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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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The purpose of this project is to continue supporting customizations of algorithms and raw data file structures to enhance software workflows for liquid chromatography (LC), mass spectrometry (MS) and ion mobility mass spectrometry (IM-MS)-based protein and metabolite characterization.
This report describes a tool for three dimensional, high fidelity, coupled electro-chemo-thermo-mechanical modeling of solid-state batteries. A complete version of the tool is available as open source software at https://github.com/hugary1995/eel.git. The theoretical framework of the tool revolves around an inf-sup statement on a total potential, comprising the Helmholtz free energy, the electrical kinetic potential, the chemical potential, the Fourier potential, the chemical reaction potential, and the external power expenditure. The tool uses the finite element framework of Multiphysics Object-Oriented Simulation Environment (MOOSE) and a variational formulation to solve the boundary value problem for solid-state battery incorporating the full set of multiphysics couplings. The variational formulation also enables a modular software architecture for the tool so additional physics can be easily included by specifying the new contribution to the total potential. The report discusses several method of manufactured solutions that were used to verify the implementation of different physics and the Butler-Volmer reaction kinetics at the electrolyte-electrode interface. Finally, the report discusses results from complete charge/discharge simulations of a quasi-1D and a 3D solid-state battery.
Qualification measurements were performed at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) on a variety of measurement standards. These measurements will be used to qualify the In-Situ Object Counting System (ISOCS) measurement method with a high-purity germanium (HPGe) detector for use at ORNL. Measured items include uranium isotopic standards, filter papers coated with uranium oxide (U 3 O 8 ), U 3 O 8 line sources, and a uranium tetrafluoride (UF 4 ) source. The 235 U mass was estimated using the ISOCS calibration software for each source and compared with the declared isotope mass value.
With geopolitical tensions high around the globe (Ukraine, Russia, China…), our mission is more important now than at any point since the Cold War – Our people help ensure the reliability of the deterrent, detect weapons of mass destruction, and advance the frontiers of science, engineering and computing.
Abstract not provided.
Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.
The TEAMS project brought together cutting-edge simulations, theoretical insights, and collaborative efforts to deepen our understanding of some of the universe’s most extreme phenomena—supernovae, neutron star mergers, and the powerful signals they emit. Using one of the largest suites of 3D supernova simulations ever conducted, researchers uncovered new insights into how massive stars explode, how those explosions vary by stellar mass, and what conditions lead to the birth of neutron stars or black holes. They also studied the radiation and gravitational wave signals emitted during these events, revealing how future observations can be used to uncover what happens deep inside collapsing stars. The team developed improved tools for modeling how light and neutrinos behave in such explosive environments, enabling more accurate predictions of what astronomers might observe. Work also explored how the chemical composition and geometry of kilonovae—the visible explosions that follow neutron star mergers—influence their signals and can reveal the origins of heavy elements like gold. These efforts not only advanced scientific knowledge, but also trained a new generation of researchers at the intersection of astrophysics, computational science, and nuclear theory.
This report describes the University of Iowa portion of a project that is now continuing at Michigan State University. We are developing novel methods, algorithms, and software to enable simulations of molecules and materials at high temperature. This is a key challenge in chemistry and materials science. By refining an approach called Density Matrix Quantum Monte Carlo (DMQMC), we developed faster and more accurate ways to conduct these simulations. These advances will help us understand how temperature affects the behavior of electrons, chemical bonds, and phase transitions in solids and molecules. These breakthroughs are especially important for applications where light and heat drive chemical reactions, superconductivity, and materials used in energy and sensing. In addition, this project involved the development of the open-source HANDE-QMC software package, supporting the broader community in benchmarking and developing finite-temperature electronic structure methods.
In this report, we evaluate the trade-offs between accuracy and performance in three equation of state interpola- tion strategies- EOSPAC, SpinerEOSDependsRhoT, and SpinerEOSDependsRhoSie-using SESAME tabular data. We also investigate ways to reduce nonlinearity in the data through invertible transformations within the SpinerEOS- DependsRhoSie class with the goal of impacting interpolation accuracy. We include a variety of benchmarking tests that vary the grid densities and how memory is accessed.
