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At least 199 records · Page 11

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DOE OSTI2025

Comparison of three measurement modalities for 3D characterization of manufactured features and process-induced porosity in titanium alloy additively manufactured parts

Nondestructive characterization of internal features and defects within complex components is vital for many industrial applications, particularly with the advent of additive manufacturing (AM) technologies. However, community understanding of the limitations of nondestructive methods such as X-ray Computed Tomography (CT) can be limited in certain industrial sectors as these may be emergent applications. In this paper, we investigate the limits of X-ray CT measurements and compare extracted data with mechanical polishing serial sectioning (MPSS) and confocal laser scanning microscopy (CLSM). The test object is an additively manufactured titanium alloy disk that contains both process-induced porosity and machined features, including focused ion beam milled features designed to probe the resolution limits of X-ray CT. Results show that each of these characterization techniques has advantages and disadvantages. We compare data acquisition times, spatial resolution, geometric measurement accuracy and defect visualization fidelity across these modalities to establish a practical framework.

Additive manufacturing↗

DOE OSTI2025

Esterification of 3-quinuclidinol, a marker for the incapacitant BZ, for analysis by EI-GC-MS in OPCW test matrices

The analysis by EI-GC-MS of 3-quinuclidinol (3Q), a marker for the riot control and incapacitating agent quinuclinidyl benzilate (BZ), using several, new acylation strategies is described. After evaluating and optimizing conditions for the acylations, these were tested on their ability to successfully derivatize 3Q in three different matrices featured in the 44th Organisation for the Prohibition of Chemical Weapons (OPCW) proficiency test (PT). As 3Q is a highly polar compound, the work here describes acylation strategies that generate 3Q analogs with superior gas chromatographic profiles relative to the underivatized material. The acyl groups studied in this work included acetyl, benzoyl, pentafluorobenzoyl and the bis(3,5-trifluoromethyl)benzoyl. The acylated 3Q products provide sharp, detectable peaks with significantly different retention times from that of the unmodified 3Q. Thus, the retention times for acetyl-, benzoyl-, pentafluorobenzoyl- and 3,5-bis(trifluoromethyl)benzoyl-3Q were determined to be 15.9, 24.9, 22.8 and 22.1 min respectively, in stark contrast to the unmodified 3Q which provides a sharp peak with a retention time centered at ~ 13.5 min only at high concentrations (> 10 µg/mL or µg/g) while a broad peak (RT ~ 15–15.5 min) at low concentrations (< 5 µg/mL or µg/g). The developed protocol was used to derivatize 3Q in three separate matrices. The first two matrices were liquid samples featured during the 44th OPCW PT at two separate concentrations in each (5 and 50 µg/mL). The last matrix was a soil sample featured in the same 44th OPCW PT wherein the 3Q had been spiked at a 12 µg/g concentration. The approach involves the extraction of 3Q from all matrices followed by its acylation resulting in a second reportable analytical method for an OPCW PT. The presented derivatization strategies should find wide applicability particularly in laboratories involved with the analysis of this chemical weapon agent degradation product and those participating in the yearly OPCW PTs.

3-quinuclidinol↗

DOE OSTI2026

Constraining neutrino-nucleon form factors with charged-current scattering at the Electron-Ion Collider

Next-generation neutrino oscillation experiments such as the Deep Underground Neutrino Experiment require percent-level knowledge of neutrino-nucleon interaction cross sections. The nucleon axial form factor 𝐹 𝐴 ⁡(𝑄 2 ), parametrized by the axial mass 𝑀 𝐴 , is the dominant source of uncertainty in the quasielastic channel, and the parity-violating structure function 𝑥⁢𝐹 3 is poorly constrained on free nucleons. We propose using charged-current (CC) electron-proton scattering at the Electron-Ion Collider (EIC) to address both problems simultaneously. The measurement exploits three key features of the EIC: (1) helicity-selective electron bunches provide in situ electromagnetic background rejection; (2) a longitudinally polarized proton target enables extraction of 𝐹 𝐴 ⁡(𝑄 2 ) through the target-spin asymmetry 𝐴 𝑈⁢𝐿 ; and (3) the 𝑦-distribution leverage in CC deep inelastic scattering (DIS) separates 𝐹 2 and 𝑥⁢𝐹 3 on a free proton, without nuclear corrections. Using a Fisher information analysis at $\sqrt{𝑠}$ =141 GeV with 500 fb −1 of integrated luminosity, we project the Cramér-Rao statistical floor of 𝛿⁢𝑀 𝐴 ≈0.03 GeV (3%). Incorporating first-order realistic detector effects, such as zero-degree calorimeter acceptance, 𝑄 2 smearing (5%), and background noise from helicity subtraction, the projected sensitivity is severely background-limited due to the small signal-to-background ratio (𝑆/𝐵 ≈ 3 ×10 −4 ) in the elastic channel. Achieving competitive sensitivity (𝛿⁢𝑀 𝐴 ≈ 0.14 GeV) would require ∼10 −7 background suppression, 3 orders of magnitude beyond current projections. The CC DIS 𝑦 distribution provides subpercent extraction of 𝑥⁢𝐹$^{𝑊^{−}}_{3}$ over 0.05 < 𝑥 < 0.5, representing the most robust electroweak measurement in the near term.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗

