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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Inclusive Charged-Current Electron Neutrino Interactions in SBND

The Short-Baseline Near Detector (SBND), a 112 ton liquid argon time projection chamber (LArTPC), is the near detector of the Short- Baseline Neutrino Program at Fermilab. Due to its proximity to the Booster Neutrino Beam target, SBND has already seen a record-breaking number of electron neutrino interactions on argon. We present preliminary results from SBND’s differential inclusive electron neutrino charged-current analysis from our first year of beam data, selecting over 2,000 interactions. This analysis will present the largest sample of electron neutrinos on argon to date, as well as the first differential cross section measurement of electron neutrinos. This poster will give an overview of the selection, cross-section extraction, and demonstrate SBND’s detector and electromagnetic shower reconstruction performance through data/MC comparisons.

Tung, Lynn [Chicago U.]↗
DOE OSTI2025

Beyond traditional diagnostics: Identifying active galactic nuclei using spectral energy distribution fitting in DESI data

Active galactic nuclei (AGN) are typically identified through their distinctive X-ray or radio emissions, mid-infrared (MIR) colors, or emission lines. However, each method captures different subsets of AGN due to signal-to-noise (S/N) limitations, redshift coverage, and extinction effects, underscoring the necessity for a multiwavelength approach for comprehensive AGN samples. This study explores the effectiveness of spectral energy distribution (SED) fitting as a robust method for AGN identification. Using CIGALE optical-MIR SED fits on DESI Early Data Release galaxies, we compare SED-based AGN selection (AGNFRAC ≥ 0.1) with traditional methods including BPT diagrams, WISE colors, X-ray, and radio diagnostics. The SED fitting identifies ∼70% of narrow- and broad-line AGN and 87% of WISE-selected AGN. Incorporating high S/N WISE photometry reduces star-forming galaxy contamination from 62% to 15%. Initially, ∼50% of SED-AGN candidates are undetected by standard methods, but additional diagnostics classify ∼85% of these sources, revealing low-ionization nuclear emission-line regions and retired galaxies potentially representing evolved systems with weak AGN activity. Further spectroscopic and multiwavelength analysis will be essential to determine the true AGN nature of these sources. SED fitting provides complementary AGN identification, unifying multiwavelength AGN selections. This approach enables more complete – albeit somewhat contaminated – AGN samples, which are essential for upcoming large-scale surveys where spectroscopic diagnostics may be limited.

Seyfert↗
DOE OSTI2025

Pentaerythritol structure-directing agents bias aluminum siting in MFI zeolites to increase para -xylene selectivity during toluene methylation

MFI zeolites contain ten-membered ring channels (∼0.55 nm diameter) that intersect to form larger voids (∼0.70 nm diameter). The distribution of framework Al sites dictates the void environments that confine active H + sites. MFI synthesized with pentaerythritol (PET) and Na + as structure-directing agents are interrogated using kinetically controlled toluene methylation rates and xylene isomer selectivities. High para-xylene selectivity and low toluene methylation rates indicate that MFI-PET samples contain H + sites predominantly in smaller channels. As-synthesized samples contain more framework Al than Na + , indicating that some charge compensation is provided by protonated PET-solvent complexes that bias Al siting towards these smaller channels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Reduced-Temperature Process for Preparing Atomically Clean Si(100) and SiGe(100) Surfaces with Vapor HF

Silicon processing techniques such as atomic precision advanced manufacturing (APAM) and epitaxial growth require surface preparations that activate oxide desorption (typically >1000 °C) and promote surface reconstruction toward atomically clean, flat, and ordered Si(100)-2 × 1. Here, in this study, we compare the aqueous and vapor phase cleaning of Si and Si/SiGe surfaces to prepare APAM-ready and epitaxy-ready surfaces at lower temperatures. Angle resolved X-ray photoelectron spectroscopy (ARXPS) and Fourier transform infrared (FTIR) spectroscopy indicate that vapor hydrogen fluoride (VHF) cleans dramatically reduce carbon surface contamination and allow the chemically prepared surface to reconstruct at lower temperatures, 600 °C for Si and 580 °C for a Si/Si 0.7 Ge 0.3 heterostructure, into an ordered atomic terrace structure indicated by scanning tunneling microscopy (STM). After thermal treatment and vacuum hydrogen termination, we demonstrate STM hydrogen desorption lithography (HDL) on VHF-treated Si samples, creating reactive zones that enable area-selective chemistry by using a thermal budget similar to CMOS process flows. We anticipate that these results will establish new pathways to integrate APAM with Si foundry processing.

