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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Integrating Hybrid-Clustering and Localized Regression for Time Synchronization of a Hierarchical Underwater Acoustic Sensor Array

Time synchronization is a critical requirement for the application of underwater acoustic sensor network (UWSN). Although a number of time synchronization protocols have been proposed for UWSN, none of them can be directly applied to autonomous acoustic receivers due to lack of hardware platform to communicate with each other. In this paper, we propose a machine learning-based time synchronization framework for autonomous receiver arrays, using the Juvenile Salmon Acoustic Telemetry System as a case study. The proposed framework consists of array partition and time synchronization. Using detections of receiver attached beacons as input, this framework synchronizes all receiver clocks to a root receiver clock. The framework has been successfully used in a field study at Trevallyn Dam forebay in Tasmania, Australia.

Fu, Tao↗
DOE OSTI2025

Confocal Raman Microscopy as a Probe of Material Deconstruction in Processed Low-Density Polyethylene Particles

Confocal Raman microscopy was applied to detect structural change within individual particles of low-density polyethylene (LDPE) following chemical and electrochemical processing steps that aimed to facilitate material decomposition. A high numerical aperture (NA) oil-immersion objective enabled depth-profiling through the near surface region (20 μm–40 μm) of irregularly shaped particles with an axial spatial resolution < 2 μm estimated from measurements of instrument detection efficiency profiles. Changes in vibrational bands sensitive to polyethylene crystallinity were evident following treatments and linked to the release of low molecular weight compounds present as additives and products of processing. Effects of processing were probed by monitoring the rise of Raman scattering intensity in vibrational modes associated with polyethylene chains in a zig-zag (trans) conformation near 1128 cm –1 , 1294 cm –1 , and 1418 cm –1 , signaling chain clustering and development of organized, crystalline-like assemblies. Pristine LDPE particles displayed a uniform structure across the near surface region, while particles treated initially with chemical extractant and then further processed displayed increasingly enhanced crystallinity up to the maximum depth probed (40 μm). As a step toward measurements on ensembles of particles, least squares modeling was adapted to derive pure component spectra reflecting crystallinity change within spectral datasets. The work demonstrates high spatial resolution Raman depth-profiling for the characterization of processed polymers using a high NA immersion objective to overcome the limitations of air-objectives often used for confocal Raman microscopy.

Wahiduzzaman, Md. [Department of Chemistry and Bio↗
DOE OSTI2021

Astrometric Membership Tests for the Zinn–Newell–Gibson UV-bright Stars in Galactic Globular Clusters

In 1972, Zinn, Newell, & Gibson (ZNG) published a list of 156 candidate UV-bright stars they had found in 27 Galactic globular clusters (GCs), based on photographs in the U and V bands. UV-bright stars lie above the horizontal branch (HB) and blueward of the asymptotic giant branch (AGB) and red giant branch in the clusters’ color–magnitude diagrams. They are in rapid evolutionary phases—if they are members and not unrelated bright foreground stars. The ZNG list has inspired numerous follow-up studies, aimed at understanding late stages of stellar evolution. However, the ZNG candidates were presented only in finding charts, and celestial coordinates were not given. Using my own collection of CCD frames in u and V, I have identified all of the ZNG objects, and have assembled their coordinates, parallaxes, and proper motions from the recent Gaia Early Data Release 3 (EDR3). Based on the Gaia astrometry, I have determined which objects are probable cluster members (45% of the sample). For the members, using photometry from EDR3, I have assigned the stars to various evolutionary stages, including luminous post-AGB stars, and stars above the HB. I point out several ZNG stars of special interest that have still, to my knowledge, never been studied in detail. This study is an adjunct to a forthcoming survey of the Galactic GCs in the uBVI photometric system, designed for detection of low-gravity stars with large Balmer discontinuities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

``Beads on a String" Star Formation Tied to one of the most Powerful AGN Outbursts Observed in a Cool Core Galaxy Cluster

With two central galaxies engaged in a major merger and a remarkable chain of 19 young stellar superclusters wound around them in projection, the galaxy cluster SDSS J1531+3414 ($z=0.335$) offers an excellent laboratory to study the interplay between mergers, AGN feedback, and star formation. New Chandra X-ray imaging reveals rapidly cooling hot ($T\sim 10^6$ K) intracluster gas, with two "wings" forming a concave density discontinuity near the edge of the cool core. LOFAR $144$ MHz observations uncover diffuse radio emission strikingly aligned with the "wings," suggesting that the "wings" are actually the opening to a giant X-ray supercavity. The steep radio emission is likely an ancient relic of one of the most energetic AGN outbursts observed, with $4pV > 10^{61}$ erg. To the north of the supercavity, GMOS detects warm ($T\sim 10^4$ K) ionized gas that enshrouds the stellar superclusters but is redshifted up to $+ 800$ km s$^{-1}$ with respect to the southern central galaxy. ALMA detects a similarly redshifted $\sim 10^{10}$ M$_\odot$ reservoir of cold ($T\sim 10^2$ K) molecular gas, but it is offset from the young stars by $\sim 1{-}3$ kpc. We propose that the multiphase gas originated from low-entropy gas entrained by the X-ray supercavity, attribute the offset between the young stars and the molecular gas to turbulent intracluster gas motions, and suggest that tidal interactions stimulated the "beads on a string" star formation morphology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗
DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗
DOE OSTI2020