DOE OSTI2025

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar

The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.

42 ENGINEERING↗

DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗

DOE OSTI2025

Low Greenhouse Gas (GHG) Vehicle Technologies Research, Development, Demonstration and Deployment Topic 5 Natural Gas Engine Enabling Technologies

A 10 liter natural gas engine has been developed with significant improvements in efficiency while maintaining ultra low NOx emissions and meeting all other EPA criteria emissions limits. This was accomplished through design and analysis of performance components specifically for operation with natural gas in contrast to current production engines which are a minimally modified diesel engine that retain most diesel design features including a flat deck swirl head. The architecture developed here includes a pent roof cylinder head with tumble charge motion and cooling passages specifically optimized for effective cooling around the spark plug and valve bridges. Also in contrast to current production natural gas engines, EGR was not used, in part to avoid the initial cost and warranty expense associated with EGR systems, but also for performance benefits of faster combustion, reduced risk of misfire and high open cycle efficiency due to the turbocharger’s ability to extract energy from the high temperature exhaust. The exhaust manifold uses high temperature material and thermal mechanical fatigue analysis was completed to ensure the ability to withstand high exhaust temperatures without EGR. Other features include dual overhead cam with late intake valve closing Miller cycle and 14:1 compression ratio steel pistons. Low NOx emissions are achieved with stoichiometric combustion and application of a close coupled plus underfloor three way catalysts. The program target of peak brake thermal efficiency of 42% has been demonstrated along with bsNOx of 0.02 g/hp-hr over HD-FTP and RMCSET emissions cycles.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

End-to-End Automated Segmentation Framework for Four-Dimensional Scanning Transmission Electron Microscopy Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is powerful for rapidly characterizing arrays of nanoparticles produced via high-throughput synthesis. However, such 4D-STEM datasets typically contain thousands of nanoparticles, each characterized by thousands of diffraction patterns spatially distributed across the nanoparticle, necessitating efficient and comprehensive analysis. We propose an end-to-end segmentation framework to automatically segment each nanoparticle into regions with distinct composition/orientation of crystal grains, using only the 4D-STEM data. Bragg disk information is extracted in a physics-informed manner from the diffraction patterns at each spatial location and combined with the real space coordinates to form feature vectors. These feature vectors are then used as inputs to a Gaussian mixture model (GMM) to segment the nanoparticle into distinct regions. We also develop two visualization tools based on the GMM outputs to infer the interface transition and the degree of superposition. Our framework comprehensively integrates machine learning tools and physics knowledge, and provides a basis for substantially compressing enormous 4D-STEM datasets, e.g., by replacing the full 4D-STEM dataset for each nanoparticle with only a single set of Bragg disk features for each distinct crystal grain identified in the nanoparticle. In this article, we demonstrate the power of our framework by presenting results for real, complex datasets.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Automated Vehicle Feasibility Study

