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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of data‐driven exponential integrators with application to modeling of delay photocurrents

Abstract Radiation‐induced photocurrent effects represent a threat to microelectronic components operating in space, manmade and terrestrial radiation environments. Analysis of these threats by circuit simulations requires accurate and computationally efficient compact models. Most existing compact models are based on closed form analytic solutions of the governing equations and require empirical assumptions and idealizations that can limit their validity. In this paper we formulate an alternative numerical, data‐driven approach that learns a compact model from data representative of the type of measurements one can obtain in an experimental facility. To develop the model we start from a generic discrete‐time dynamical system and then use physics knowledge to refine its structure. Numerical studies demonstrate the potential of the model and establish some empirical guidelines for its training.

Bochev, Pavel↗
DOE OSTI2021

Molecular structure retrieval directly from laboratory-frame photoelectron spectra in laser-induced electron diffraction

Ubiquitous to most molecular scattering methods is the challenge to retrieve bond distance and angle from the scattering signals since this requires convergence of pattern matching algorithms or fitting methods. This problem is typically exacerbated when imaging larger molecules or for dynamic systems with little a priori knowledge. Here, we employ laser-induced electron diffraction (LIED) which is a powerful means to determine the precise atomic configuration of an isolated gas-phase molecule with picometre spatial and attosecond temporal precision. We introduce a simple molecular retrieval method, which is based only on the identification of critical points in the oscillating molecular interference scattering signal that is extracted directly from the laboratory-frame photoelectron spectrum. The method is compared with a Fourier-based retrieval method, and we show that both methods correctly retrieve the asymmetrically stretched and bent field-dressed configuration of the asymmetric top molecule carbonyl sulfide (OCS), which is confirmed by our quantum-classical calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Indefinite and bidirectional near-infrared nanocrystal photoswitching

Materials whose luminescence can be switched by optical stimulation drive technologies ranging from superresolution imaging, nanophotonics, and optical data storage, to targeted pharmacology, optogenetics, and chemical reactivity. Furthermore, these photoswitchable probes, including organic fluorophores and proteins, can be prone to photodegradation and often operate in the ultraviolet or visible spectral regions. Colloidal inorganic nanoparticles can offer improved stability, but the ability to switch emission bidirectionally, particularly with near-infrared (NIR) light, has not, to our knowledge, been reported in such systems. Here, we present two-way, NIR photoswitching of avalanching nanoparticles (ANPs), showing full optical control of upconverted emission using phototriggers in the NIR-I and NIR-II spectral regions useful for subsurface imaging. Employing single-step photodarkening and photobrightening, we demonstrate indefinite photoswitching of individual nanoparticles (more than 1,000 cycles over 7 h) in ambient or aqueous conditions without measurable photodegradation. Critical steps of the photoswitching mechanism are elucidated by modelling and by measuring the photon avalanche properties of single ANPs in both bright and dark states. Unlimited, reversible photoswitching of ANPs enables indefinitely rewritable two-dimensional and three-dimensional multilevel optical patterning of ANPs, as well as optical nanoscopy with sub-Å localization superresolution that allows us to distinguish individual ANPs within tightly packed clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

BuildingQA: A Benchmark for Natural Language Question Answering over Building Knowledge Graphs

Graph-based representations of building metadata using ontologies like Brick are vital for smart building applications, but querying them remains a challenge for practitioners. Knowledge Graph Question Answering (KGQA) systems, meant to retrieve answers from natural language questions, traditionally require large-scale training data, making them ill-suited for the specialized and data-scarce building domain. The advent of Large Language Models (LLMs) offers a paradigm shift, enabling zero-shot natural language querying without building/domain-specific training. Yet, there is no standardized benchmark for building-specific KGQA which can guide and validate research in this area. To address this gap, our work makes three primary contributions. First, we introduce the BuildingQA Benchmark Dataset, constructed through a multi-stage process of collecting practitioner data, augmenting it with LLMs for linguistic diversity, and curating a final set of 188 questions across 4 buildings. Second, we characterize the benchmark's complexity and ambiguity, introducing a novel method to quantify its "lexical gap" and providing a four-stage diagnostic framework for analyzing how systems fail. Third, we benchmark zero-shot LLM-powered KGQA systems to establish baseline performance and analyze their failure modes. Our evaluation reveals that top-performing systems achieve a maximum F1 score of only 0.38. This result does not indicate a failure of these powerful systems, but rather underscores the unique challenges posed by our benchmark. It demonstrates a critical performance gap, showing that current methods successful on general KGs struggle with the specific lexical and structural nuances of the building domain. BuildingQA1 thus provides the benchmark dataset and foundational analysis needed to drive the development of novel, domain-aware methods required to unlock the use of semantic data in buildings.