Separation of overlapping sources in bioacoustic mixtures

Source separation is an important step to study signals that are not easy or possible to record individually. Common methods such as deep clustering, however, cannot be applied to signals of an unknown number of sources and/or signals that overlap in time and/or frequency—a common problem in bioacoustic recordings. This work presents an approach, using a supervised learning framework, to parse individual sources from a spectrogram of a mixture that contains a variable number of overlapping sources. This method isolates individual sources in the time-frequency domain using only one function but in two separate steps, one for the detection of the number of sources and corresponding bounding boxes, and a second step for the segmentation in which masks of individual sounds are extracted. This approach handles the full separation of overlapping sources in both time and frequency using deep neural networks in an applicable manner to other tasks such as bird audio detection. This paper presents method and reports on its performance to parse individual bat signals from recordings containing hundreds of overlapping bat echolocation signals. This method can be extended to other bioacoustic recordings with a variable number of sources and signals that overlap in time and/or frequency.

Izadi, Mohammad Rasool↗
DOE OSTI2024

Using low volume eDNA methods to sample pelagic marine animal assemblages

Environmental DNA (eDNA) is an increasingly useful method for detecting pelagic animals in the ocean but typically requires large water volumes to sample diverse assemblages. Ship-based pelagic sampling programs that could implement eDNA methods generally have restrictive water budgets. Studies that quantify how eDNA methods perform on low water volumes in the ocean are limited, especially in deep-sea habitats with low animal biomass and poorly described species assemblages. Using 12S rRNA and COI gene primers, we quantified assemblages comprised of micronekton, coastal forage fishes, and zooplankton from low volume eDNA seawater samples (n = 436, 380–1800 mL) collected at depths of 0–2200 m in the southern California Current. We compared diversity in eDNA samples to concurrently collected pelagic trawl samples (n = 27), detecting a higher diversity of vertebrate and invertebrate groups in the eDNA samples. Differences in assemblage composition could be explained by variability in size-selectivity among methods and DNA primer suitability across taxonomic groups. The number of reads and amplicon sequences variants (ASVs) did not vary substantially among shallow (<200 m) and deep samples (>600 m), but the proportion of invertebrate ASVs that could be assigned a species-level identification decreased with sampling depth. Using hierarchical clustering, we resolved horizontal and vertical variability in marine animal assemblages from samples characterized by a relatively low diversity of ecologically important species. Low volume eDNA samples will quantify greater taxonomic diversity as reference libraries, especially for deep-dwelling invertebrate species, continue to expand.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Thermal, RGB, and Multispectral Unexploded Ordnance Data Collection

As of May 1, 2026 Landmine contamination affects 58 countries and many hundreds of thousands of km² of land. For example, the National Mine Action Program Demining Ukraine reporting that up to 144,000 km² of territory are potentially contaminated and require survey and clearance (National Mine Action Program “Demining Ukraine,” n.d.) alone. This contamination includes mines and other explosive remnants of war and continues to constrain civilian access, agricultural use, infrastructure recovery, and broader socioeconomic activity (International Campaign to Ban Landmines–Cluster Munition Coalition [ICBL-CMC], 2024; Mine Action Review, 2024). Current response activities rely on established mine-action approaches including non-technical survey, technical survey, clearance, and explosive ordnance disposal, consistent with international mine-action terminology and operational practice (United Nations Mine Action Service [UNMAS], 2024; Geneva International Centre for Humanitarian Demining [GICHD], 2023). In this context, UAV-based sensing, including UAV-mounted thermal imaging, may provide a useful supplementary capability by supporting faster, safer detection and mapping of suspect hazards prior to ground intervention (Smiljanic, 2022).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Formation and detection of metastable formic acid in a supersonic expansion: High resolution infrared spectroscopy of the jet-cooled cis -HCOOH conformer