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

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2025

First Event-by-Event Identification of Cherenkov Radiation from Sub-Mev Particles in Liquid Argon

This Letter reports the event-by-event observation of Cherenkov light from sub-MeV electrons in a high scintillation light-yield liquid argon detector by the coherent CAPTAIN-Mills (CCM) experiment. The CCM200 detector, located at Los Alamos National Laboratory, instruments seven tons (fiducial volume) of liquid argon with 200 eight-inch photomultiplier tubes, 80% of which are coated in a wavelength-shifting material and the remaining 20% are uncoated. In the prompt time region of an event, defined as - 6 ≤ t < 0 ns relative to the event start time t = 0 , the uncoated photomultiplier tubes are primarily sensitive to visible Cherenkov photons. Using gamma rays from a Na 22 source for production of sub-MeV electrons, we isolated prompt Cherenkov light with > 5 σ confidence and developed a selection to obtain a low-background electromagnetic sample. This is the first event-by-event observation of Cherenkov photons from sub-MeV electrons in a high-yield scintillator detector and represents a milestone in low-energy particle detector development.

Aguilar-Arevalo, A. A. [UNAM, Mexico]↗
DOE OSTI2024

Search for a heavy neutral lepton that mixes predominantly with the tau neutrino

We report a search for a heavy neutral lepton (HNL) that mixes predominantly with ν τ . The search utilizes data collected with the Belle detector at the KEKB asymmetric energy e + e − collider. The data sample was collected at and just below the center-of-mass energies of the ϒ ( 4 S ) and ϒ ( 5 S ) resonances and has an integrated luminosity of 915 fb − 1 , corresponding to ( 836 ± 12 ) × 10 6 e + e − → τ + τ − events. We search for production of the HNL (denoted N ) in the decay τ − → π − N followed by its decay via N → μ + μ − ν τ . The search focuses on the parameter-space region in which the HNL is long-lived, so that the μ + μ − originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the τ neutrino as a function of the HNL mass. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2026

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗
DOE OSTI2026

Expanding the Domain of Applicability of Machine Learning Models with Limited Data for Drug Property Prediction

Accurate machine learning models for predicting small molecule interactions with biological targets are essential for therapeutic discovery, biothreat response, and computational drug design, but their performance is often limited for understudied targets with sparse experimental data. To address this challenge, we developed and evaluated methods to improve molecular property prediction under low-data conditions, using the NimA-related kinase (NEK) family as a proof-of-concept. This work focused on two complementary goals within the ATOM Modeling PipeLine (AMPL) and the Generative Molecular Design (GMD) loop: expanding model applicability through transfer learning, representation learning, feature scaling, sampling strategies, and active-learning-inspired compound selection; and enabling efficient virtual screening to prioritize compounds that balance predicted activity, design objectives, and synthetic accessibility.

organic↗
DOE OSTI2026

The Compilation and Validation of the Spectroscopic Redshift Catalogs for the DESI-COSMOS and DESI-XMM-LSS Fields

Over several dedicated programs that include targets beyond the main cosmological samples, the Dark Energy Spectroscopic Instrument collected spectra for 304,970 unique objects in two fields centered on the COSMOS and XMM-LSS fields. In this work, we develop spectroscopic redshift robustness criteria for those spectra, validate these criteria using visual inspection, and provide two custom value-added catalogs with our redshift characterizations. With these criteria, we reliably classify 212,935 galaxies below z < 1.6, 9713 quasars, and 35,222 stars. The resulting catalogs achieve a redshift purity exceeding 99.4% across all galaxy samples. As a critical element in characterizing the selection function, we provide the description of 70 different algorithms that were used to select these targets from imaging data. To facilitate joint imaging/spectroscopic analyses, we provide row-matched photometry from the Dark Energy Camera, Hyper-Suprime Cam, and public COSMOS2020 photometric catalogs. Finally, we demonstrate example applications of these large catalogs to photometric redshift estimation, cluster finding, and completeness studies.

Ratajczak, J. [Univ. of Utah, Salt Lake City, UT (↗
DOE OSTI2025

Probing Condensed-Phase Structure and Dynamics in Hierarchical Zeolites and Nanosheets for Catalytic Upgradation of Biomass (Final Report)

Understanding complex reaction pathways in systems governed by multi-scale collective interactions across time and length scales remains a central scientific challenge. This project was guided by the hypothesis that the interplay among oligomers, solvents, and active sites can be tuned by a suitable choice of solvation environment and pore architecture in solid-acid catalysts to direct chemical transformations relevant to biomass conversion. Zeolites and zeolite nanosheets were used as model platforms, allowing for the interaction of macromolecules with the surface of the zeolite nanosheets and with smaller pores that host catalytically active sites. To investigate these coupled phenomena, we employ a multi-scale computational framework that integrates molecular-level descriptions with advanced sampling approaches to capture key physical and chemical interactions. Our work through this project improved fundamental understanding of how reactants and solid-acid catalysts interact in solvent-rich environments, thereby enabling the rational design of catalytic systems that upgrade biomass with enhanced selectivity and energy efficiency. In addition, the project developed advanced sampling methodologies critical for disentangling complex, reactive processes in multi-component catalytic environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhanced Monte Carlo Simulations for Electron Energy Loss Mitigation in Real-Space Nanoimaging of Thick Biological Samples and Microchips

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Alpha-Quartz Plastic Strength Investigation Via Diffraction Experiments on Novaculite Using A D-Dia and Elastic Plastic Self- Consistent Interpretation [Thesis}