Mulayim, Ozan Baris↗
DOE OSTI2020

Water Structure and Properties at Hydrophilic and Hydrophobic Surfaces

The properties of water on both molecular and macroscopic surfaces critically influence a wide range of physical behaviors, with applications spanning from membrane science to catalysis to protein engineering. Yet, our current understanding of water interfacing molecular and material surfaces is incomplete, in part because measurement of water structure and molecular-scale properties challenges even the most advanced experimental characterization techniques and computational approaches. This review highlights progress in the ongoing development of tools working to answer fundamental questions on the principles that govern the interactions between water and surfaces. One outstanding and critical question is what universal molecular signatures capture the hydrophobicity of different surfaces in an operationally meaningful way, since traditional macroscopic hydrophobicity measures like contact angles fail to capture even basic properties of molecular or extended surfaces with any heterogeneity at the nanometer length scale. Resolving this grand challenge will require close interactions between state-of-the-art experiments, simulations, and theory, spanning research groups and using agreed-upon model systems, to synthesize an integrated knowledge of solvation water structure, dynamics, and thermodynamics.

catalysis (heterogeneous)↗
DOE OSTI2023

Integrating biomechanics in evolutionary studies, with examples from the amphidromous goby model system

The functional capacities of animals are a primary factor determining survival in nature. In this context, understanding the biomechanical performance of animals can provide insight into diverse aspects of their biology, ranging from ecological distributions across habitat gradients to the evolutionary diversification of lineages. To survive and reproduce in the face of environmental pressures, animals must perform a wide range of tasks, some of which entail tradeoffs between competing demands. Moreover, the demands encountered by animals can change through ontogeny as they grow, sexually mature or migrate across environmental gradients. To understand how mechanisms that underlie functional performance contribute to survival and diversification across challenging and variable habitats, we have pursued diverse studies of the comparative biomechanics of amphidromous goby fishes across functional requirements ranging from prey capture and fast-start swimming to adhesion and waterfall climbing. The pan-tropical distribution of these fishes has provided opportunities for repeated testing of evolutionary hypotheses. By synthesizing data from the lab and field, across approaches spanning high-speed kinematics, selection trials, suction pressure recordings, mechanical property testing, muscle fiber-type measurements and physical modeling of bioinspired designs, we have clarified how multiple axes of variation in biomechanical performance associate with the ecological and evolutionary diversity of these fishes. Here our studies of how these fishes meet both common and extreme functional demands add new, complementary perspectives to frameworks developed from other systems, and illustrate how integrating knowledge of the mechanical underpinnings of diverse aspects of performance can give critical insights into ecological and evolutionary questions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gen IV Education and Training Working Group Webinars’ Initiative

Collaboration and support among national laboratories, industry, universities, and research and development organizations are vital to not only maintain a skilled and competent nuclear workforce but also to avert the risk of human resource shortages. As stated in a recent IAEA paper [1], the world nuclear electrical generating capacity is projected to increase to 554 GW(e) by 2030 and up to 874 GW(e) by 2050 (Fig. 1). This represents a 42% increase over current levels by 2030 and a doubling of the current capacity by 2050. To promote Education and Training (E&T) on Gen IV reactor systems and other nuclear related topics of interest, the Gen IV Education and Training Working Group (ETWG) identifies and advertises training courses; engages collaboration with other international education and training organizations; delivers webinars dedicated to Gen IV systems; and maintains a modern social medium platform to exchange information and ideas on Gen IV R&D topics, as well as on related GIF education and training activities. At the end of June 2020, the GIF ETWG has produced, podcasted and posted forty-one webinars covering the six Gen IV systems and various subjects addressing e.g. the economics of the nuclear fuel cycle, sustainability aspects of Gen IV systems, nuclear fuels and materials challenges, the thorium fuel cycle, energy conversion systems, and lessons learned for knowledge management and preservation. This paper describes the development of these webinars from the initial concept to its full realization, with future webinars planned in 2021, addressing topics such as materials and fuels performance for advanced reactors, small modular reactor.

