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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ionothermally Synthesized Nanoporous Ti 0.95 W 0.05 Nb 2 O 7 : a Novel Anode Material for High-Performance Lithium-Ion Batteries

Although TiNb 2 O 7 is regarded as a fast-rechargeable lithium-ion battery (LIB) anode material, the intrinsic poor electrochemical kinetics of TiNb 2 O 7 still dramatically impedes its development. Herein, an ionothermal synthesis-assisted doping strategy is proposed for the preparation of a new W 6+ -doped TiNb 2 O 7 material (Ti 0.95 W 0.05 Nb 2 O 7 ) with nanoporous structure (denoted as NPTWNO). The improved Li + diffusion coefficient of NPTWNO suggests that the ionic-liquid-templated nanoporous architecture improves the Li+ diffusion kinetics. The density functional theory computational study reveals that the doped W 6+ successfully boosts the electronic conductivity due to the narrowed conduction-valance bandgap resulted from charge redistribution, which is reflected by the electrochemical impedance spectroscopy data. With the simultaneously enhanced Li+ diffusivity and electronic conductivity, NPTWNO achieves fast-rechargeability in LIBs. So this work indicates the potential of ionothermal synthesis-assisted doping strategy on energy storage materials and offers NPTWNO material with promising electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion

The biogas upgrading process through bioconversion of CO 2 to CH 4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH 4 % and H 2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R 2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH 4 % and H 2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Biogas upgrading↗
DOE OSTI2021

Impact of KOH on the interfacial precipitation rates of C-S-H during the early hydration of C3S

In this work we investigated the influence of alkalis on the hydration of two triclinic samples of C{sub 3}S. One pure C{sub 3}S and one Al-doped C{sub 3}S (0.5 wt.‐% Al{sub 2}O{sub 3}). We added differently concentrated KOH solutions representing alkalis in the pore solution, which lead to an increase of the interfacial precipitation rates of C-S-H during the hydration of C{sub 3}S compared to the hydration without KOH. Isothermal calorimetry was conducted for the determination of the rates of reaction during the hydration process and ICP-OES measurements for the determination of the pore solution composition at certain times of hydration. The increasing effect of KOH solution on the calculated interfacial precipitation rates from pore solution data can explain the enhancement of the rates of reaction recorded by heat flow calorimetry during the early hydration. Therefore, the interfacial precipitation rates of C-S-H can accurately describe the acceleration effect of alkalis on the early hydration of C{sub 3}S.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Opportunities and challenges in thermochemical conversion of municipal solid waste: A comprehensive review

Recent advancements in thermochemical conversion processes have elucidated new pathways for converting municipal solid waste into valuable resources. This review explores the primary thermochemical conversion methods, including combustion, gasification, pyrolysis, torrefaction, hydrothermal carbonization, and hydrothermal liquefaction, emphasizing their potential roles in waste management and energy recovery. Key challenges including feedstock variability, ash behavior, and scale-up limitations are discussed alongside opportunities for hybrid systems and circular economy integration. A comparative analysis of research publications indicates a significant focus on thermochemical pathways within the broader context of municipal solid waste research, underscoring the growing interest in these technologies. Recent advancements in each thermochemical process, alongside their operational, technical, and economic challenges, are discussed. Comparative data reveal that torrefaction enhances the hydrophobicity and grindability of municipal solid waste components, though its energy densification benefits are more modest than those observed in biomass. Hydrothermal carbonization and liquefaction are highlighted for their ability to process high-moisture and heterogeneous waste streams. The review also synthesizes recent findings on reactor configurations, emissions control, and synergistic effects in co-processing municipal solid waste fractions. The findings underscore the importance of developing standardized protocols for municipal solid waste characterization and the need for innovative hybrid systems to improve efficiency.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

Satellite-based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear-sky conditions for estimating surface urban heat island intensity (SUHII). Clear-sky SUHII may not accurately represent all-sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear-sky and all-sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear-sky SUHII is mostly higher than all-sky SUHII, particularly in summer, daytime, and precipitation-rich regions. Besides, clear-sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all-sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear-sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks

