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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Short-Term Stable Isotope Probing of Proteins Reveals Taxa Incorporating Inorganic Carbon in a Hot Spring Microbial Mat

The upper green layer of the chlorophototrophic microbial mats associated with the alkaline siliceous hot springs of Yellowstone National Park consists of oxygenic cyanobacteria ( Synechococcus spp.), anoxygenic Roseiflexus spp., and several other anoxygenic chlorophototrophs. Synechococcus spp. are believed to be the main fixers of inorganic carbon (C i ), but some evidence indicates that Roseiflexus spp. also contribute to inorganic carbon fixation during low-light, anoxic morning periods. Contributions of other phototrophic taxa have not been investigated. In order to follow the pathway of C i incorporation into different taxa, mat samples were incubated with [ 13 C]bicarbonate for 3 h during the early-morning, low-light anoxic period. Extracted proteins were treated with trypsin and analyzed by mass spectrometry, leading to peptide identifications and peptide isotopic profile signatures containing evidence of 13 C label incorporation. A total of 25,483 peptides, corresponding to 7,221 proteins, were identified from spectral features and associated with mat taxa by comparison to metagenomic assembly sequences. A total of 1,417 peptides, derived from 720 proteins, were detectably labeled with 13 C. Most 13 C-labeled peptides were derived from proteins of Synechococcus spp. and Roseiflexus spp. Chaperones and proteins of carbohydrate metabolism were most abundantly labeled. Proteins associated with photosynthesis, C i fixation, and N 2 fixation were also labeled in Synechococcus spp. Importantly, most proteins of the 3-hydroxypropionate bi-cycle for C i fixation in Roseiflexus spp. were labeled, establishing that members of this taxocene contribute to C i fixation. Other taxa showed much lower [ 13 C]bicarbonate incorporation. IMPORTANCE Yellowstone hot spring mats have been studied as natural models for understanding microbial community ecology and as modern analogs of stromatolites, the earliest community fossils on Earth. Stable-isotope probing of proteins (Pro-SIP) permitted short-term interrogation of the taxa that are involved in the important process of light-driven C i fixation in this highly active community and will be useful in linking other metabolic processes to mat taxa. Here, evidence is presented that Roseiflexus spp., which use the 3-hydroxypropionate bi-cycle, are active in C i fixation. Because this pathway imparts a lower degree of selection of isotopically heavy C i than does the Calvin-Benson-Bassham cycle, the results suggest a mechanism to explain why the natural abundance of 13 C in mat biomass is greater than expected if only the latter pathway were involved. Understanding how mat community members influence the 13 C/ 12 C ratios of mat biomass will help geochemists interpret the 13 C/ 12 C ratios of organic carbon in the fossil record.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗
DOE OSTI2024

Constituent Data Replacement Tool

The purpose of this tool is to estimate key parameters that may be missing in public wastewater composition datasets. The tool can be applied to develop complete treatment and critical mineral extraction profiles for leachate, produced water and other aqueous waste streams. The tool applies machine learning algorithms to replace missing data in a user’s water data set that are adjusted based on user preferences for options including algorithm type, number of features, and classification variables. The tool can use the user’s data alone or combine user data with the NEWTS USGS Produced Water Database for more robust training. This research was funded by the U.S. Department of Energy’s Office Fossil Energy and Carbon Management (FECM) through National Energy Technology Laboratory’s ongoing research under the Water Management for Power System Field Work Proposal, DE-FECM 1022428 and Critical Minerals Field Work Proposal, DE-FECM 1022420.

Aqueous Chemistry↗
DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Adhering grains and surface features on two Itokawa particles

We investigated the surface texture and chemical compositions of two ~40-μm particles returned from the surface regolith of asteroid Itokawa (RB-DQ04-0062 and RB-DQ04-0091) by the Japan Aerospace Exploration Agency’s Hayabusa mission. We identified splash melts, surface blistering, and many small adhering particles. Seven focused ion beam sections were extracted from both Itokawa particles, targeting one splash melt and ten adhering particles to investigate their composition and provenance and the role of micrometeoroid impacts on Itokawa’s surface. Based on the particle’s structure, mineralogy, and interface between the adhering particle and host grain, we identified lithic fragments and particles deposited by impact. These have morphologies and compositions consistent with impact-generated deposits: two have morphologies and compositions that are consistent with impact-generated silica glass, and one was a Ni-free, metallic Fe, and S-rich assemblage that was likely generated by vapor recondensation during a micrometeoroid impact. Here this study shows that, even though its regolith is young, micrometeoroid impacts have altered the regolith of asteroid Itokawa.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Confining system integrity assessment by detection of natural gas migration using seismic diffractions [Seismic diffractions for gas leakage detection in the shallow waters of the Gulf of Mexico]

