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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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DOE OSTI2020

Thermionic emission and energy conversion

This project worked on development of a transformative heat-to-electricity conversion device based on thermionic energy conversion. The research provided a unique opportunity to transform the thermionics technology of the 1950-1990s by applying modern materials and 21st century wafer-scale fabrication methods. Research was conducted to further development of a thermionic energy converter based on modern microfabrication and new low work function anode materials that can surpass all other forms of direct heat-to-electricity conversion. This project completed the following goals: (1) Developed components for use in thermionic energy converters and electron emission devices, including electrode materials for electron emission and electron absorption with ultimate work functions below 5 eV; (2) Designed and fabricated thermionic devices for energy conversion and electron emission based on the components developed in (1); (3) Engineered test systems to characterize electronic, thermal, and mechanical properties of thermionic devices and electron emitters and their components, and test devices and components designed and fabricated in (1) and (2); and (4) Developed theoretical modeling capabilities in support of these technical objectives.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Data Quality Objectives Supporting Radiological Air Emissions Monitoring for the PNNL-Richland Campus: North Campus Construction (Rev. 3)

Starting in fiscal year 2023, the north area of the PNNL-Richland Campus will undergo development. Initially, infrastructure (water, electrical) improvements will be installed. Later development includes the construction of several new office buildings. The area to be developed currently contains no buildings but does have two existing solar-powered ambient air sampling stations. Under the requirements of Washington State Department of Health Radioactive Air Emissions License -005, the PNNL-Richland Campus must operate and maintain a radiological air monitoring program. This revision documents and evaluates how the new North Campus construction impacts ambient air surveillance stations within the development region. This revision also considers current stack configurations and uses an updated environmental dispersion model and updated meteorological data. The DQO team concluded that one sampling station in the construction area will be relocated and the other remain in place. The evaluations conducted for this DQO also identified a third sampling station outside the development area that is recommended for relocation as a result of updated dispersion modeling. Recommended sites for the relocations are presented. Considerations for sampling in and around a construction zone are also addressed. Additionally, programmatic improvements to the ambient air sampling program were identified in the DQO process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

JRO Exhibit Article

Lab staff can now see unique artifacts related to the Lab’s famed first director, J. Robert Oppenheimer. The temporary display, located in the lobby of the National Security Sciences Building (TA-03-1400) through the end of June, highlights objects and photos from Oppenheimer’s life, including his time as director of Los Alamos. Items from both the Lab’s National Security Research Center and the Bradbury Science Museum will be on display.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Searching for dark clumps with gravitational-wave detectors

Dark compact objects (“clumps”) transiting the Solar System exert accelerations on the test masses (TM) in a gravitational-wave (GW) detector. We reexamine the detectability of these clump transits in a variety of current and future GW detectors, operating over a broad range of frequencies. TM accelerations induced by clump transits through the inner Solar System have frequency content around f ~ μ Hz . Some of us [Fedderke et al., Phys. Rev. D 105, 103018 (2022)] recently proposed a GW detection concept with μ Hz sensitivity, based on asteroid-to-asteroid ranging. From the detailed sensitivity projection for this concept, we find both analytically and in simulation that purely gravitational clump–matter interactions would yield one detectable transit every ~ 20 yrs , if clumps with mass m cl ~ 10 14 kg saturate the dark-matter (DM) density. Other (proposed) GW detectors using local TMs and operating in higher frequency bands are sensitive to smaller clump masses and have smaller rates of discoverable signals. We also consider the case of clumps endowed with an additional attractive long-range clump–matter fifth force significantly stronger than gravity (but evading known fifth-force constraints). For the μ Hz detector concept, we use simulations to show that, for example, a clump–matter fifth-force ~ 10 3 times stronger than gravity with a range of ~ AU would boost the rate of detectable transits to a few per year for clumps in the mass range 10 11 kg ≲ m cl ≲ 10 14 kg , even if they are a ~ 1 % subcomponent of the DM. The ability of μ Hz GW detectors to probe asteroid-mass-scale dark objects that may otherwise be undetectable bolsters the science case for their development.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Sandia National Laboratories Ecosystem for Open Science: Metadata Schema v0.2 Description.

