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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Comparative Analysis of Infrastructure-Based Perception Sensors for Intelligent Transportation Systems

The rise of privatized and public investment in smart city infrastructure and intelligent transportation systems has generated a heightened demand for perception sensors that effectively track and detect objects while being reliable in diverse weather and lighting conditions. This growing demand for perception sensors has accelerated their development and enhanced their capabilities. With these new capabilities, it is challenging to determine the most suitable sensing unit to use in each situation. Therefore, it is essential to have a comprehensive understanding of the benefits and limitations of each sensing unit to effectively leverage their capabilities. The purpose of this paper is to provide a detailed evaluation of various perception sensors. Additionally, this paper will demonstrate the benefits of combining multiple perception sensors, which complement each other by addressing data gaps inherent to single-sensor systems, to facilitate the creation of a digital twin that models the real world. The Infrastructure, Perception, and Control (IPC) team will conduct data analysis using data collected through field testing at traffic intersections in Colorado Springs, Colorado, to make comparisons between sensors. This research aims to provide clear and concise information about modern perception systems, which will support the development of intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Integrative analysis of the 3D genome and epigenome in mouse embryonic tissues

While a rich set of putative cis-regulatory sequences involved in mouse fetal development have been annotated recently on the basis of chromatin accessibility and histone modification patterns, delineating their role in developmentally regulated gene expression continues to be challenging. To fill this gap, here we mapped chromatin contacts between gene promoters and distal sequences across the genome in seven mouse fetal tissues and across six developmental stages of the forebrain. We identified 248,620 long-range chromatin interactions centered at 14,138 protein-coding genes and characterized their tissue-to-tissue variations and developmental dynamics. Integrative analysis of the interactome with previous epigenome and transcriptome datasets from the same tissues revealed a strong correlation between the chromatin contacts and chromatin state at distal enhancers, as well as gene expression patterns at predicted target genes. We predicted target genes of 15,098 candidate enhancers and used them to annotate target genes of homologous candidate enhancers in the human genome that harbor risk variants of human diseases. We present evidence that schizophrenia and other adult disease risk variants are frequently found in fetal enhancers, providing support for the hypothesis of fetal origins of adult diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Innovative Method for Reliable Measurement of PEM Water Electrolyzer Component Resistances

Understanding the sheet resistance of porous electrodes is essential for improving the performance of polymer electrolyte membrane (PEM) water electrolyzers and related technologies. Despite its importance, existing methods often fail to provide reliable and comprehensive data, especially for porous materials with complex morphologies and non‐uniform thicknesses. This study introduces a robust and straightforward method for determining the sheet resistance of porous electrodes using a novel probe concept based on industrial printed circuit board (PCB) technology. This probe measures resistance across ten distances, ranging from 250 µm to 2500 µm, enabling local mapping of resistance. The study focuses on the sheet resistance of key components in PEM water electrolyzers, including the gas diffusion layer (GDL), porous transport layer (PTL), and catalyst layers deposited on a membrane. Additionally, an image‐processing‐based method is presented to obtain the thickness distribution of the studied catalyst layers, facilitating a detailed analysis of the electrical in‐plane resistivity with thickness variations. Overall, this methodology has the potential to expedite material integration and bridge the gap between electrode engineering and single‐cell testing, thereby advancing the development of PEM water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Phonons in Copper Diphosphide (CuP 2 ): Raman Spectroscopy and Lattice Dynamics Calculations

Copper diphosphide (CuP 2 ) is an emerging binary semiconductor with promising properties for energy conversion and storage applications. While functionality and possible applications of CuP 2 have been studied, there is a curious gap in the investigation of its vibrational properties. In this work, we provide a reference Raman spectrum of CuP 2 , with a complete analysis of all Raman active modes from both experimental and theoretical perspectives. Raman measurements have been performed on polycrystalline CuP 2 thin films with close to stoichiometric composition. Detailed deconvolution of the Raman spectrum with Lorentzian curves has allowed identification of all theoretically predicted Raman active modes (9A g and 9B g ), including their positions and symmetry assignment. Furthermore, calculations of the phonon density of states (PDOS), as well as the phonon dispersions, provide a microscopic understanding of the experimentally observed phonon lines, in addition to the assignment to the specific lattice eigenmodes. We further provide the theoretically predicted positions of the infrared (IR) active modes, along with the simulated IR spectrum from density functional theory (DFT). Overall good agreement is found between the experimental and DFT-calculated Raman spectra of CuP 2 , providing a reference platform for future investigations on this material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