Successful carbon capture and storage (CCS) requires secure CO 2 confinement within a geologic reservoir. If associated with a depleted hydrocarbon reservoir, the sealing capability can be determined by examination of the shallow subsurface for hydrocarbon leaks. Numerous seismic signatures have been reported to be hydrocarbon indicators. The interpretation can be advanced by using seismic diffractions, which could indicate subtle hydrocarbon accumulations not detectable by conventional techniques. In this work, we investigate the potential of seismic diffractions for use in shallow gas detection. We extract diffractions from the ultra-high-resolution 3D P-Cable seismic dataset acquired along the Gulf of Mexico inner continental shelf. Interpretation of this dataset revealed numerous seismic signatures associated with hydrocarbon accumulations (e.g., a prominent gas chimney). We analyze scattering features of the detected hydrocarbon accumulations and confirm the correlation between confidently interpreted gas accumulations and seismic diffractions. Based on that, we suggest using diffractions for confining system integrity assessment. As a result, diffraction analysis allows operating with subtle seismic signals that facilitates exploration of reliable CO 2 storage sites.

58 GEOSCIENCES↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Anomalous Hall effect in ZrTe 5

Research in topological matter has expanded to include the Dirac and Weyl semimetals which feature three-dimensional Dirac states protected by symmetry. Zirconium pentatelluride has been of recent interest as a potential Dirac or Weyl semimetal material. Here, we report the results of experiments performed by in situ three-dimensional double-axis rotation to extract the full 4π solid angular dependence of the transport properties. A clear anomalous Hall effect is detected in every sample studied, with no magnetic ordering observed in the system to the experimental sensitivity of torque magnetometry. Large anomalous Hall signals develop when the magnetic field is rotated in the plane of the stacked quasi-two-dimensional layers, with the values vanishing above about 60 K, where the negative longitudinal magnetoresistance also disappears. Finally, this suggests a close relation in their origins, which we attribute to the Berry curvature generated by the Weyl nodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗
DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗
DOE OSTI2025

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗
DOE OSTI2024

Tractometry of the Human Connectome Project: resources and insights

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Revealing Hidden Quinones Through Diagnostic MS² Fragmentation of Peptide–Quinone Adducts

Quinones are redox-active components of natural organic matter that mediate electron transfer and influence biogeochemical processes, but many quinones in pyrogenic organic matter (PyOM) remain unresolved because they ionize poorly by mass spectrometry. Here, we present a peptide-tagging approach to improve detection of cysteine-reactive electrophiles in PyOM, with quinones expected to be a dominant subset based on reaction chemistry and selectivity experiments. A cysteine-containing peptide was used to form Michael-addition adducts, enhancing electrospray ionization and enabling untargeted screening by high-performance liquid chromatography-high-resolution tandem mass spectrometry. The method was benchmarked with five quinone standards and applied to extracts from charred plant material as a discovery-level screen for cysteine-reactive targets. We identified 98 quinone-candidate adducts (mean neutral mass ~603 Da), of which more than 70% were not detectable in native MS1 data. Among formula-assigned features, hidden quinone candidates had median (O+N)/C of 0.391 and normalized oxidation state of carbon of -0.281, consistent with relatively low polarity and low oxidation state. These results reveal a previously inaccessible pool of hidden redox-active compounds in PyOM and provide a framework for prioritizing quinone-like electrophiles for confirmation and incorporation into models of fire-driven biogeochemical cycling.

LC-MS/MS↗
DOE OSTI2020

Correlations of the physicochemical properties of organosolv lignins from Broussonetia papyrifera with their antioxidant activities

To understand the influence of lignin characteristics on their antioxidant activities, lignins were isolated from Broussonetia papyrifera by organosolv pretreatment at different temperatures. The physicochemical features of the lignins were investigated by GPC, 31 P NMR and 2D HSQC NMR analyses. An increase of organosolv pretreatment temperature resulted in a decrease of M w and an increase of the phenolic hydroxyl group content in the isolated lignin. The lignins extracted at higher organosolv pretreatment temperature showed higher antioxidant activity according to the IC 50 determined using DPPH and ABTS. The correlations between the physicochemical properties of organosolv lignins and their antioxidant activities were studied. The contents of phenolic hydroxyl groups, especially syringyl and guaiacyl, and double bonds in the C α position had positive effects on the antioxidant activity of the lignins. Finally, on the other hand, M w and aliphatic hydroxyl groups of the lignins exhibited negative correlations with the antioxidant activity.