Despite advancements in Artificial Intelligence (AI) methods for climate downscaling, significant challenges remain for their practicality in climate research. Current AI-methods exhibit notable limitations, such as limited application in downscaling Global Climate Models (GCMs), and accurately representing extremes. To address these challenges, we implement an AI-based methodology using super-resolution convolutional neural networks (SRCNN), trained and evaluated on 40 years of daily precipitation data from a reanalysis and a high-resolution dynamically downscaled counterpart. The dynamical downscaled simulations, constrained using spectral nudging, enable the replication of historical events at a higher resolution. This allows the SRCNN to emulate dynamical downscaling effectively. Modifications, such as incorporating elevation data and data pre-processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. Future work will expand this methodology to downscale additional variables for future climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The critical role of soil moisture in compound hazards

Soil moisture regulates the exchange of energy, water, and carbon across land–vegetation–atmosphere interfaces. Extremes in soil moisture can amplify natural hazards through interactions with diverse Earth system processes. Despite its mechanistic importance, soil moisture remains underrepresented in hazard research and predictive frameworks. Here, in this study, we review our current understanding of the role of soil moisture in the evolution and onset of diverse compound hazards by synthesizing the latest findings from observational and modelling studies. We highlight key soil moisture mechanisms, including atmospheric feedbacks that amplify drought–heatwave–wildfire events, precipitation couplings that promote clustered storms, and threshold responses that drive vegetation die-offs, trigger landslides, and induce flooding. Persistent challenges in observational data, model representation and operational implementation have limited the integration of soil moisture into hazard early-warning systems. Addressing these gaps through advances in observations, data assimilation, and physics-based and data-driven modelling will enhance hazard prediction and preparedness in a rapidly changing world.

Li, Chuxuan [University of California, Los Angeles↗
DOE OSTI2025

Oral microbiome and mycobiome dynamics in cancer therapy-induced oral mucositis

Cancer therapy-induced oral mucositis is a frequent major oncological problem, secondary to cytotoxicity of chemo-radiation treatment. Oral mucositis commonly occurs 7–10 days after initiation of therapy; it is a dose-limiting side effect causing significant pain, eating difficulty, need for parenteral nutrition and a rise of infections. The pathobiology derives from complex interactions between the epithelial component, inflammation, and the oral microbiome. Our longitudinal study analysed the dynamics of the oral microbiome (bacteria and fungi) in nineteen patients undergoing chemo-radiation therapy for oral and oropharyngeal squamous cell carcinoma as compared to healthy volunteers. The microbiome was characterized in multiple oral sample types using rRNA and ITS sequence amplicons and followed the treatment regimens. Microbial taxonomic diversity and relative abundance may be correlated with disease state, type of treatment and responses. Identification of microbial-host interactions could lead to further therapeutic interventions of mucositis to re-establish normal flora and promote patients’ health. Data presented here could enhance, complement and diversify other studies that link microbiomes to oral disease, prophylactics, treatments, and outcome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion

Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.

Shan, Siming [Yale University, New Haven, CT (Unit↗
DOE OSTI2023

Laboratory electron screening in nuclear resonance reactions

Both nonresonant and resonance reaction data are subject to laboratory electron screening effects. For non resonant reactions, such effects are well documented and the measured cross sections can be corrected to find the unscreened ones. Frequently, the procedure and expression to calculate laboratory electron screening factors for nonresonant reactions are also applied to isolated narrow resonances, without much theoretical support or experimental evidence. Here, a simple model is applied to estimate electron screening factors, lengths, and potentials for narrow resonances. The corrections to the measured data result in an enhancement of the unscreened resonance strengths by less than 0.2%, contrary to published narrow-resonance screening correction factors, which predict a reduction of the unscreened strengths by up to 25%. Unless it can be proven otherwise, it is recommended that measured strengths of isolated narrow resonances not be corrected for laboratory electron screening. The prospects of investigating laboratory electron screening effects by measuring almost negligible differences in resonance strengths are not promising. Instead, the difference of the resonance energy for the unscreened and screened situation may be measurable. As an example, the case of the E r = 956-keV resonance in the 27 Al(p,γ) 28 Si reaction is discussed. It is also demonstrated that the claim of a previously reported detection of a resonance near 800 keV in the 176 Lu(p,n) 176 Hf reaction is incorrect.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

New methods and simulations for cosmogenic induced spallation removal in Super-Kamiokande-IV