High Thermopower in a Zn-Based 3D Semiconductive Metal–Organic Framework

Conductive metal–organic frameworks (c-MOFs) have drawn increasing attention for their outstanding performance in energy-related applications. However, the majority of reported c-MOFs are based on 2D structures. Synthetic strategies for 3D c-MOFs are under-explored, leaving unrealized functionality in both their structures and properties. Here in this paper we report a 3D c-MOF, namely Zn-HAB, designed through topological analysis. Comprising a nitrogen donor linker, hexaaminobenzene, and Zn(II) node, Zn-HAB was found to have microporosity with a band-gap of approximately 1.68 eV. The relatively large band-gap results in a modest conduc-tivity of 0.86 mS cm -1 and a high Seebeck coefficient of 200 μV K -1 at 300 K. The power factor of 3.44 nW m -1 K -2 constitutes the first report of the thermoelectric properties of an intrinsically conductive 3D MOF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2022

The flying spider-monkey tree fern genome provides insights into fern evolution and arborescence

To date, little is known about the evolution of fern genomes, with only two small genomes published from the heterosporous Salviniales. Here we assembled the genome of Alsophila spinulosa, known as the flying spider-monkey tree fern, onto 69 pseudochromosomes. The remarkable preservation of synteny, despite resulting from an ancient whole-genome duplication over 100 million years ago, is unprecedented in plants and probably speaks to the uniqueness of tree ferns. Our detailed investigations into stem anatomy and lignin biosynthesis shed new light on the evolution of stem formation in tree ferns. We identified a phenolic compound, alsophilin, that is abundant in xylem, and we provided the molecular basis for its biosynthesis. Finally, analysis of demographic history revealed two genetic bottlenecks, resulting in rapid demographic declines of A. spinulosa. The A. spinulosa genome fills a crucial gap in the plant genomic landscape and helps elucidate many unique aspects of tree fern biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Low-frequency Raman signature of Ag-intercalated few-layer MoS 2

Density functional theory based calculations and experimental analysis on a limited number of real samples are performed to study how the presence of silver intercalated in the van der Waals gap of few-layer MoS 2 affects the low-frequency Raman active modes of this material. Silver is found to predominantly affect the breathing-like and shear-like vibrational modes of MoS 2 . These modes correspond to quasi-rigid movements of each individual layer with a restoring force (and, in turn, frequency) that is determined by modulations in the weak interlayer interactions. Noticeable red-shifts with increasing Ag concentration are found for all low-frequency modes. This finding indicates the potential for low-frequency vibrations as useful gauges for practical determination of silver concentration using low-frequency Raman spectroscopy. Finally, this work also describes a semi-classical linear chain model that allows to extrapolate results to a large number of layers. Further, first-principles calculations show how Raman spectroscopy can be used to characterize the quality of the two-dimensional interface between MoS 2 and a silver substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impurity states and indirect exchange interaction in irradiated graphene

We theoretically investigate the impurity levels and exchange interaction between magnetic impurities in graphene driven by an off-resonant circularly polarized light field. Our analysis captures the non-perturbative effects resulting from scattering with magnetic impurities with a strong onsite potential. Under irradiation, a dynamical band gap opens up at the Dirac point, allowing impurity levels to exist inside the gap. These impurity levels are shown to give rise to a resonance feature in the exchange energy for impurities located either at the same or different sublattices. The exchange interaction also shows a wider spatial range of antiferromagnetic behavior due to irradiation. Furthermore, our work demonstrates that the exchange energy of magnetic impurities in graphene is extensively tunable by light irradiation in the presence of strong potential scattering.