High-resolution direct absorption infrared spectra of metastable cis-formic acid (HCOOH) trapped in a cis-well resonance behind a 15 kcal/mol barrier are reported for the first time, with the energetically unstable conformer produced in a supersonic slit plasma expansion of trans-formic acid/H 2 mixtures. We present a detailed high-resolution rovibrational analysis for cis-formic acid species in the OH stretch (ν 1 ) fundamental, providing first precision vibrational band origin, rotational constants, and term values, which in conjunction with ab initio calculations at the couple-cluster with single, double, and perturbative triple [CCSD(T)]/ANOn (n = 0, 1, 2) level support the experimental assignments and establish critical points on the potential energy surface for internal rotor trans-to-cis isomerization. Relative intensities for a- and b-type transitions observed in the spectra permit the transition dipole moment components to be determined in the body fixed frame and prove to be in good agreement with ab initio CCSD(T) theoretical estimates but in poor agreement with simple bond-dipole predictions. As a result, the observed signal dependence on H 2 in the discharge suggests the presence of a novel H atom radical chemical mechanism for strongly endothermic “up-hill” internal rotor isomerization between trans- and cis-formic acid conformers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Controlling morphology in hybrid isotropic/patchy particle assemblies

Brownian dynamics is used to study self-assembly in a hybrid system of isotropic particles (IPs), combined with anisotropic building blocks that represent special “designer particles.” Those are modeled as spherical patchy particles (PPs) with binding only allowed between their patches and IPs. In this study, two types of PPs are considered: Octahedral PPs (Oh-PPs) and Square PPs (Sq-PPs), with octahedral and square arrangements of patches, respectively. The self-assembly is additionally facilitated by the simulated annealing procedure. Here, the resultant structures are characterized by a combination of local correlations in cubatic ordering and a symmetry-specific variation of bond orientation order parameters (SymBOPs). By varying the PP/IP size ratio, we detected a sharp crossover between two distinct morphologies in both types of systems. High symmetry phases, NaCl crystal for Oh-PP and square lattice for Sq-PP, are observed for larger size ratios. For the smaller ones, the dominant morphologies are significantly different, e.g., Oh-PPs form a compact amorphous structure with predominantly face-to-face orientation of neighboring PPs. Unusually, for a morphology without a long-range order, it is still possible to identify well organized coherent clusters of this structure, thanks to the adoption of our SymBOP-based characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Investigation of the isoscalar monopole response in the proton-rich nucleus 14 O

Deuteron inelastic scattering on 14 O was measured in inverse kinematics using an active-target time projection chamber and a magnetic spectrograph. The experimental technique enabled precise measurements of deuteron recoiling particles in coincidence with beam-like fragments detected in the spectrograph focal plane. The double differential cross section was reconstructed for scattering angles of 3–6 degrees and excitation energies up to 26 MeV. The monopole strength distribution was obtained from the data using a multipole decomposition analysis. The results were compared to quasiparticle random-phase approximation (QRPA) and generator coordinate method (GCM) calculations. The QRPA calculation accurately describes experimental data in the energy range of 13 to 26 MeV. GCM calculations assuming a 12 C (g . s .) + p + p cluster configuration were used to determine the 0 + strength in 14 O below 13 MeV. The monopole transition strength of these cluster states provides a good description of the experimental distribution in the 9–11 MeV region, while the 0$^{+}_{2}$ state accounts for only a small fraction of the experimental strength around 6 MeV.

Active target↗
DOE OSTI2026

The SPT-3G+ Experiment on the South Pole Telescope

Observations of the cosmic microwave background (CMB) offer an unparalleled opportunity to advance our understanding of fundamental physics. SPT-3G+ is an upgraded receiver for the arcminute-resolution South Pole Telescope (SPT) that plans to deploy in late 2028. SPT-3G+ will increase the CMB mapping speed of SPT by nearly an order of magnitude over the currently installed SPT-3G receiver. SPT-3G+ will have ~24,000 transition-edge sensor (TES) bolometers in two frequency bands with center frequencies at 95 GHz and 150 GHz that will be read out with microwave multiplexing. SPT-3G+ will measure the CMB lensing spectrum and galaxy clusters to constrain the growth of structure, dark matter, and dark energy. SPT-3G+ will also reach critical thresholds on inflationary constraints by combining data with BICEP/Keck, forming the South Pole Observatory (SPO). BICEP/Keck has deep degree-angular scale measurements but is currently delensing-limited, while SPT-3G+ will provide deep lensing measurements. Forecasts show that SPO will reach an uncertainty on the tensor-to-scalar ratio $r$ of $\sigma(r) \sim 1.2\times 10^{-3}$ by 2034. A detection at these levels would provide evidence of inflation and probe new physics at grand unified theory energy scales, while no detection would exclude large classes of models and shift the scientific paradigm describing the early universe. I will give an overview of SPT-3G+ including its design and current status.

