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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Equity for women and underrepresented minorities in STEM: Graduate experiences and career plans in chemistry

Significance On June 10, 2020, as part of the Black Lives Matter movement, scientists in the United States and throughout the world paused to consider how systematic racism affects the scientific enterprise. As a result, many academic departments are now assessing policies and practices that may contribute to this situation. This paper provides evidence of the nature of inequities related to race–ethnicity and gender in graduate school experiences and career plans of PhD students in one science, technology, engineering, and math (STEM) discipline, chemistry. The results can help promote understanding of the problems and guide efforts toward equity within STEM and, potentially, other academic areas. In turn, these changes can strengthen the scientific enterprise and the well-being of society.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An essential and highly selective protein import pathway encoded by nucleus-forming phage

Targeting proteins to specific subcellular destinations is essential in prokaryotes, eukaryotes, and the viruses that infect them. Chimalliviridae phages encapsulate their genomes in a nucleus-like replication compartment composed of the protein chimallin (ChmA) that excludes ribosomes and decouples transcription from translation. These phages selectively partition proteins between the phage nucleus and the bacterial cytoplasm. Currently, the genes and signals that govern selective protein import into the phage nucleus are unknown. Here, we identify two components of this protein import pathway: a species-specific surface-exposed region of a phage intranuclear protein required for nuclear entry and a conserved protein, PicA (Protein importer of chimalliviruses A), that facilitates cargo protein trafficking across the phage nuclear shell. We also identify a defective cargo protein that is targeted to PicA on the nuclear periphery but fails to enter the nucleus, providing insight into the mechanism of nuclear protein trafficking. Using CRISPRi-ART protein expression knockdown of PicA, we show that PicA is essential early in the chimallivirus replication cycle. Together, our results allow us to propose a multistep model for the Protein Import Chimallivirus pathway, where proteins are targeted to PicA by amino acids on their surface and then licensed by PicA for nuclear entry. The divergence in the selectivity of this pathway between closely related chimalliviruses implicates its role as a key player in the evolutionary arms race between competing phages and their hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A template of information needs for decision-making about delaying remediation on contaminated lands to protect human health

The contamination legacy of industrialization, militarization, and nuclear arms race poses current or future risks to populations and the environment. Responsible parties and regulators make decisions regarding which sites to clean up, how, how much, and when. This study aimed to provide an information needs template to evaluate and reduce risks to human health when considering whether to initiate or delay remediation. This investigation focused on four aspects of timing and prioritization: 1) management, planning and implementation, 2) source terms, pathways, and exposures, 3) risks and receptors, and 4) external drivers. Within each type, issues were identified and described. Management class included personnel, health and safety data, funding, equipment, and structural integrity. Source term included contaminant sources, pathways, initiating events, and barriers to exposure. Risk included types and exposures to workers and general public. External drivers included regulatory framework, stakeholders, Congressional mandates, and economic and social contexts. Risk may increase over time as contamination spreads, enters aquifers, and reaches receptors, and may decline as radionuclides decay, and plumes dissipate. The overall objective was to provide a template of information that is useful to managers and regulators, and might be used by the public to understand the risks and benefits of re-prioritization cleanup.

Environmental Sciences & Ecology↗
DOE OSTI2024

Distributional Equity in the Employment and Wage Impacts of Energy Transitions

We use restricted-access, geocoded data on the near-universe of workers in 23 US states to quantify the impact of wind energy development on local earnings and employment, by race, ethnicity, sex, and educational attainment. We find significant impacts that persist for several years beyond the project construction phase. Our estimates are larger than those from previous studies but still small relative to typical economic multipliers for fiscal spending or investment in other industries. We find the largest percentage increases for black workers and workers who either do not have a high school diploma or who have a college degree. We also find the economic gains for men to be much larger than those for women. Finally, we find estimates from data aggregated to the county level to be significantly lower than our worker-level estimates. We suggest a number of areas for further study building off the justice implications of our findings.

employment↗
DOE OSTI2024

The health, climate, and equity benefits of freight truck electrification in the United States

Abstract Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are $195–$249/capita compared to $174–$205/capita for a new diesel fleet, and $156–$177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in $32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

