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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A pilot oral history of plant synthetic biology

The whole field of synthetic biology (SynBio) is only about 20 years old, and plant SynBio is younger still. Nevertheless, within that short time, SynBio in general has drawn more scientific, philosophical, government, and private-sector interest than anything in biology since the recombinant DNA revolution. Plant SynBio, in particular, is now drawing more and more interest in relation to plants’ potential to help solve planetary problems such as carbon capture and storage and replacing fossil fuels and feedstocks. As plant SynBio is so young and so fast-developing, we felt it was too soon to try to analyze its history. Instead, we set out to capture the essence of plant SynBio's origins and early development through interviews with 8 of the field's founders, representing 5 countries and 3 continents. We then distilled these founders’ personal recollections and reflections into this review, centering the narrative on timelines for pivotal events, articles, funding programs, and quoting from interviews. We have archived the interview recordings and documented timeline entries. This work provides a resource for future historical scholarship.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pharmacokinetics, Metabolite Measurement, and Biomarker Identification of Dermal Exposure to Permethrin Using Accelerator Mass Spectrometry

Impregnating military uniforms and outdoor clothing with the insecticide permethrin is an approach to reduce exposure to insect borne diseases and to repel pests and disease vectors such as mosquitos and sandflies, but the practice exposes wearers to prolonged dermal exposure to the pesticide. Key metabolite(s) from a low dose dermal exposure of permethrin were identified using accelerator mass spectrometry. Metabolite standards were synthesized and a high performance liquide chromatography (HPLC) elution protocol to separate individual metabolites in urine was developed. Six human subjects were exposed dermally on the forearm to 25 mg of permethrin containing 1.0 µCi of 14 C for 8 h. Blood, saliva and urine samples were taken for 7d. Absorption/elimination rates and metabolite concentrations varied by individual. Average absorption was 0.2% of the dose. Serum concentrations rose until 12–24 h postdermal application then rapidly declined reaching predose levels by 72 h. Maximum saliva excretion occurred 6 h postdosing. The maximum urinary excretion rate occurred during 12–24 h; average elimination half-life was 56 h. 3-Phenoxybenzyl alcohol glucuronide was the most abundant metabolite identified when analyzing elution fractions, but most of the radioactivity was in still more polar fractions suggesting extensive degradative metabolism and for which there were no standards. Finally, analyses of archived urine samples with the ultra performance liquid chromatography-accelerator mass spectrometry-mass spectrometry (UPLC-AMS-MS) system isolated a distinct polar metabolite but it was much diminished from the previous analyses a decade earlier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluation of the Radioactive Material Released in the Harborview Research and Training Building and Some Implications for Emergency Response

On 2 May 2019, during the 137 Cs source recovery operation, a source capsule in a research irradiator containing approximately 77.1 TBq was breached. Based on a geometric reconstruction analysis of the damage to the capsule, approximately 46.3 GBq (0.04%) was impacted by the chop saw (grinder) inside a mobile hot cell on the loading dock at the University of Washington Harborview Research and Training (HRT) Building. A very small fraction of the material impacted, less than 1%, was released from the mobile hot cell and then to the rest of the HRT Building. The objectives of this project were to assess the accidental release of 137 CsCl and its implications related to emergency response methods and the ramifications of 137 CsCl transport. The phenomenology of this event was also compared with past alkali halide dispersal events. The vast number of measurements and samples collected by the remediation contractors, the Department of Energy’s Nuclear Emergency Support Team, and the small number of retrospective samples collected by the authors informed the analysis. The techniques included (1) autoradiography and electron microscopy of samples collected from the HRT Building and the irradiator, (2) 3D visualization of deposition on surfaces and within the ventilation system, and (3) a study of the damage to the source capsule to evaluate the Cs particle size and particle composition due to the grinding accident. Subsequently, the cesium contaminant transport through the numerous pathways in the building was reconstructed to assess the deposition on surfaces as a function of particle size. Furthermore, the implications for emergency response are relevant to data quality and management. A Data Quality Objective guides data collection methods so that they have appropriate accuracy and precision for the intended application. Recommendations were made with respect to the sample collection protocols and archiving of samples.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2025

Measurement of the nucleon spin structure functions for 0.01 < 𝑄 2 < 1 GeV 2 using CLAS

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH 3 and ND 3 targets, for 𝑄 2 values as small as 0.012 and 0.02 GeV 2 , respectively, using the CEBAF Large Acceptance Spectrometer. This is the archival paper of the EG4 experiment that summarizes the previously reported results of the polarized structure functions 𝑔 1 , 𝐴 1 ⁢𝐹 1 , and their moments $\bar{Γ}$ 1 , $\bar{𝛾}$ 0 , and $\bar{𝐼}$ TT , for both the proton and the deuteron. In addition, we report on new results on the neutron 𝑔 1 extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\bar{Γ}$ 1 , $\bar{𝛾}$ 0 , and $\bar{𝐼}$ TT formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for 𝑔 1 and the Bjorken integral $\bar{Γ}^{𝑝−𝑛}_1$, and it was compared to available theoretical predictions. All of our results, to the best of our knowledge, provide for the first time extensive tests of spin observable predictions from chiral effective field theory (𝜒⁢EFT) in a 𝑄 2 range commensurate with the pion mass. Finally, they motivate further improvement in 𝜒⁢EFT calculations from other approaches such as the lattice gauge method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Bayesian Optimization of a Free-Electron Laser

The Linac Coherent Light Source X-ray free-electron laser is a complex scienti c apparatus which changes con gurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing X- ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. Furthermore, the result is a sample-e cient optimization routine, combining both historical data and knowledge of accelerator physics to signi cantly outperform existing optimizers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Direct Detection of Hawking Radiation from Asteroid-Mass Primordial Black Holes

