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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Host Onboarding Tool (HObT) v1.0.0

The Host OnBoarding Tool (Hobt) is a publicly accessible, web-based software designed to organize and share information about microbial hosts under development at the Agile BioFoundry (ABF). It streamlines the assessment, tracking, and sharing of information related to microbial host development and provides a centralized platform where users can rapidly evaluate hosts' readiness for various bio processes. HObT leverages the Tier System, a standardized host development framework that organizes and assesses microbial hosts based on their readiness for biomanufacturing. Each tier outlines key targets—including genetic tools, growth conditions, omics data, and predictive models—needed to transform new or emerging microbes into established production platforms. By applying clear criteria for advancement, the Tier System helps users quickly evaluate each organism's current development status, identify gaps in available knowledge or tools, and prioritize future strain improvement efforts. Through its user-friendly interface, HObT encourages contributions of new data and insights from researchers, fostering collaboration and accelerating host development. By providing structured guidance for microbial strain advancement, HObT and the Tier System support more systematic, rapid, and cost-effective development of non-traditional microbial hosts, ultimately enhancing the efficiency and impact of biomanufacturing research and applications.

Plahar, Hector [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Tracking the protein conformational motions driving HIV-1 membrane fusion

HIV-1 Env (trimeric gp120/gp41) is the surface protein responsible for membrane fusion. The Env binds to the receptor proteins, which induces gp120 shedding leading to conformational changes of gp41 from the pre-fusion to post-fusion state, allowing its fusion peptide to embed in the host cell membrane and bringing the viral and host cell membranes together. The gp41 refolding is a target of several peptide inhibitors. Yet, the molecular mechanism of this dynamic process is still not well understood. In this study, we successfully simulate the conformational change of gp41 from pre-fusion to post-fusion state in atomistic resolution using all-atom structure-based models. We reveal that maintaining the directionality of protomer interactions in both pre-fusion and post-fusion states is crucial for gp41 refolding. Additionally, we find that HR1 inherently extends as a three-helical bundle toward the host-cell membrane without any bias. Importantly, we identify native contacts in the pre-fusion state that are critical for the proper refolding of gp41 towards the post-fusion state. Lastly, by incorporating the membrane-fusion inhibitors, T20 and SFT, we identify the most vulnerable stage in the fusion pathway that exhibits the greatest sensitivity to these drugs, which could aid in a better understanding of drug resistance mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Angular Dependence of Drell-Yan in the SeaQuest Experiment using Deep Neural Network-Based Reconstruction

The SeaQuest and SpinQuest experiments at Fermilab were designed to probe the internal dynamics of protons and neutrons via the angular dependence of muons created by colliding 120 GeV protons into stationary targets. To do this, it is necessary to translate the detector data into coherent information about the particles detected in the experiment. This dissertation describes the development and implementation of QTracker, a neural network-based algorithm designed to reconstruct muons. The application of QTracker to experimental data yields improved reconstruction of muon tracks, allowing for a more precise investigation of the transverse momentum distributions and angular modulations in the Drell-Yan process. This work highlights the potential of neural network-based methods to advance particle tracking and enhance our understanding of nucleon structure.

Conover, Arthur↗
DOE OSTI2025

Studies of Quark Transport and Hadronization in Nuclei

In this project, we conducted the first measurement of di‑hadron azimuthal correlations in deep inelastic scattering (DIS) off nuclei using the CLAS detector at Jefferson Lab. Using 5 GeV electron‑beam data collected on deuterium, carbon, iron, and lead targets, we extracted di‑pion correlation functions over a broad kinematic range. The results show a monotonic broadening of the correlation peak with increasing nuclear mass, along with pronounced dependencies on the pions’ kinematics. Separately, we implemented an algorithm based on the Kalman filter that achieved the first complete alignment of the CLAS12 central tracking system. In parallel, we developed simulations, algorithms, and performance studies that informed the conceptual designs of the forward hadronic calorimeter Insert and the Zero Degree Calorimeter, both of which are now included in the ePIC detector baseline for the forthcoming Electron Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

What next: Further implosion space exploration on the path to NIF extended yield capability