2-dimensional systems↗
DOE OSTI2020

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Final Report)

The Phase 1 Final Technical Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. A key part of the Phase 1 work was the completion of a thermodynamic cycle analysis for the MHHE at the selected module size. The hybrid heat engine has been developed as a modular unit (sized in the range of 500kW – 60MW) that can be used with modular coal or biomass gasifiers, with distributed power generation systems, with large power plants comprised of multiple generating units, and with natural gas compression stations. The MHHE will provide cleaner, more efficient, and lower cost generation with better load following capabilities than existing competing technologies with a singular generating source such as solar farm, gas turbine, or combustion engine. The drivers of the MHHE technology are: benefits of modular power generation (reduced equipment cost, construction cost and implementation time, connection ready on delivery, flexible scalability, serviceability), fuel flexibility, lowest cost power generation and reduced emissions.A logical progression of work and a clear path forward toward meeting the FOA goals and objectives have been established. Namely, a preliminary market analysis and primary fuel identification was completed first and then the modularity of the system was defined. The benefits of the proposed hybrid and modular heat engine were described when applied to modular coal gasifiers, distributed power generators, and larger power plants. Based on the market analysis, modularity, and chosen primary fuel, a conceptual design and layout of the hybrid heat engine was developed, analyzed, and characterized. The technology gaps already identified have been reviewed and expanded upon, and a test plan to address these gaps through bench scale testing in Phase 2 has been developed. Cost estimate methodology and considerations in support of a potential Phase 2 project have been described.

08 HYDROGEN↗
DOE OSTI2021

Higher Energy-Content Jet Blending Components Derived from Ethanol (CRADA 467)

This project aims to develop a new sustainable aviation fuel (SAF) that comprises favorable properties, such as high energy density, excellent thermal stability, and favorable cold fold properties. Through catalyst development, this work will provide a route to control the cycloalkane/n-alkane/iso alkane content of a next-generation fuel with minimal or no aromatic content. Combining n-alkane and iso-alkane streams (high specific energy, MJ/kg) with cycloalkanes (higher energy density, MJ/L), is expected to enable at least a 4% net increase in combined (specific (MJ/kg) and volumetric (MJ/L)) energy content without impacting ‘drop-in’ fuel requirements, such as seal swelling. PNNL and LanzaTech have already demonstrated a sustainable, non-petroleum, route to isoalkanes. However, economically attractive cycloalkane production from waste and biomass is challenged by large hydrogen requirements, preferential selectivity to aromatic compounds and low yields to jet fuel range components. Many gaps in understanding cycloalkane properties and performance in complex jet fuel mixtures remain. The close tie and integration of Purdue’s fuel property analysis, with PNNL’s process development, can lead to an economically attractive process. The fuel analysis and testing by Purdue will enable a robust understanding of the properties and behavior of the cycloalkanes produced to inform process development. Additionally, seal-swelling analysis will quantify the ability of fuel blends with zero or minimal aromatics content to satisfy the seal swell requirement of O rings. Lastly, Purdue’s system-level analysis will lead to the development of a roadmap for deployment in key regions that considers system pressures such as hydrogen, water, energy efficiency, and ease of infrastructure access.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Numerical Analysis and Flow Induced Vibration Studies in Pressurized Helium Gas Cooling Channels

Los Alamos National Laboratory (LANL) is working with NorthStar Medical Radioisotopes (NMR) on their efforts to produce Mo-99 from the irradiation of Mo-100 targets. The NMR target consists of an Inconel window that allows the electron beam to penetrate and irradiate a stack of Mo-100 discs. The irradiation process generates large amounts of heat and is cooled using pressurized helium gas flowing at 400 g/s. LANL provides both numerical analysis support and experimental support. Computational fluid dynamics (CFD) simulations for both 300 g/s and 400 g/s are performed on the NMR target to analyze the helium flow through the NMR system. The pressure drop, the mass flow rate through each of the disc and window channels and the force applied on the first disc are some of the quantities estimated using CFD. These results will be combined with Monte Carlo N-Particle (MCNP) estimates of heat flux into the system from the electron beam to perform conjugate heat transfer analysis (CHT). The experiments presented in this report focus on the effect of the flow induced vibrations from helium flowing through the coolant gaps between the discs. There is concern that the flow induced vibrations may lead to excessive mass loss of the enriched Mo-100 discs, and the goal would be to mitigate the mass loss. This work presents benchtop tests of flow through a subset of the target stack which involves high speed imaging and displacement measurements used to estimate disc vibration, as well as mass loss.