The Ecosystem for Open Science (eOS) initiative was established in 2019. Its objective is improving openness and sharing of data and information across Defense Nuclear Nonproliferation (DNN) Research and Development (R&D) activities. To support this initiative, the eOS team at Sandia National Laboratories (SNL) developed metadata and data standards and proposed a machine-readable metadata schema. The nuclear explosion monitoring field was selected as a focus area due to its the wide range of pertinent phenomenologies.We developed the DCAT-eOS-AP metadata schema extending the Data Catalog Vocabulary version 2 (DCATv2) standard using an application profile (AP), to fit the needs of multi-disciplinary NA-22 projects. The DCAT-eOS-AP metadata schema describes data at different levels of granularity ranging from general descriptions to more domain-specific granular metadata. Its implementation and serialization is flexible with the ability to include new file or data types. Thus, it will scale with the ever-increasing data management needs of government research. Due to the multitude of phenomenologies represented in the DCAT-eOS-AP schema, we anticipate that it will be easily extensible to various projects across many DOE mission areas. This document describes data management challenges faced within the DNN R&D portfolio and provides insight on how metadata and data standards/guidelines combined with a comprehensive metadata schema can add value to programs throughout the Department of Energy (DOE). It reviews the importance of metadata standards, FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles, and metadata schemas. Additionally, it summarizes input from subject matter experts (SME) at SNL and other National Laboratories that resulted in metadata and data standards/guidelines encompassing domains relevant to NA-22 projects. Finally, we discuss the DCAT-eOS-AP metadata development. Implementation recommendations and future development directions are included for those keen on adopting the DCAT-eOS-AP metadata schema.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

FCIC Task 5--Preprocessing

the objective of this project is to develop science-based design and operation principles (as informed by TEA and LCA), which result in preprocessing units to operate predictably and reliably, and are capable of scalable performance.

09 BIOMASS FUELS↗

DOE OSTI2026

ARM Aerosol Measurement Science Group 2024 Strategic Planning Workshop Report

This report summarizes the results of the 2024 Strategic Planning Workshop conducted by the Aerosol Measurement Science Group (AMSG) for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility. The objective of this workshop was to provide ARM facility guidance through specific recommendations relating to aerosol science for a period of five years. The AMSG primary focus of this workshop was to help advance strategic external partnership engagement for ARM’s aerosol measurement program. The workshop was held July 9-10, 2024 at the University of Utah. This was the third such workshop since the AMSG was chartered in 2015 to enhance coordination of ARM observations of aerosols and atmospheric trace gases with the needs of ARM users.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Atomic Layer Deposition (ALD) of Metal and Metal Oxide Films: A Surface Science Study (Final Report)

36 MATERIALS SCIENCE↗

DOE OSTI2026

Atomic Layer Deposition (ALD) of Metal and Metal Oxide Films: A Surface Science Study (Final Report)

This is the final report for this project. The long-term objective of our project is to develop a general molecular-level picture of the surface chemistry associated with ALD processes. Our central hypothesis is that the chemistry of ALD precursors can differ significantly from that seen in solution. Awareness of such differences should provide general guidelines on what to watch for when synthesizing new ALD precursors and designing and optimizing new ALD processes, especially in terms of minimizing the incorporation of impurities in the growing films and of carrying out depositions under mild pressure and temperature conditions. The main objective of our research project has been to advance the fundamental knowledge of the surface chemistry of ALD precursors needed for the design and optimization of film deposition processes. A modern surface-science approach has been implemented to both elucidate the mechanism of the reactions of the precursors on the surface and characterize the composition and morphology of the growing films. In general, emphasis is being placed on: • Identifying the primary reactions that may lead to the deposition of clean films; •Identifying the secondary reactions that may help the ALD process, by, for instance, helping with the reduction (or oxidation) of the metal atom; • Identifying the secondary reactions that may lead to the deposition of undesirable impurities in the growing films; • Determining the kinetic parameters of the relevant surface reactions in order to define the optimum conditions for film deposition and to minimize impurity deposition. • Characterizing the nature of the resulting films, with focus on their stoichiometry and on the final oxidation states of the constituent elements; and • Using the information obtained to propose better precursors for given ALD processes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multi-objective surrogate-assisted calibration of CPFEM models using macroscopic response and in situ EBSD measurements of grain reorientation trajectories