X-Ray Tomography. Porosity is commonly measured using mercury injection (MI) or water immersion porosimeter (WIP). Both MI and WIP utilize pressure to fill open voids with mercury or water respectively. A measure of the volume change of the sample is then used to estimate the percentage of open voids. However, for the purpose of rheological study it is important to get an accurate measure of open and closed voids. X-ray tomography has been selected as it offers a three-dimensional view into the sample that can quantify all pores limited only by the voxel size which is in the micron range for this study. Radiographs for tomography were collected at the Material Science and Technology Division at Los Alamos National Lab using the Carl Zeiss Xradio 520 instrument and Scout-and-Scan version 16.1 operating software. Two samples were imaged, the starting material and the deformed sample SiO2_65. 3001 radiographs were taken of the starting material with a 6 second exposure time using a 4x objective lens. The x-ray beam was set to 60 kilovoltage peak (kVp) and 5 watts. 1901 radiographs were taken of SiO2_65 with a 25 second exposure time using a 10x objective lens. The x-ray beam was set to 80 kVp and 7 watts. Radiograph files were analyzed by Brian Patterson using Avizo. Void and inclusion volumes were output by voxel sized (1.03 μm) slices, used to calculate a total percentage volume for the starting material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geant4 Event Biasing and Fast Simulation

Geant4 offers advanced event biasing techniques to significantly accelerate simulations involving rare events. Various biasing methods, such as leading particle selection, cross-section biasing, radioactive decay enhancement, and bremsstrahlung splitting, enable efficient event sampling, though they require careful handling. Additionally, Geant4 provides a Fast Simulation Interface, allowing the replacement of standard processes in specific region and for selected particles, enabling faster execution or external code integration. Applications of fast simulation include electromagnetic shower modeling in calorimeters, machine learning inference, and offloading tasks to specialized hardware like GPUs, making Geant4 a powerful tool for computationally demanding simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Muon Time-of-Flight studies for cosmic background rejection in the Short Baseline Near Detector

The Short-Baseline Neutrino (SBN) program at Fermilab is a cutting-edge project in experimental neutrino physics. One of its main goals is to systematically investigate the possible existence of eV-scale sterile neutrinos. This phenomenon has been hypothesized to explain some anomalies found in short-range experiments and, if confirmed, would imply a substantial extension of the Standard Model. SBN also offers an important opportunity to deepen the understanding of neutrino-nucleus interactions in the GeV energy range, through the use of Liquid Argon Time Projection Chambers (LArTPC) detectors, a fundamental technology also for the future DUNE experiment. The SBN experimental infrastructure consists of three detectors aligned along the Booster Neutrino Beamline at Fermilab. Among them, the detector located closest to the neutrino source, SBND (Short-Baseline Near Detector), positioned approximately 110 meters from the target, plays a key role in directly characterizing the initial neutrino flux. This allows for a direct comparison with the measurements from the far detector, ICARUS, located about 600 meters from the source, in order to search for potential signs of anomalous neutrino oscillations. My master's thesis focuses on the commissioning and characterization activities of the SBND detector, with particular reference to the Cosmic Ray Tagger (CRT). The CRT is a subsystem for identifying and rejecting events produced by cosmic rays, which constitute the main source of background for surface experiments like SBND. The activity began with the commissioning of the final components of the detector, as well as their validation to verify their correct functioning and signal acquisition. A central part of my work involved studying the veto efficiency of the CRT system, analyzing the rate of cosmic ray-induced events to quantify any loss of neutrino-induced events caused by cosmic background. This allowed for a more precise evaluation of the systematic impact of the CRT on the useful physics sample. A further phase of my analysis involved an in-depth study of the temporal correlation between the CRT signals and those acquired by the LArTPC's internal photodetector system, consisting of photomultiplier tubes and X-ARAPUCA devices. The objective is to explore the possibility of using combined temporal information as an additional criterion for discriminating between cosmic signals and signals genuinely due to neutrino interaction. Preliminary results indicate the presence of characteristic temporal signatures that could be exploited to improve event selection and increase the purity of the neutrino-induced sample. These methodologies will certainly contribute to the optimization of SBND analysis strategies and, more generally, to a better understanding of background mechanisms in next-generation LArTPC experiments.

Corallo, Annalea [Ferrara U.]↗
DOE OSTI2025

Constraining primordial non-Gaussianity with DESI 2024 LRG and QSO samples

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter f NL loc via the large-scale scale-dependent bias in the power spectrum using 1,631,716 LRGs (0.6 < z < 1.1) and 1,189,129 QSOs (0.8 < z < 3.1). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative technique allowing the correction through the window matrix convolution. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure f NL loc = -3.6 -9.1 +9.0 at 68% confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to f NL loc = 3.5 -7.4 +10.7 at 68% confidence. Due to restricted selection in the LRG sample, the inclusion of the LRGs allows for 10% improvement. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of 2.3.

79 ASTRONOMY AND ASTROPHYSICS↗