This study collected automated vehicle (AV) performance data on public roadways in Athens, Ohio. The route for the study contained a combination of roads with different functional classifications, conditions, annual average daily traffic, and ownership responsibilities for maintenance and repair. Preparation for the public road deployment was done in a controlled environment at Transportation Research Center’s SMARTCenter, a dedicated AV test facility in East Liberty, Ohio. Researchers analyzed data and extracted insights relevant for both AV developers and infrastructure owners and operators. The study found that rural environments offer a unique set of roadway features such as hills and curves, which can challenge the driving behavior of an AV. Rural regions can also contain a large number of low-traffic gravel roads that lack pavement markings, which appear to be a crucial infrastructure element for operation of current generation AVs. Similarly, the presence of well-maintained lane lines along curves can influence the AV’s roadway departure tendencies. The study found that curvature-related behavior of an AV is also influenced by driving speed on the roadway segment. Such findings were consistent regardless of the time of day along the route or season of data collection. However, commentary about AV performance in active adverse weather cannot be made, as this is still an area of active research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Charmonium χ c 0 and χ c 2 resonances in coupled-channel scattering from lattice QCD

In order to explore the spectrum of hidden-charm scalar and tensor resonances, we study meson-meson scattering with J P C = 0 + + , 2 + + in the charmonium energy region using lattice QCD. Employing a light-quark mass corresponding to m π ≈ 391 MeV , we determine coupled-channel scattering amplitudes up to around 4100 MeV considering all kinematically relevant channels consisting of a pair of open-charm mesons or a charmonium meson with a light meson. A single isolated scalar resonance near 4000 MeV is found with large couplings to D D ¯ , D s D ¯ s and the kinematically closed D * D ¯ * channel. A single tensor resonance at a similar mass couples strongly to D D ¯ , D D ¯ * and D * D ¯ * . We compare the extracted resonances to contemporary experimental candidate states, previous lattice results and theoretical modeling. In contrast to several other studies, we do not find any significant feature in the scalar amplitudes between the ground state χ c 0 ( 1 P ) and the resonance found around 4000 MeV. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Stratigraphy‐Induced Localization of Microseismicity During CO 2 Injection in Illinois Basin

Abstract Subsurface fluid injection stimulates complex hydromechanical interaction, necessitating the integration of geomechanical data across spatial and temporal scales to consider the sophisticated behavior. Induced seismic response is usually associated with the complex reservoir architecture and pre‐existing features that are three‐dimensional, such as local stratigraphy, fractures, faults, and other discontinuities. This study encompasses laboratory characterization of the coupled hydromechanical response of cores extracted from rock formations in Illinois Basin: reservoir ‐ Mt. Simon sandstone, basal seal ‐ Argenta sandstone, and crystalline basement ‐ Precambrian rhyolite. High‐resolution numerical modeling allows considering the three‐dimensional complexity of the Illinois Basin Decatur Project with spatial resolution comparable to one of the active seismic surveys. A detailed reconstruction of the evolving state of stress in formations lacking direct stress measurements is achieved by numerical modeling that integrated laboratory‐derived hydromechanical properties, a porosity‐permeability relationship, active seismic data, and an inverted three‐dimensional porosity distribution. It appears that the microseismic clusters, mainly observed in the crystalline basement during the injection, are linked to zones experiencing more critically stressed conditions prior to injection. These zones have a potential for reactivation during the injection and are attributed to the specific local stratigraphy of the injection site, as well as transfer of triggering perturbations during the injection.

Bondarenko, N. [University of Illinois Urbana‐Cham↗

DOE OSTI2024

Compressed baryon acoustic oscillation analysis is robust to modified-gravity models

Abstract We study the robustness of the baryon acoustic oscillation (BAO) analysis to the underlying cosmological model. We focus on testing the standard BAO analysis that relies on the use of a template. These templates are constructed assuming a fixed fiducial cosmological model and used to extract the location of the acoustic peaks. Such “compressed analysis” had been shown to be unbiased when applied to the ΛCDM model and some of its extensions. However, it has not been known whether this type of analysis introduces biases in a wider range of cosmological models where the template may not fully capture relevant features in the BAO signal. In this study, we apply the compressed analysis to noiseless mock power spectra that are based on Horndeski models, a broad class of modified-gravity theories specified with eight additional free parameters. We study the precision and accuracy of the BAO peak-location extraction assuming DESI, DESI II, and MegaMapper survey specifications. We find that the bias in the extracted peak locations is negligible; for example, it is less than 10% of the statistical error for even the proposed future MegaMapper survey. Our findings indicate that the compressed BAO analysis is remarkably robust to the underlying cosmological model.