Hennessy, Eleanor M. (ORCID:0000000294715765)↗
DOE OSTI2021

Simulating quantum materials with digital quantum computers

Quantum materials exhibit a wide array of exotic phenomena and practically useful properties. A better understanding of these materials can provide deeper insights into fundamental physics in the quantum realm as well as advance technology for entertainment, healthcare, and sustainability. The emergence of digital quantum computers (DQCs), which can efficiently perform quantum simulations that are otherwise intractable on classical computers, provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. Equipped with these new tools, scientists from diverse domains are racing towards achieving physical quantum advantage (i.e., using a quantum computer to learn new physics with a computation that cannot feasibly be run on any classical computer). The aim of this review, therefore, is to provide a summary of progress made towards this goal that is accessible to scientists across the physical sciences. We will first review the available technology and algorithms, and detail the myriad ways to represent materials on quantum computers. Next, we will showcase the simulations that have been successfully performed on currently available DQCs, emphasizing the variety of properties, both static and dynamic, that can be studied with this nascent technology. Finally, we work through two examples of how to map a materials problem onto a DQC, with full code included in the Supplementary Material. It is our hope that this review can serve as an organized overview of progress in the field for domain experts and an accessible introduction to scientists in related fields interested in beginning to perform their own simulations of quantum materials on DQCs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evolutionary vs imitation learning for neuromorphic control at the edge*

Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

Schuman, Catherine↗
DOE OSTI2021

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Prediction of high-temperature superconductivity in C2/c-24 solid hydrogen

Recent experimental developments in hydrogen-rich materials at high pressures have put this class of materials above others in the race toward room-temperature superconductivity. As it is the basis of all the materials in this class, the efforts to determine the properties of pure solid hydrogen at high pressures remain intense. Most notably, a recent experimental study of the metallization of hydrogen identified the crystal phase of the solid as the C2/c-24 molecular phase up to ~425 GPa [Loubeyre et al., Nature (London) 577, 631 (2020)10.1038/s41586-019-1927-3]. It is possible that the observed metallization is caused by band structure effects and not a structural phase transition, and the material remains in this crystal phase up to higher pressures [Dogan et al., J. Phys.: Condens. Matter 33, 03LT01 (2020)10.1088/1361-648X/abba8a]. Therefore, it is of crucial importance to determine the superconducting properties of the C2/c-24 phase. Here, we employ a Wannier function-based dense k-point and q-point sampling to compute the electron-phonon coupling and superconducting properties of molecular hydrogen in the C2/c-24 phase. We find that the material has a high superconducting transition temperature of 242 K at 500 GPa. We also find that the transition temperature rapidly increases with pressure in the 400-500-GPa range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Optimization and Comparison of Modern Offshore Wind Turbine Generators Using GeneratorSE 2.0

As the offshore wind industry keeps growing at a rapid pace, developers are bracing themselves for a huge demand in critical rare earth metals which will threaten an already vulnerable supply chain. The wind energy industry is addressing this problem by investing in modern generator technologies that employ magnets with reduced rare earth content and high-field magnets enabled by rare-earth-free superconductors. In this paper we introduce the National Renewable Energy Laboratory's newly advanced GeneratorSE 2.0, which is a design and optimization tool that was developed to investigate the feasibility of such modern generators. Two direct-drive generator topologies with different magnet materials and mounting arrangements are investigated: an outer-rotor, V-shaped interior permanent magnet generator, and an inner-rotor normally conducting armature, paired with a low-temperature superconducting field with race-track coils. These technologies were evaluated for a range of power ratings between 15 and 25 MW, which represent the next generation of offshore wind turbines for both fixed-bottom and floating applications. The analyses indicate a new trend favoring the low-temperature superconducting technology for the direct-drive system.

direct-drive generators↗
DOE OSTI2021

FPV Video Adaptation for UAV Collision Avoidance

First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Structure of a Vaccine-Induced, Germline-Encoded Human Antibody Defines a Neutralizing Epitope on the SARS-CoV-2 Spike N-Terminal Domain

Structural characterization of infection- and vaccination-elicited antibodies in complex with antigen provides insight into the evolutionary arms race between the host and the pathogen and informs rational vaccine immunogen design. We isolated a germ line-encoded monoclonal antibody (mAb) from plasmablasts activated upon mRNA vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and determined its structure in complex with the spike glycoprotein by electron cryomicroscopy (cryo-EM). We show that the mAb engages a previously uncharacterized neutralizing epitope on the spike N-terminal domain (NTD). The high-resolution structure reveals details of the intermolecular interactions and shows that the mAb inserts its heavy complementarity-determining region 3 (HCDR3) loop into a hydrophobic NTD cavity previously shown to bind a heme metabolite, biliverdin. We demonstrate direct competition with biliverdin and that, because of the conserved nature of the epitope, the mAb maintains binding to viral variants B.1.1.7 (alpha), B.1.351 (beta), B.1.617.2 (delta), and B.1.1.529 (omicron). Our study describes a novel conserved epitope on the NTD that is readily targeted by vaccine-induced antibody responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

MPIDiff

MPIDiff is a C++ library for pairing a correct version of an executable with a version that may have bugs or race conditions in it. The correct version checks that the data communicated from the other program matches its own and reports any differences.