This report provides a performance summary of the “Isotopics by Nuclides” tool in InterSpec. The primary objective of the NA-241 FY25 project was the detection and characterization of non-homogeneous uranium samples. However, the tool also demonstrates effectiveness in determining the enrichment of homogeneous uranium or plutonium from single gamma spectra. This paper presents a subset of evaluated data to avoid distribution limitations while offering potential users’ insight into the tool's performance.
At LANL, I have had the privilege and pleasure of working with Dr. Tika Kafle, an optics and condensed matter physicist at the MagLab in TA-35. Dr. Kafle’s research primarily involves the Angle Resolved Photoemission Spectroscopy (ARPES) technique of characterizing materials. More specifically, Dr. Kafle works with Time Resolved ARPES (Tr-ARPES), which extends this technique into the time domain.
The annual String Data conferences have become the flagship annual meeting for the subfield at the interface of formal high energy theory, pure mathematics, and machine learning. String Data 2023 featured invited plenary talks by leading researchers in addition to a parallel session. The funds helped mitigate conference planning and provided support to young researchers.
We pursued two major projects. In the first, we studied the degradation of a-Si/c-Si Heterojunction cells. We substantially expanded our previously developed SolDeg platform, and incorporated hydrogen into it. We also expanded the capabilities of SolDeg by developing a three channel model for a very efficient way to determine long term junction dynamics. The first main result was that we identified hydrogen drifting away from the interface as a key degradation channel. The second was that we developed and proposed the concept of a “reverse silicon density gradient” in the a-Si layer that had the promise of reducing degradation by up to 80%. In the second project, we studied the degradation of TOPCon solar cells. We again identified a key degradation channel, which in these cells was the formation of pinholes across the oxide layer. We then again were able to develop and to propose a way to dramatically reduce degradation by discovering that tuning the hydrogen concentration forms a sharp and deep minimum of the recombination current.
We report that the last decade has seen a continued development of better experimental techniques to measure equation-of-state (EOS) for various materials. These improvements of both static and shock-compression approaches have increased the accuracy of the EOS and challenged the complimentary theoretical modeling. The conventional modeling of EOS, at least at pressure and temperature conditions that are not too extreme, is founded on density-functional theory (DFT). Naturally, there is an increased interest in the accuracy of DFT as the measurements are becoming more refined and there is a particular interest in the robustness and validity of DFT at conditions where experimental data are not available. Here, we consider a broad and large set of 64 elemental solids from low atomic number Z up to the very high Z actinide metals. The intent is to compare DFT with experimental zero-temperature isotherms up to 1 Mbar (100 GPa) and draw conclusions regarding the theoretical (DFT) error and quantify a reasonable and defensible approach to define the theoretical uncertainty. Finally, we find that in all 64 cases the DFT error at high pressure is smaller than or equal to the DFT error at lower pressures which thus provides an upper bound to the error at high compression.
For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.
General physics, plasma dynamics, engineering, and communication sciences programs at Research Laboratory of Electronics
The RDamage series of ten experiments is part of a long-term collaboration with RFNC/VNIIEF in pulsed power technology. These experiments use a cylindrical configuration to study spallation damage, which allows for a natural recollection of the damaged material under proper driving conditions and post-shot collection of the damaged target material for subsequent metallographic analysis. Dynamic in-situ experimental velocimetry diagnostics are also employed. LANL is responsible for the design of the experimental load and velocimetry system. VNIIEF is responsible for the design and construction of the driving explosive magnetic generator. In the RDamage-0, -1 and -2 experiments, data was collected about failure initiation of a well-characterized material (aluminum) in a cylindrical geometry. The RDamage-3, -4 and -5 experiments produced data on the behavior of material recollected after damage from pressures in the damage initiation regime. Data on the behavior of material recollected after complete failure was collected in the RDamage-6, -7, -8 and -9 experiments. This presentation shows the design choices behind the evolution of the load assembly through the experimental series.