We present quantitative motivations and assessments of various proposed and ongoing directions to further improving yields and target gain of igniting indirect-drive implosions at the National Ignition Facility (NIF). These include increasing compression and confinement time, improving hohlraum and ablator efficiency, and further increasing peak power and laser energy. 1D hydroscaled simulations, augmented by analytic 1D theory, have been used to project yield improvements for each of these implosion optimization tracks, normalized to the best current performing 4 MJ shot. At current NIF capabilities of 2.2 MJ, 450 TW, we project several paths could reach 15 MJ yield levels. We also expect several key implosion physics questions will be addressed in attempting to reach this yield level. These include demonstrating to what extent lower adiabat designs leading to higher compression will increase gain and efficiency, and whether we can reduce residual kinetic energy and ablator-fuel mix that is probably limiting the current burn-up fraction. For an envisaged NIF upgrade to EL = 3 MJ at fixed 450 TW peak power, scaling capsule size and fuel thicknesses faster than pure hydroscaling should allow for yields that could reach up to 60–80 MJ, depending on the efficiency gains realized in increasing deuterium-tritium fuel thickness, reducing hohlraum losses, and switching to lower Z ablators. The laser-plasma instability and beam transmission scaling in these larger hohlraums is shown to be favorable if the spot size is increased with hohlraum scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Developing a data-driven method to constrain the antiproton background in the Mu2e experiment

The Mu2e experiment will search for CLFV neutrinoless coherent muon to electron conversion in the field of an Al nucleus. The expected signal is a 104.97 MeV/c monochromatic $e^-$ (CE). CE-like $e^-$’s could also come from $\bar{p}$’s annihilating in the Stopping Target (ST). The background induced by $\bar{p}$’s is expected to be low but has a large systematic uncertainty. It cannot be suppressed by the time window cut used to reduce the prompt background. However, $p\bar{p}$ annihilation in the ST is the only source of events in the Mu2e detector with multiple tracks coming from the ST, simultaneous in time, each with a momentum in the signal window region. We exploited this unique feature and developed algorithms to identify and reconstruct multi-track events. This paper discusses the status and prospects of this data-driven method to constrain the $\bar{p}$ background at Mu2e.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Vortex rings in event-by-event relativistic heavy-ion collisions

We present event-by-event simulations for central asymmetric light+heavy and Au +Au collisions to investigate the formation and evolution of vortex-ring structures in the longitudinal flow velocity profile. The production-plane polarization of Λ hyperons, defined with respect to the Λ momentum and the beam, can track the “vortex-ring” feature in the event, a characteristic vortical structure generated by longitudinal flow gradients. We make comprehensive model predictions for the rapidity-dependent vortex-ring observables for different collision system sizes at √s NN = 200 and 72 GeV. Furthermore, our predictions at the latter energy can be explored in the future LHCb fixed-target experiment at the Large Hadron Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Advances in building data management for building performance standards using the SEED platform

Reducing energy consumption and greenhouse gas emissions in the built environment is a critical step in achieving emission goals to mitigate climate change impacts. Local, federal, and international jurisdictions are deploying several methods to reduce energy and emissions such as voluntary and mandatory benchmarking and building performance standards, requiring building owners to reach energy and emission targets. Jurisdictions leveraging benchmarking and building performance standards require knowledge of the buildings covered; which is a large task due to staffing constraints, limited information on building characteristics and tax parcel data, and the need for advanced data management techniques to align datasets. This paper describes an open-source platform's recent advances to create consistent taxonomies, identify erroneous data, enable auditability, and track building performance. The paper concludes with two use cases on how the platform has been used by jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Wind lidar operations and observations from Tundra Pigeon

Lawrence Livermore National Laboratory brought a ZX-300 profiling lidar to the Tundra Pigeon experiment for the purpose of collecting wind measurements in the lower atmospheric boundary layer. The ZX-300 lidar is a portable Doppler lidar which uses light to track naturally occurring aerosols across a measurement cone, thereby deriving wind speed (horizontal and vertical) and wind direction. The ZX-300 is programable between the heights of 10 m and 300 m above ground level and additionally has a 1 m onboard meteorological sensor for collecting measurements of air temperature, relative humidity, air pressure and wind at 1 m height. We programmed the ZX-300 to target altitudes of most interest to the tracer experiment. These heights were 10, 15, 20, 30, 38, 50, 75, 100, 125, 150 and 200 m. Note that the 38 m level is a fixed calibration height and cannot be changed. This measurement strategy prioritized winds close to the surface while ensuring that we’d also collect information about the winds aloft. The wind measurements are collected directly over the lidar instrument and represent a vertical profile of the winds over that location. However, given that there were minimal terrain and vegetation differences in the area, we’d expect these wind conditions to be representative of a larger volume area, discussed in more detail below.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Pu-238 Production Progress at Idaho National Laboratory From December 2023 to November 2024

Idaho National Laboratory (INL) has continued in the effort to produce Pu-238 to support NASA deep space missions. In support of the production goal of contributing 700 g to the 1.5 kg per year constant rate production of Pu-238 heat source material in the United States by 2026. All available inner core positions of the Advanced Test Reactor (ATR) have been qualified for production for a 20% Np target, with efforts nearing completion for a 30% Np target further increasing production amounts. This includes the North East Flux Trap (NEFT), South Flux Trap (SFT), Inner A, H positions. With the completion of qualifying the inner core of ATR, work has started on the qualifying the outer core. The portion of the outer core that will produce Pu-238 includes the Large and Medium I positions. With the continued qualification work, INL is on track to contribute to the production goal by 2026.