Simpkins, BryanG.↗
DOE OSTI2025

Scabbard

Scabbard is a tool to detect data races that occur between AMD GPUs and CPUs, for projects using HIP technology. The tool detects unsynchronized memory accesses when using unified memory. It is designed as an extension of LLVM/clang for ROCm.

Laguna Peralta, Ignacio [Lawrence Livermore Nation↗
DOE OSTI2023

Longitudinal Changes in Immune Activation Serum Biomarkers Prior to Diagnosis and Risk of B-cell NHL Subtypes

To examine the contribution of B-cell activation molecules to B-cell follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), a prospective study was conducted using pre-diagnosis serial serum samples from the US Department of Defense Serum Repository. Each case (n = 142 FL, n = 211 DLBCL) was matched to two controls on age, gender, race, military branch, and blood collection dates. Immune activation molecules (IL1β, IL2, IL4, IL5, IL6, IL10, IL12, CXCL13, IL8, TNFα, IFNγ, GM-CSF, VEGF, sCD30, IgE) were quantified using ELISA or multiplex immunometric (Luminex) assay. Longitudinal data were analyzed using linear mixed modeling. As serial specimens were collected over several years before diagnosis, we evaluated the temporal dynamics of these markers. Increased serum levels of sCD30, CXCL13, and to a lesser extent IL10, were associated with both FL and DLBCL in cases compared with controls, with a median follow-up of 5.5 years from the earliest specimen collection to diagnosis date. Significant increasing sCD30 and CXCL13 trajectories for FL and DLBCL subtypes were noted starting at the earliest time points and with IL10 levels increasing significantly at time points closer to diagnosis. In conclusion, these results suggest that sCD30, CXCL13, and IL10 may contribute to the etiology of FL and DLBCL and are potential biomarkers for these non–Hodgkin lymphoma subtypes. The increasing trajectories of the B-cell activation molecules, sCD30, CXCL13, and to a lesser extent IL10, may indicate early disease-induced effects or reflect the chronic stimulation of B-cells that promotes the development of FL and DLBCL subtypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Predicting Postoperative Injury and Military Discharge Status After Knee Surgery in the US Army

Background: Researchers have assessed postoperative injury or disability predictors in the military setting but typically focused on 1 type of surgical procedure at a time, used relatively small sample sizes, or investigated mixed cohorts with civilian populations. Purpose: To identify the relationship between baseline variables and injury incidence or military discharge status in US Army soldiers after knee surgery. Study Design: Case-control study; Level of evidence, 3. Methods: Data were obtained from a repository containing personnel, performance, and medical records for all active-duty US Army soldiers. Multivariate logistic regressions were used to estimate the effects of numerous variables on postoperative injury or on medical discharge. Variable selection and model validation were conducted using the k-fold method. Results: A total of 7567 soldiers underwent knee surgery between 2017 and 2019. Meniscal procedures were the most common type of surgery (39%), and approximately 71% of the cohort had a postoperative injury. Significant predictors for sustaining a postoperative injury included having a previous nonknee injury (odds ratio [OR], 1.5), female sex (OR, 1.3), and Black race (OR, 1.2). Within 4 years after surgery, 17% of soldiers were discharged from the military because of knee-related disability. Significant predictors for discharge from duty included enlisted rank (OR, 2.3), recent fitness test failure (OR, 1.9), number of previous knee surgeries (OR, 1.7), and having a previous nonknee injury (OR, 1.6). Conclusion: After knee surgery, nearly three-fourths of the soldiers in this cohort sustained a postoperative injury and almost one-fifth of soldiers were medically discharged from the military within 4 years. This study identified variables that indicate statistically increased risk for these postoperative outcomes and highlighted potentially modifiable factors.

Orthopedics↗
DOE OSTI2023

Levels of Digitization, Digitalization, and Automation for Advanced Reactors

Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of analog information and is typical of migration from analog to digital control systems, digitalization involves enhancing the functionality of digital information, and automation changes control from humans to machines. Each of these technology implementations has its own scales, and each forms a viable type of functionality that should be considered not as a continuum toward automation but rather as separate categories of solutions that meet the needs of advanced reactors. In this paper we develop separate LODi, LODa, and LOA scales and demonstrate how conflation of these technologies, using the example of computer-based procedures, can lead to confusion in the design process. With the race to develop advanced reactors, the surest metric of success and safety is proper consideration of the right technology requirements for different control systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Prescribed fires, smoke exposure, and hospital utilization among heart failure patients

Abstract Background Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients’ hospital utilization. Methods We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients’ primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI). Results Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies. Conclusions Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires.

Raab, Henry↗