving electrons, spins, orbitals, phonons, and magnons. However, controversies and open questions persist concerning the fundamental origins of these couplings. A critical piece of information required to advance the understanding is the precise electronic band structure of NiPS 3 . Angle-resolved photoemission spectroscopy (ARPES), combined with alkali metal dosing (AMD), can enable us to directly observe the subtle electronic states that appear around the Fermi surface, offering valuable insights into the intriguing quantum properties and interplays of the examined material. Here, in this study, we present a comprehensive characterization and analysis of the band structure of van der Waals layered antiferromagnet NiPS 3 , leveraging state-of-the-art μ-ARPES measurements supported by density functional theory (DFT) calculations. Theoretical DFT results identify the orbital contributions to the observed bands, providing a precise understanding of the experimental ARPES data. Crucially, AMD enables the observation of conduction band and defect-related states above the valence band maximum in NiPS 3 . Furthermore, temperature dependent ARPES results across the Néel transition temperature of NiPS 3 reveal that the paramagnetic and antiferromagnetic phases have nearly identical band structures, underlining the highly localized character of Ni d states. These findings substantially deepen our understanding of the electronic properties of NiPS 3 and lay a vital foundation for exploring the intriguing quantum phenomena it exhibits.

Cao, Yifeng [Boston Univ., MA (United States); Law↗
DOE OSTI2025

Bacteria export alarmone synthetases that produce (p)ppApp and (p)ppGpp

Guanosine penta- and tetraphosphate [(p)ppGpp] and their adenosine analogs [(p)ppApp] are bacterial second messengers known as alarmones. Members of the RelA-SpoT homolog (RSH) family synthesize (p)ppGpp to mediate the stringent response during nutrient starvation, whereas (p)ppApp synthetases have been identified as bactericidal toxins in diverse contexts including type VI secretion systems, toxin-antitoxin modules, and phages. Although alarmone synthesis has traditionally been viewed as a cytoplasmic process, early studies in Streptomyces suggested the existence of secreted alarmone synthetases. Here, we identify SaEAS, an exported alarmone synthetase (EAS) from Streptomyces albidoflavus, as the long-mysterious source of extracellular alarmone synthetase activity in Streptomyces. SaEAS produces both (p)ppGpp and (p)ppApp at rates exceeding 100,000 molecules per minute and has kinetic properties adapted to low substrate environments. A broader bioinformatic survey reveals ~600 EASs linked to a range of specialized bacterial secretion systems. Characterization of two additional EASs, VpEAS from Vibrio parahaemolyticus and AaEAS from Amycolatopsis azurea, shows that both produce (p)ppGpp exclusively and inhibit bacterial growth when localized to the cytoplasm. These findings challenge the longstanding view of (p)ppGpp as strictly pro-survival and unveil a diverse family of secreted RSH enzymes with potential roles in interbacterial antagonism and environmental signaling.

Ahmad, Shehryar↗
DOE OSTI2025

Electricity Baseline 2021 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗
DOE OSTI2025

Electricity Baseline 2020 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗
DOE OSTI2025

Optimizing Geospatial Assessments for Nuclear Safeguards Applications with Large Language Models

A multidisciplinary team at Argonne National Laboratory evaluated the ability of large language models (LLMs) to identify geographic locations from open-source text and assessed post-processing measures to strengthen the reliability of those extractions in support of international nuclear safeguards. The study focused on addressing challenges such as toponym ambiguity, imprecise descriptions, and misinformation, which often undermine the accuracy of LLM-derived geospatial assessments. By integrating authoritative geospatial datasets, employing rigorous validation techniques, and leveraging human-in-the-loop processes, the project aimed to enhance the precision, transparency, and reproducibility of geospatial localization workflows. The findings demonstrate that while LLMs exhibit significant potential for accelerating geospatial analysis, their outputs require systematic grounding and verification to ensure reliability in high-stakes applications. This work contributes to the broader field of geospatial intelligence and supports strategic objectives of international organizations such as the International Atomic Energy Agency (IAEA) and the U.S. Department of Energy (DOE).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Benchmarking a Defeatured Geant4 NIF Model with HTOAD and HNED Neutron Data

This study benchmarks a simplified Geant4 NIF Target Chamber (TC) model against foil measurements in two instruments: the HTOAD and HNED Snout. The number of product atoms per source neutron per gram ( N 0 /n/g ) is compared across multiple locations and material configurations. The model reproduces high energy threshold reactions dominated by 14 MeV neutrons to within a few percent, depending on location. Discrepancies occur where scattering and moderation are significant for low energy threshold reactions. The defeatured Geant4 model achieves comparable statistical precision with ∼400 times less CPU time than a full fidelity TC model in MCNP. The results can be produced locally on a laptop without the need for high performance computing. Low energy biasing can be improved by preserving room return pathways and implementing IRDFF cross section evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM)

Abstract. Formaldehyde (HCHO) is an important air pollutant with direct cancer risk and ozone-forming potential. HCHO sources are complex because HCHO is both directly emitted and produced from oxidation of most gas-phase reactive organic carbon. We update the secondary production of HCHO in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) in the Community Multiscale Air Quality (CMAQ) model. Production of HCHO from isoprene and monoterpenes is increased, correcting an underestimate in the current version. Simulated June–August surface HCHO during peak photochemical production (11:00–15:00 LT, local time) increased by 0.6 ppb (32 %) over the southeastern USA and by 0.2 ppb (13 %) over the contiguous USA. The increased HCHO compares more favorably with satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI) and from aircraft-based observations. Evaluation against hourly surface observations indicates a missing nighttime sink that can be improved by increased nighttime deposition, which reduces June–August nocturnal (20:00–04:00 LT) surface HCHO by 1.1 ppb (36 %) over the southeastern USA and 0.5 ppb (29 %) over the contiguous USA. The ability of CRACMM to capture peak levels of HCHO at midday is improved, particularly at sites in the northeastern USA, while peak levels at sites in the southeastern USA are improved, although still lower than observed. Using established risk assessment methods, lifetime exposure of the population in the contiguous USA (∼ 320 million) to ambient HCHO levels predicted here may result in 6200 lifetime cancer cases, with 40 % from controllable anthropogenic emissions of nitrogen oxides and reactive organic compounds. Chemistry updates will be available in CRACMM version 2 (CRACMM2) in CMAQv5.5.

Skipper, T. Nash↗
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