Paviet, Patricia D.↗
DOE OSTI2020

Advanced Moderator Material Handbook

High hydrogen density moderators such as metal hydrides are an important research topic within the DOE NE Microreactor Research, Development, and Deployment (RD&D) Program due to their ability to retain hydrogen to much higher temperatures than other hydrogenous media. This class of moderators, which includes yttrium dihydride (YH 2 ), thermalizes neutrons in the system such that the overall fuel mass or the required uranium enrichment in the system can be significantly reduced. Knowledge of material properties, both in the as-fabricated and irradiated state, are important to understanding moderator performance during steady-state and transient reactor operation. Provided in this document is the Advanced Moderator Material Handbook, which provides a detailed summary of the literature data on yttrium dihydride, thermomechanical and other property data, and a critical evaluation of that data. This handbook also provides a description of ongoing experiments to understand in-reactor performance, such as irradiations in ATR, as well as nuclear data from an integral critical experiment at NCERC. The majority of this report focuses on measured values but also includes some modeling results for comparison where applicable.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Research Roadmap on Grid-Forming Inverters

This research roadmap is intended to fill the knowledge gap by providing a system view of grid-forming inverter-based resource controls and their impact on grid stability, which we believe is central to meeting some of the challenges to operating the future North American electric power system. This includes the roles and requirements of grid-forming inverter-based resources—including solar photovoltaics, wind generators, and energy storage. For this roadmap, we focus on a specific family of grid-forming inverter control approaches that do not rely on an external voltage source (i.e., no phase-locked loop) and that can share load without explicit communications. Although the roadmap is focused narrowly on system challenges for grid-forming controls and power system stability, including interactions with protection, we hope it serves as a foundational element for future system-of-systems roadmapping needed in a broader grid modernization effort with increasing deployments of inverter-based resources. The roadmap first introduces formal definitions for the grid stability topics and then describes the differences between grid-forming and traditional grid-following control approaches for inverter-based resources. The core of the roadmap consists of a review of current research and an outline of research needs related to five grid-forming inverter topics: frequency control, voltage control, system protection, fault ride-through and voltage recovery, and modeling and simulation. The review both delineates contemporary advances and highlights open research questions that must be addressed to enable the widespread adoption of inverter-based resources across the grid. Feedback from industry on these research questions is incorporated, including discussions during the Workshop on Grid-forming Inverters for Low-inertia Power Systems. The workshop included industry presentations and discussion of ongoing research, technology gaps, and piloting needs. This roadmap concludes by offering a multiyear perspective on the gradual field validation of grid-forming inverters. This perspective recognizes that the scale and scope of the types of power systems that inverters will be called on to provide grid-forming services will and should begin modestly. Specifically, this roadmap recognizes that inverter controls today are predominantly grid-following and that future power systems will involve a mix of inverter-based resources with both grid-following and grid-forming control capabilities. Growth over time will depend on how well grid-forming inverters perform and what advantages they bring as penetration levels (instantaneous and average) of inverter-based resources increases. This recognition, in turn, establishes a natural sequence of priorities for the research questions that must be addressed. Following this multiyear perspective, the roadmap offers short descriptions of two specific near-term research priorities: the review of regulatory and technical standards and the development of advanced modeling techniques. These priorities are foundational. We recommend immediate pursuit of them in parallel with and in direct support of the research outlined by our multiyear perspective.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Advanced Moderator Material Handbook (FY22 Rev. 2)

High hydrogen density moderators such as metal hydrides are an important research topic within the DOE NE Microreactor Research, Development, and Deployment (RD&D) Program due to their ability to retain hydrogen to much higher temperatures than other hydrogenous media. This class of moderators, which includes yttrium dihydride (YH 2 ), thermalizes neutrons in the system such that the overall fuel mass or the required uranium enrichment in the system can be significantly reduced. Knowledge of material properties, both in the as-fabricated and irradiated state, are important to understanding moderator performance during steady-state and transient reactor operation. This document provides a detailed summary of the literature data on yttrium dihydride, thermomechanical and other property data, and a critical evaluation of that data. This handbook also provides a description of ongoing experiments to understand in-reactor performance, such as irradiations in ATR, as well as nuclear data from an integral critical experiment at NCERC. This report focuses on measured quantities but also includes some modeling results for comparison where applicable.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Advanced Moderator Material Handbook (FY23 Version)