Radioactivity induced by cosmic muon spallation is a dominant source of backgrounds for 𝒪⁡(10 MeV) neutrino interactions in water Cherenkov detectors. In particular, it is crucial to reduce backgrounds to measure the solar neutrino spectrum and find neutrino interactions from distant supernovae. In this paper we introduce new techniques to locate muon-induced hadronic showers and efficiently reject spallation backgrounds. Applying these techniques to the solar neutrino analysis with an exposure of 2790 × 22.5 kton · day increases the signal efficiency by 12.6%, approximately corresponding to an additional year of detector running. Furthermore, we present the first spallation simulation at Super-Kamiokande, where we model hadronic interactions using fluka. The agreement between the isotope yields and shower pattern in this simulation and in the data gives confidence in the accuracy of this simulation, and thus opens the door to use it to optimize muon spallation removal in new data with gadolinium-enhanced neutron capture detection.

muons↗
DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗
DOE OSTI2026

Data-Driven Voltage Regulation of Distribution Grid Using Nonlinear Autoregressive Model with Exogenous Inputs (NARX)

This article proposes data-driven control via a nonlinear autoregressive model with exogenous inputs (NARX) for real-time voltage regulation of a modified feeder using reactive power sources. Traditional voltage control strategies rely on rule-based heuristics or optimization techniques, which often require detailed system models and extensive computational resources. The NARX-based controller learns system dynamics from historical data and predicts optimal reactive power dispatch in real-time for voltage correction. The proposed approach is evaluated on a power system feeder model under varying load and network conditions. Simulation results demonstrate that the NARX-based controller achieves improved voltage regulation, offering higher adaptability to system fluctuations. This study highlights the potential of data-driven control for enhancing the reliability of power distribution networks.

Donge, Vrushabh [ORNL] (ORCID:0000000306062803)↗
DOE OSTI2025

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

building-physics/refrigeration-system

The existing refrigeration system for OpenStudio was originally designed for a prototype supermarket model. To support the development of new modular space types, it is crucial to refactor the system. This process includes refining the design by utilizing the manufacturer's existing data, along with incorporating the latest data from manufacturers, to enhance the system’s adaptability and functionality. This repository currently contains the refrigeration system database and Python-based automation for generating OpenStudio-compatible refrigeration JSON files.

Bae, Yeonjin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2023

Reaction nanoscopy of ion emission from sub-wavelength propanediol droplets

Abstract Droplets provide unique opportunities for the investigation of laser-induced surface chemistry. Chemical reactions on the surface of charged droplets are ubiquitous in nature and can provide critical insight into more efficient processes for industrial chemical production. Here, we demonstrate the application of the reaction nanoscopy technique to strong-field ionized nanodroplets of propanediol (PDO). The technique’s sensitivity to the near-field around the droplet allows for the in-situ characterization of the average droplet size and charge. The use of ultrashort laser pulses enables control of the amount of surface charge by the laser intensity. Moreover, we demonstrate the surface chemical sensitivity of reaction nanoscopy by comparing droplets of the isomers 1,2-PDO and 1,3-PDO in their ion emission and fragmentation channels. Referencing the ion yields to gas-phase data, we find an enhanced production of methyl cations from droplets of the 1,2-PDO isomer. Density functional theory simulations support that this enhancement is due to the alignment of 1,2-PDO molecules on the surface. The results pave the way towards spatio-temporal observations of charge dynamics and surface reactions on droplets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

HED Hydrodynamics in the Common Modeling Framework

Within the Common Modeling Framework (CMF), the HED Hydrodynamics Project has implemented a common methodology, or "Authority", for modeling high energy density (HED) experiments by extending development of the Eulerian Applications Project code Authority, "EAPA," to include relevant physics models, customization tools and templates, and in situ and post-processing capabilities. Additionally, several models for small-scale HED experiments were successfully added to the CMF and simulations reproduce the results of preexisting, benchmarked input decks. We expect use of the CMF to improve our effectiveness in developing predictive capabilities for experiments ranging from small-scale planar single-interface, single-shock to multi-interface, multi-shock configurations and up through multi-shell inertial confinement fusion (ICF) implosions. Additionally, this will strengthen our ability to design future targeted experiments. Using the CMF infrastructure and Authorities for HED modeling has three key impacts. First, it enables a version-controlled, pedigreed and archived base model for each experiment with tools that allow easy setup, execution and analysis of simulations to assess and/or expose sensitivities to various parameters. Second, having a common base set of inputs and physics definitions helps identify systematic differences between models in the codes and experimental data to support code validation, and build confidence in our predictive capabilities. Finally, designed to work in concert with multiple programs and projects including PEM, IC, OES, V&V and DSW, the CMF supports improved collaboration and integration through shared model definitions, data, simulations and results, enhancing the workflow required to underwrite the knowledge base and capabilities ultimately required for stockpile stewardship

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