Astronomy & Astrophysics↗

DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

String-based model with Hagedorn temperature of 𝑇 𝐻 ∼ 300 MeV describes the spectrum of mesons and glueballs

We consider the thermodynamics of a color-confined phase of quantum chromodynamics (QCD) and pure gauge theory within a string-inspired corresponding to a physical spatial dimension, 𝑑 = 3. We show that the physical mass spectrum of massive mesons—in both the strange and nonstrange sectors separately—is reasonably well described and extended by the exponential mass spectrum of open strings, 𝜌⁡(𝑚), characterized by a unique Hagedorn temperature 𝑇 𝐻 = $\sqrt3⁢𝜎/2⁢𝜋$, expressed by the string tension 𝜎. This 𝑇 𝐻 is the value appropriate for 𝑑 = 3 spatial dimensions and is of the order of 𝑇 𝐻 ∼ 300 MeV for typical values of the string tension. It is much larger than the values of 𝑇 𝐻 which have been phenomenologically extracted so far to describe the meson spectrum. Glueball states in pure gauge theory, modeled by closed strings, exhibit a similarly large Hagedorn temperature, highlighting a universal feature of the exponential spectrum. We further analyze the thermodynamic properties of the equation of state at finite temperature and demonstrate that, in the confined phase, the string models agree with lattice QCD results. This lends further support to the recent interpretation of the QCD phase diagram that incorporates strings as relevant degrees of freedom.

Phase transitions↗

DOE OSTI2024

Evidence for B + → K + ν ν ¯ decays

We search for the rare decay B + → K + ν ν ¯ in a 362 fb − 1 sample of electron-positron collisions at the ϒ ( 4 S ) resonance collected with the Belle II detector at the SuperKEKB collider. We use the inclusive properties of the accompanying B meson in ϒ ( 4 S ) → B B ¯ events to suppress background from other decays of the signal B candidate and light-quark pair production. We validate the measurement with an auxiliary analysis based on a conventional hadronic reconstruction of the accompanying B meson. For background suppression, we exploit distinct signal features using machine learning methods tuned with simulated data. The signal-reconstruction efficiency and background suppression are validated through various control channels. The branching fraction is extracted in a maximum likelihood fit. Our inclusive and hadronic analyses yield consistent results for the B + → K + ν ν ¯ branching fraction of [ 2.7 ± 0.5 ( stat ) ± 0.5 ( syst ) ] × 10 − 5 and [ 1.1 − 0.8 + 0.9 ( stat ) − 0.5 + 0.8 ( syst ) ] × 10 − 5 , respectively. Combining the results, we determine the branching fraction of the decay B + → K + ν ν ¯ to be [ 2.3 ± 0.5 ( stat ) − 0.4 + 0.5 ( syst ) ] × 10 − 5 , providing the first evidence for this decay at 3.5 standard deviations. The combined result is 2.7 standard deviations above the standard model expectation. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

Quasiparticle Interference in Kitaev Quantum Spin Liquids

We study quasiparticle interference (QPI) in the Kitaev quantum spin liquid (QSL) for electrons tunneling into the QSL. The local tunneling conductance around a spin vacancy or localized vison reveals unique features associated with fractionalized Majorana fermions, chargons, and visons. In certain parameter regimes, the single-spinon density of states and momentum dispersion can both be directly extracted from the tunneling conductance. In conclusion, our results suggest that QPI is a promising tool for identifying the Kitaev QSL and its fractionalized excitations.

Anyons↗

DOE OSTI2026

Electromagnetic Tomography of Radial Flow in the Quark-Gluon Plasma

We present a novel multimessenger approach to extract the effective radial flow of the quark-gluon plasma (QGP) by jointly analyzing thermal photon and dilepton spectra in heavy-ion collisions. A key feature of this method is that it circumvents the need for a directly unmeasurable reference—the photon temperature in the absence of flow—by establishing, within a calibrated model framework, a stable, approximately linear correlation with the dilepton-inferred temperature. This construction defines an experimentally constructible quantity, 𝑣$^{eff}_{𝑟}$, which reflects early-time collectivity and exhibits a strong correlation with the spacetime-averaged radial velocity of the QGP. Together with previous results linking dilepton slopes to the initial QGP temperature, our work establishes a consistent framework for electromagnetic tomography of the QGP. Here, our framework quantifies the experimental precision target, thereby providing a concrete road map for future measurements at RHIC and the LHC and opening a new avenue to probe the early-time dynamics of hot QCD matter.

Collective flow↗