42 ENGINEERING↗
DOE OSTI2024

A Scalable & Non-Destructive Characterization Strategy to Study Semiconductor/Dielectric Interfaces and Predict Wafer-Level Device Performance

The defect density present at the dielectric-semiconductor interface in an MOS structure directly influences the channel carrier characteristics in semiconductor devices, especially in wide bandgap material systems used in power devices. While these trap defects are typically quantified through electrical characterization of MOS-capacitor test structures, this treatment offers very little insight into the physical nature of interface defects. Such shortcomings demand a physical characterization strategy to guide fabrication optimization. X-ray photoelectron spectroscopy (XPS) is suggested as a viable technique to determine chemical data for dielectric interfaces formed using atomic layer deposition (ALD) on GaN substrates. Previously, 1-D XPS characterization has confirmed the presence of a Ga x O y interlayer between ALD dielectrics and the GaN substrate. In this work, XPS data is serially collected to form 2-D images of an ALD-Al 2 O 3 /GaN interface as a proof-of-concept experiment for in-situ XPS quality monitoring during ALD processing. The information provided by this work reveals some of the challenges for incorporating XPS characterization as an in-situ strategy during fabrication of GaN-based devices. Separately, electrical mapping of a 2-D array of ALD-Al 2 O 3 /GaN MOS-capacitor devices provide a means to quantify the spatial variations in interface quality across a single wafer. Physical characterization techniques, such as time-of-flight secondary ion mass spectroscopy, provide additional chemical information about the Al 2 O 3 /Ga x O y /GaN structure that complement the electrical mapping results. This analysis shows that a higher Ga x O y content correlates with higher interface state defects for trap energies deep in the band gap.

42 ENGINEERING↗
DOE OSTI2025

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Cross-correlation image analysis for real-time single particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Instruments & Instrumentation↗
DOE OSTI2023

Mobility Gaps between Low-Income and Not Low-Income Households: A Case Study in New York State

Understanding the travel challenges faced by low-income residents has always been and continues to be one of the most important transportation equity topics. This study aims to explore the mobility gaps between low-income households (HHs) and not low-income HHs, and how the gaps vary within different socio-demographic population groups in New York State (NYS). The latest National Household Travel Survey data was used as the primary data source for the analysis. The study first employed the K-prototype clustering algorithm to categorize the HHs in NYS based on their socio-demographic attributes. Five population groups were identified based on nine different household (HH) features such as HH size, vehicle ownership, and elderly status of its members. Then, the mobility differences, measured by trip frequency, trip distance, travel time, and person miles traveled, were examined among the five population groups. Results suggest that the individuals in low-income HHs consistently took fewer trips and made shorter trips compared to their not low-income counterparts in NYS. The travel distance gaps were most obvious among white HHs with more vehicles than drivers. In addition, while the population from low-income HHs made shorter trips on average (2.7 mi shorter per trip), they experienced longer travel time than those from not low-income HHs (1.8 min longer per trip). These key findings provide a deeper understanding of the travel behavior disparities between low-income and not low-income households. The findings could also support policymakers and transportation planners in addressing the critical needs of residents in low-income households in NYS and provide inputs for designing a more equitable transportation system.

Liu, Yuandong↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 105, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adaptation to perform analysis on future survey data with a deep neural network trained on simulated data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

AGR-5/6/7 CAPSULE 1 THERMAL MODEL WITH OFFSET GAS GAPS

Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tristructural isotropic (TRISO) fuel particles. In the absence of direct measurement, fuel compact temperatures were calculated using a 3 D finite-element thermal model that was subject to simulation uncertainty. The most dominant factor in the uncertainty of calculated fuel temperatures is the gas gap uncertainty caused by the graphite holder nub-to-capsule clearance. This thermal model was revised to examine the most probable graphite offset position for Capsule 1 for four different days during the irradiation. In the analysis, the offset distance and azimuthal direction at both the top and bottom of the holder were varied. The best-fit offset was estimated based on the minimum root mean square error (RMSE) of the residuals (measured minus calculated thermocouple [TC] temperatures) for the operational TCs in Capsule 1. Near the end of the irradiation, when the graphite had shrunk and the gas gaps had grown, a peak temperature was found with maximum offset along the TC lines, with nickel introducing a potential mechanism for fuel particle failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