09 BIOMASS FUELS↗
DOE OSTI2019

Density functional theory based neural network force fields from energy decompositions

In order to develop force fields (FF) for molecular dynamics simulations that retain the accuracy of ab initio density functional theory (DFT), in this work we developed a machine learning protocol based on an energy decomposition scheme that extracts atomic energies from DFT calculations. Our DFT to FF (DFT2FF) approach provides almost hundreds of times more data for the DFT energies, which dramatically improves accuracy with less DFT calculations. In addition, we use piecewise cosine basis functions to systematically construct symmetry invariant features into the neural network model. We illustrate this DFT2FF approach for amorphous silicon where only 800 DFT configurations are sufficient to achieve an accuracy of 1 meV/atom for energy and 0.1 eV/A for forces. We then use the resulting FF model to calculate the thermal conductivity of amorphous Si based on long molecular dynamics simulations. The dramatic speedup in training in our DFT2FF protocol allows the adoption of a simulation paradigm where an accurate and problem specific FF for a given physics phenomenon is trained on-the-spot through a quick DFT precalculation and FF training.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

SU-F-J-94: Development of a Plug-in Based Image Analysis Tool for Integration Into Treatment Planning

Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image-based metrics within treatment plan optimization and (ii) evaluation of dose-functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose-functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel-by-voxel basis through API extensions that can access registration transforms. A user interface was designed through WinForms to input parameters and display results. To test the performance of this program, image- and dose-based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug-in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image-based metrics as well as dose-intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image-based metrics, dose-intensity computations, and dose-intensity histograms were applied to analyze the image-dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image-intensity-based metrics in the context of functional targeting and avoidance. In addition to providing dose-intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug-in optimization system, which can directly use the computed metrics for optimization of post-treatment tumor or normal tissue response models. Supported by NIH - P01 - CA059827.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Visualizing broken symmetry and topological defects in a quantum Hall ferromagnet

The interaction between electrons in graphene under high magnetic fields drives the formation of a rich set of quantum Hall ferromagnetic (QHFM) phases with broken spin or valley symmetry. Visualizing atomic-scale electronic wave functions with scanning tunneling spectroscopy (STS), we resolved microscopic signatures of valley ordering in QHFM phases and spectral features of fractional quantum Hall phases of graphene. At charge neutrality, we observed a field-tuned continuous quantum phase transition from a valley-polarized state to an intervalley coherent state, with a Kekulé distortion of its electronic density. Mapping the valley texture extracted from STS measurements of the Kekulé phase, we could visualize valley skyrmion excitations localized near charged defects. Our techniques can be applied to examine valley-ordered phases and their topological excitations in a wide range of materials.

valley symmetry↗
DOE OSTI2017

Study of the Vertical Magnetic Field in Face-on Galaxies Using Faraday Tomography

Faraday tomography allows astronomers to probe the distribution of the magnetic field along the line of sight (LOS), but that can be achieved only after the Faraday spectrum is interpreted. However, the interpretation is not straightforward, mainly because the Faraday spectrum is complicated due to a turbulent magnetic field; it ruins the one-to-one relation between the Faraday depth and the physical depth, and appears as many small-scale features in the Faraday spectrum. In this paper, by employing “simple toy models” for the magnetic field, we describe numerically as well as analytically the characteristic properties of the Faraday spectrum. We show that the Faraday spectrum along “multiple LOSs” can be used to extract the global properties of the magnetic field. Specifically, considering face-on spiral galaxies and modeling turbulent magnetic field as a random field with a single coherence length, we numerically calculate the Faraday spectrum along a number of LOSs and its shape-characterizing parameters, that is, the moments. When multiple LOSs cover a region of ≳(10 coherence length){sup 2}, the shape of the Faraday spectrum becomes smooth and the shape-characterizing parameters are well specified. With the Faraday spectrum constructed as a sum of Gaussian functions with different means and variances, we analytically show that the parameters are expressed in terms of the regular and turbulent components of the LOS magnetic field and the coherence length. We also consider the turbulent magnetic field modeled with a power-law spectrum, and study how the magnetic field is revealed in the Faraday spectrum. Our work suggests a way to obtain information on the magnetic field from a Faraday tomography study.

79 ASTRONOMY AND ASTROPHYSICS↗