Crystal plasticity finite element method (CPFEM) models are widely used to simulate the deformation behaviour of polycrystalline materials, but their calibration is often limited by their high computational cost and the non-convexity of the optimisation landscape. Here, this study develops a multi-objective surrogate-assisted calibration workflow that couples a multi-objective genetic algorithm (MOGA) with an adaptively trained deep neural network (DNN) surrogate model to efficiently identify CPFEM parameters from experimental data. The workflow is demonstrated on three crystal plasticity (CP) formulations of increasing complexity — Voce hardening (VH), two-coefficient latent hardening (LH2), and six-coefficient latent hardening (LH6) — using in situ electron backscatter diffraction (EBSD) measurements of Alloy 617 under uniaxial tensile loading. The CPFEM models are calibrated against the experimentally observed stress–strain response and reorientation trajectories of eight grains, then validated against eight additional trajectories and overall texture evolution. Across the CP formulations, the macroscopic response was reproduced reliably, while differences emerged in the robustness and accuracy of the grain-scale predictions. Including grain reorientation trajectories in the multi-objective calibration improved texture evolution predictions and filtered out physically inconsistent parameter sets that can arise from calibrating against only the stress–strain data. The workflow also demonstrates good transferability of calibrated parameters from a low- to a high-fidelity microstructural model. These results provide practical guidance for integrating in situ microstructural data into CPFEM through efficient, repeatable, and physically meaningful multi-objective calibration.

Crystal plasticity finite element method↗

DOE OSTI2022

Challenges for Implementing FAIR Digital Objects with High Performance Workflows

New types of workflows are being used in science that couple traditional distributed and high-performance computing (HPC) with data-intensive approaches, and orchestrate ensembles of numerical simulations and artificial intelligence (AI) models. Such workflows may use AI models to supplement computation where numerical simulations may be too computationally expensive, to automate trivial yet time consuming operations, to perform preliminary selections among intractable numbers of combinations in domains as diverse as protein binding, fine-grid climate simulations, and drug discovery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Multi-objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three-dimensional functional–structural plant model, SimRoot, to the Borg Multi-Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade-offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non-mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi-objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Capturing high‐resolution digital images for use in forensic document examination

Abstract In the past, pattern disciplines within forensic science have periodically faced criticism due to their subjective and qualitative nature and the perceived absence of research evaluating and supporting the foundations of their practices. Recently, however, forensic scientists and researchers in the field of pattern evidence analysis have developed and published approaches that are more quantitative, objective, and data driven. This effort includes automation, algorithms, and measurement sciences, with the end goal of enabling conclusions to be informed by quantitative models. Before employing these tools, forensic evidence must be digitized in a way that adequately balances high‐quality detail and content capture with minimal background noise imparted by the selected technique. While the current work describes the process of optimizing a method to digitize physical documentary evidence for use in semi‐automated trash mark examinations, it could be applied to assist other disciplines where the digitization of physical items of evidence is prevalent. For trash mark examinations specifically, it was found that high‐resolution photography provided optimal digital versions of evidentiary items when compared to high‐resolution scanning.

Riley, Paige↗

DOE OSTI2021

Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21): 15-Year Evaluation of an Innovative Research Training Program

To evaluate the 15-year impact of a transdisciplinary research training program for graduate students, postdoctoral fellows, and clinical trainees focused on radiation science, entitled Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21) with a primary objective to build capacity in radiation research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

LightMAT: Continous Fiber Malleable Thermoset Composites with sub-1-minute dwell times; validation of impact performance and evaluation of the efficacy of the compression forming process

Recent major advances in polymer chemistry are enabling a paradigm shift in the area of processing and re-processing/recycling of advanced composite materials. Inexpensive compression molding with short cycle times, traditionally available for thermoplastic composites only, are now possible for the thermoset resins based on polyimine chemistry. Participant will collaborate with the LightMAT consortium Labs ORNL, SNL, and PNNL to non-destructively characterize microscopic structural defects (such as voids and resin-rich areas), and perform high speed impact characterization of its malleable thermoset CFRP's. This work is critical to the validation and adoption of these materials for the automotive market and towards DOE-VTO lightweighting mission objectives.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multi-Probe FY25 Omega EP Shot Request [Slides]

The goal of the Multi-Probe experimental campaign is to assess the feasibility of using multiple probe types (x ray, proton, neutron, and electron) in future LANL radiographic facilities. X-ray and proton platforms have been demonstrated. The first attempts at simultaneous radiography were hampered by cross talk, which was the subject of FY23 & FY24 experiments. The major goal for FY25 is to demonstrate simultaneous radiography of both static and dynamic objects.

36 MATERIALS SCIENCE↗