We discuss light, asteroid-mass primordial black holes, with lifetimes in the range between hundreds to several millions times the age of the Universe, are well-motivated candidates for the cosmological dark matter. Using archival COMPTEL data, we improve over current constraints on the allowed parameter space of primordial black holes as dark matter by studying their evaporation to soft gamma rays in nearby astrophysical structures. We point out that a new generation of proposed MeV gamma-ray telescopes will offer the unique opportunity to directly detect Hawking evaporation from observations of nearby dark matter dense regions and to constrain, or discover, the primordial black hole dark matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Deep Search for Decaying Dark Matter with XMM-Newton Blank-Sky Observations

Sterile neutrinos with masses in the keV range are well-motivated extensions to the Standard Model that could explain the observed neutrino masses while also making up the dark matter (DM) of the universe. If sterile neutrinos are DM then they may slowly decay into active neutrinos and photons, giving rise to the possibility of their detection through narrow spectral features in astrophysical x-ray data sets. Here, we perform the most sensitive search to date for this and other decaying DM scenarios across the mass range from 5 to 16 keV using archival XMM-Newton data. We reduce 547 Ms of data from both the MOS and PN instruments using observations taken across the full sky and then use this data to search for evidence of DM decay in the ambient halo of the Milky Way. We determine the instrumental and astrophysical baselines with data taken far away from the Galactic Center, and use Gaussian process modeling to capture additional continuum background contributions. No evidence is found for unassociated x-ray lines, leading us to produce the strongest constraints to date on decaying DM in this mass range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Resurrecting Hitomi for Decaying Dark Matter and Forecasting Leading Sensitivity for XRISM

The Hitomi x-ray satellite mission carried unique high-resolution spectrometers that were set to revolutionize the search for sterile neutrino dark matter (DM) by looking for narrow x-ray lines arising from DM decays. Unfortunately, the satellite was lost shortly after launch, and to date the only analysis using Hitomi for DM decay used data taken towards the Perseus cluster. Here, in this work we present a significantly more sensitive search from an analysis of archival Hitomi data towards blank sky locations, searching for DM decaying in our own Milky Way. The recently launched XRISM satellite has nearly identical soft-x-ray spectral capabilities to Hitomi; we project the full-mission sensitivity of XRISM for analyses of their future blank-sky data, and we find that XRISM will have the leading sensitivity to decaying DM for masses between roughly 1 to 18 keV, with important implications for sterile neutrino and heavy axionlike particle DM scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Announcing the launch of Protein Data Bank China as an Associate Member of the Worldwide Protein Data Bank Partnership

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Clustering-Based Predictive Analytics to Improve Scientific Data Discovery

Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.

Devarakonda, Ranjeet↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
DOE OSTI2024

Machine Learning (ML) Classifier to Assist Metadata Creation

The Atmospheric Radiation Measurement (ARM) Data Center is responsible for the timely collection, archival, and curation of science data products. These products are freely available through an online data repository. Metadata creation is paramount for scientific users to find and access over seven petabytes of atmospheric science data. The hierarchical metadata structure allows users to search for information at both broad and narrow levels. This project aims to leverage 30 years’ worth of manually created metadata to enable machine predictions of broad-term classifications from narrow-term descriptions. These classification predictions would assist metadata coordinators with their term selections. This paper discusses the cleaning and preprocessing of the training data, the pipeline developed to determine the best model for this task, and the creation of an API metadata classifier for ARM measurement metadata. Our results show that the Linear Support Vector Classification (LinearSVC) algorithm, along with the Term Frequency – Inverse Document Frequency (TF-IDF) vectorizer, is well-suited for our multi-class classification task. Lengthier input training data led to better results, and artificial balancing was unnecessary for this particular use case. This predictive classifier enhances efficiency in metadata creation, as well as supports greater consistency and accuracy in metadata tagging.

Collier, Hannah [ORNL] (ORCID:0000000341284292)↗
DOE OSTI2021

A Conceptual Framework for HPC Operational Data Analytics

This paper provides a broad framework for under- standing trends in Operational Data Analytics (ODA) for High- Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four- pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading- edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.

Netti, Alessio↗
DOE OSTI2020

Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.

Ott, Michael↗
DOE OSTI2021

Educating HPC Users in the use of advanced computing technology

We examine a multi-modal approach to educating and training users of an advanced computing technology testbed at the Institute for Advanced Computational Science at Stony Brook University. Ookami provides researchers worldwide with access to 176 Fujitsu A64FX compute nodes, this being the same processor technology powering the Japanese Fugaku supercomputer, the fastest computer in the world since June 2020. However, achieving high-performance on this Arm-based, leadership computing technology requires that users be familiar with details of computer architecture, performance analysis and modeling, and high-performance programming models that are commonly omitted in introductory programming courses. Indeed, regardless of their seniority, many of the testbed users are surprisingly unfamiliar with basic concepts such as vectorization, pipelining, latency/bandwidth, roofline models, computing energy/power, threads, and non-uniform memory access. These same concepts also pervade mainstream x86 technologies, so this is of widespread concern. Due to the national/global nature of our user community that is also very diverse in both discipline and experience, the inability to offer formal classes, and our experience that most people do not tend to read online documentation or training materials in sufficient depth, we have consciously employed multiple approaches that heavily emphasize (online) personal interactions and transfer of skills. Online documentation has been organized around best-practices and FAQs; twice-weekly hackathons and office hours via Zoom enable deep dives by both the team and the user community with multiple broad benefits; a Slack channel provides both real time and archived answers and discussions; and workshops, training and webinars target community needs as they arise. Furthermore, the perspective that these tools are being used in an educational setting rather than just for project communication makes them more effective and contributes to community success.

A64FX↗