07 - ISOTOPES AND RADIATION SOURCES↗
DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗
DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Analysis of the electrical double layer using electrochemical X-ray photoelectron spectroscopy

The element-sensitivity of X-ray spectroscopies offers the potential to disentangle the individual chemistries of water, ions, and adsorbates at the electrode-electrolyte interface in an element-by-element manner. However, targeted experimental design is needed to establish interface-sensitive in situ X-ray spectroscopy in a realistic electrochemical environment. Here, we demonstrate how electrochemical X-ray photoelectron spectroscopy (EC-XPS) in the dip-and-pull geometry can be used to specifically probe the behavior of ions in the electrical double layer. Taking the case study of a polycrystalline Au foil in 50 mM KClO 4 electrolyte, we tracked the electrochemical response of interfacial K + cations across a broad potential range. We show how, in combination with modeling, key parameters such as the potential of zero charge (PZC), ion packing behavior, dielectric saturation, and the electrostatic potential decay in the double layer can be extracted from the data. Importantly, we also analyze how the experimental conditions and non-idealities can influence the results and put forward criteria for reliable experimentation and data analysis.

Dip-and-pull↗
DOE OSTI2024

Studies of e+e- Pair Photo-Production on Proton Target at 8 GeV in the GlueX Experiment

Lepton pair production has played an important role in both nuclear and particle physics, being among the earliest calculations utilizing QED, and seen most famously in the discovery of the J= at BNL. A technique is presented for measuring the linear polarization of GeV scale photon beams through the detection of e+e? pairs photo-produced in the target. This technique is applied to the analysis of GlueX data on proton target. Simulation predicts the analyzing power for pair production to be :5725 ? 0:0025 for the GlueX data, and the analysis of experimental data gives a linear polarization of approximately 35%, in good agreement with other measurements of beam polarization. The pair production technique is complementary to other electromagnetic and hadronic measurements of beam polarization, and is generally applicable in experiments that allow for forward angle electron and positron identi?fication and tracking. To facilitate this study, a neural net was trained for e=? separation to eliminate the pion background. Further, it is demonstrated that these e+e? pairs are sensitive to the proton charge form factor, which opens up the possibility for a new method to measure the proton RMS charge radius.

Schick, Andrew↗
DOE OSTI2025

γIn-beam angular distribution and linear polarization measurements with GRETINA using a simple energy-ordering approach

Angular distribution and linear polarization measurements are powerful tools for inferring the spins and parities of nuclear levels. In this work, the performance of the Gamma-Ray Energy Tracking In-beam Nuclear Array (GRETINA) as a Compton polarimeter was characterized in a fusion-evaporation reaction experiment using a simple energy-ordering approach for the interaction points assigned in the signal decomposition process. A variety of multipolarities and characters for γ-ray transitions in the reaction products 25 Mg, 25 Na, and 22 Ne, formed from fusion-evaporation of an 18 O beam on a 9 Be target, were examined. The experimental angular distributions and linear polarization asymmetries were consistent with predictions using the theoretical formalism accounting for the Lorentz boost.

Angular distribution↗
DOE OSTI2024

Performance Analysis of Phase 2 Tracker Upgrade Ps Module before and after Irradiation

The Large Hadron Collider will undergo a luminosity upgrade targeting a peak instantaneous luminosity ranging from 5–7.5×1034 cm−2s−1. The ambitious goal of the High Luminosity LHC is to achieve a total of 3000–4000 fb−1 of proton-proton collisions at a center-of-mass energy of 13–14 TeV by 2029. To cope with such challenging environmental conditions, the outer tracker of the CMS experiment will be upgraded using closely spaced silicon sensors (pixels and strips) to provide tracking information at the Level-1 trigger. A PS-Module, composed of both a pixel and a strip sensor, was tested at the Fermilab Test-Beam Facility to evaluate its ability to provide accurate tracking information, Pt discrimination capabilities, and optimal performance at the irradiation levels expected after being exposed to the harsh conditions of the High Luminosity LHC. The results of the test and the comparison of the module performance before and after irradiation will be presented in this poster

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