Simon, Sara M. [Fermilab] (ORCID:0009000006683584)↗
DOE OSTI2026

Unsupervised Detection of SOC Spoofing in OCPP 2.0.1 EV Charging Communication Protocol Using One-Class SVM

The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2.0.1 supports the communication between the Electric Vehicle Supply Equipment (EVSE) and Charging Station Management Systems (CSMSs); therefore, it becomes vulnerable to several types of attacks, which aim to jeopardize smart charging, billing, and energy management. Specifically, OCPP 2.0.1 allows the self-reporting of the State of Charge (SOC) values, which makes it vulnerable to spoofing-based cyberattacks, which target manipulating the scheduling priorities, distorting the load forecasts, and extending the charging sessions in an unfair manner. In this paper, we try to address this type of attack by providing a comprehensive analysis of the SOC spoofing attacks and introducing a novel unsupervised detection framework based on the One-Class Support Vector Machine (OCSVM) algorithm. Specifically, two types of attack scenarios are analyzed (i.e., priority manipulation and session extension) by deriving engineered features that capture the nonlinear relationships under normal charging behavior. Detailed simulation-based results are derived by utilizing the DESL-EPFL Level 3 EV charging dataset. Our results demonstrate high F1-score and recall in identifying spoofed SOC values and that the proposed OCSVM model demonstrates superior performance compared to alternative clustering and deep-learning based detectors.

EV charging↗
DOE OSTI2021

Flame stability analysis of flame spray pyrolysis by artificial intelligence

Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles through the combustion of an atomized precursor solution; this process has applications in catalysts, battery materials, and pigments. Current limitations revolve around understanding how to consistently achieve a stable flame and the reliable production of nanoparticles. Machine learning and artificial intelligence algorithms that detect unstable flame conditions in real time may be a means of streamlining the synthesis process and improving FSP efficiency. In this study, the FSP flame stability is first quantified by analyzing the brightness of the flame's anchor point. This analysis is then used to label data for both unsupervised and supervised machine learning approaches. The unsupervised learning approach allows for autonomous labeling and classification of new data by representing data in a reduced dimensional space and identifying combinations of features that most effectively cluster it. The supervised learning approach, on the other hand, requires human labeling of training and test data but is able to classify multiple objects of interest (such as the burner and pilot flames) within the video feed. The accuracy of each of these techniques is compared against the evaluations of human experts. Both the unsupervised and supervised approaches can track and classify FSP flame conditions in real time to alert users of unstable flame conditions. This research has the potential to autonomously track and manage flame spray pyrolysis as well as other flame technologies by monitoring and classifying the flame stability.

42 ENGINEERING↗
DOE OSTI2022

Revisiting constraints on asymmetric dark matter from collapse in white dwarf stars

The runaway collapse phase of a small dark matter cluster inside a white dwarf star encompasses a reversible stage, where heat can be transferred back and forth between nuclear and dark matter. Induced nuclear burning phases are stable and early carbon depletion undermines previous claims of type Ia supernova ignition. Instead, mini black holes are formed at the center of the star that either evaporate or accrete stellar material until a macroscopic sub-Chandrasekhar-mass black hole is formed. In the latter case, a 0.1 to 1 second lasting electromagnetic transient signal can be detected upon ejection of the white dwarf’s potential magnetic field. Binary systems that transmute to black holes and subsequently merge emit gravitational waves. Advanced LIGO should detect one such sub-Chandrasekhar binary black hole inspiral per year, while future Einstein telescope-like facilities will detect thousands per year. The effective spin parameter distribution is peaked at 0.2 and permits future studies to disentangle from primordial sub-Chandrasekhar black holes. Such signatures are compatible with current direct detection constraints, as well as with neutron star constraints in the case of bosonic dark matter, even though they remain in conflict with the fermionic case for part of the parameter space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multiplex Detection of Multiple-Quantum/Single-Quantum NMR Correlation Spectra

High-ordered multiple-quantum coherences have been used to gauge the size of coupled networks of nuclear spins but are seldom used in modern high-resolution solid-state NMR spectroscopy due to the low resolution of earlier techniques. Herein, we extend this work to the third dimension by designing an MQ spin counting experiment with full chemical resolution of the nuclei involved in the coherences. The technique uses multiplex processing to simultaneously acquire MQ/1Q correlation 2D spectra, with all even-ordered values of M, in the same amount of time it would take to acquire the 2Q/1Q spectrum alone. Further, we apply this multiplexed MQ/1Q correlation method to probe site clustering on functionalized silica surfaces and find the first evidence of clusters involving three or more functionalities. When applied to a supported zirconium metallocene, the observation of a 6Q coherence between six cyclopentadienyl proton nuclei confirms the colocation of two cyclopentadienyl ligands on a single metal center.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