High hydrogen density moderators such as metal hydrides are an important research topic within the DOE NE Microreactor Program due to their ability to retain hydrogen to much higher temperatures than other hydrogenous media. This class of moderators, which includes yttrium dihydride (YH 2 ), thermalizes neutrons in the system such that the overall fuel mass or the required uranium enrichment in the system can be significantly reduced. Knowledge of material properties, both in the as-fabricated and irradiated state, are important to understanding moderator performance during steady-state and transient reactor operation.

08 HYDROGEN↗
DOE OSTI2025

Advanced Moderator Material Handbook (FY25)

High hydrogen density moderators such as metal hydrides are an important research topic within the DOE NE Microreactor Program due to their ability to retain hydrogen to much higher temper atures than other hydrogenous media. This class of moderators, which includes yttrium dihydride (δ-YH 2 ) and zirconium hydride (δ-ZrH 1.6 or ϵ-ZrH 1.8 ), thermalizes neutrons in the system such that the overall fuel mass or the required uranium enrichment in the system can be significantly reduced. Knowledge of material properties, both in the as-fabricated and irradiated state, are important to understanding moderator performance during steady-state and transient reactor operation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Final Report for Electro-oxidative valorization of biomass: design strategies for selective and stable catalysis

This project aimed to develop a fundamental understanding of the factors that dictate activity and selectivity during electrochemical partial oxidation of multi-carbon organic molecules derived from biomass. Value-adding selective conversions of alcohols to aldehydes, and aldehydes to carboxylic acids were considered, with the ultimate goal to establish general principles for controlling selectivity in organic oxidations. Specific aims involved the use of furfural (FF) and 5-hydoxymethyfurfural (HMF) as model systems and seek to develop knowledge for control of mechanisms that permit selective conversions between oxygenate functional groups with prevention or control of C-C cleavage steps. Another major focus of the work was in achieving these transformations in acidic conditions, which contrasts most existing work (done in base), but is crucial toward compatibility with common pretreatments used to generate small molecules from lignocellulose (e.g. acid hydrolysis or pyrolysis). The work involved well-defined catalyst material synthesis, comparative kinetics, and operando spectroscopies (PI Holewinski), supported by quantum chemical simulations (PI Janik), enabling progress toward efficient, acid-stable oxidation catalysis for biomass upgrading.

Holewinski, Adam [University of Colorado Boulder] ↗
DOE OSTI2022

Synthesis of Nano-Oxide Precipitates by Implantation of Ti, Y and O Ions in Fe-10%Cr: Towards an Understanding of Precipitation in Oxide Dispersion-Strengthened (ODS) Steels

The properties of oxide dispersion-strengthened steels are highly dependent on the nature and size distribution of their constituting nano-oxide precipitates. A fine control of the processes of synthesis would enable the optimization of pertinent properties for use in various energy systems. This control, however, requires knowledge of the precise mechanisms of nucleation and growth of the nanoprecipitates, which are still a matter of debate. In the present study, nano-oxide precipitates were produced via the implantation of Y, Ti, and O ions in two different sequential orders in an Fe-10%Cr matrix that was subsequently thermally annealed. The results show that the oxides that precipitate are not necessarily favoured thermodynamically, but rather result from complex kinetics aspects related to the interaction between the implanted elements and induced defects. When Y is implanted first, the formation of nanoprecipitates with characteristics similar to those in conventionally produced ODS steels, especially with a core/shell structure, is evidenced. In contrast, when implantation starts with Ti, the precipitation of yttria during subsequent high-temperature annealing is totally suppressed, and corundum Cr 2 O 3 precipitates instead. Moreover, the systematic involvement of {110} matrix planes in orientation relationships with the precipitates, independently of the precipitate nature, suggests matrix restriction effects on the early stages of precipitation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid ® V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

36 MATERIALS SCIENCE↗