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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Contrasting Chemical Kinetic Parameters for Near In‐Service‐Temperature Aging and High‐Temperature Thermal Decomposition of Triaminotrinitrobenzene Formulations

Triaminotrinitrobenzene-based high explosives such as LX-17 offer high energy density and exceptional safety, yet their long-term aging behavior at low temperatures remains poorly understood. In this study, several legacy and new production lots of LX-17 were subjected to accelerated aging experiments below 100°C, during which the formation rate of the initial degradation product, monofurazan (F1), was monitored. Kinetic analysis was performed using a sample-age-aware computational approach, yielding activation energy estimates of 82–91 kJ mol −1 for the low-temperature initiation step—markedly lower than the ∼200 kJ mol −1 associated with high-temperature thermal decomposition. Extrapolation from established cookoff models supports the conclusion that the dominant degradation mechanism at low temperatures differs from that at high temperatures. In conclusion, our results provide a unified kinetic framework that bridges the gap between in-service conditions and high-temperature damage.

Chemistry - Chemical explosives↗
DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗
DOE OSTI2024

Review of Technical Photovoltaic Key Performance Indicators and the Importance of Data Quality Routines

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. Further, the effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

14 SOLAR ENERGY↗
DOE OSTI2026

Measurement of coherent exclusive J/ψ → μ+μ− production in ultraperipheral Pb+Pb collisions at sNN=5.36 TeV with the ATLAS detector

The ATLAS experiment has performed a measurement of coherent exclusive J/ψ → μ+μ− production in ultraperipheral Pb+Pb collisions at sNN=5.36$$ \sqrt{s_{\textrm{NN}}}=5.36 $$ TeV. The data was recorded at the Large Hadron Collider (LHC) during 2023, and corresponds to an integrated luminosity of 79 μb−1. Exclusive J/ψ candidates were selected with a dedicated track-sensitive trigger based on the ATLAS transition radiation tracker. The analysis involves reconstruction of the dimuon invariant mass based on muon tracks from the inner detector, as the muon transverse momentum range of interest precludes the use of the standard muon reconstruction and identification algorithms. Differential cross sections are measured as a function of J/ψ rapidity and are compared with theoretical predictions. After extrapolation to sNN=5.02$$ \sqrt{s_{\textrm{NN}}}=5.02 $$ TeV, they are also compared with previous measurements performed by other experiments using data from LHC Run 2. While the results agree reasonably well with theoretical predictions, they are in tension with previous Run-2 results for the central rapidity region.

Aad, G↗
DOE OSTI2026

Combination and interpretation of differential Higgs boson production cross sections in proton-proton collisions at $ \sqrt{s}=13 $ TeV

Precision measurements of Higgs boson differential production cross sections are a key tool to probe the properties of the Higgs boson and test the standard model. New physics can affect both Higgs boson production and decay, leading to deviations from the distributions that are expected in the standard model. In this paper, combined measurements of differential spectra in a fiducial region matching the experimental selections are performed, based on analyses of four Higgs boson decay channels (γγ, ZZ$^{(*)}$, WW$^{(*)}$, and ττ) using proton-proton collision data recorded with the CMS detector at $ \sqrt{s}=13 $ TeV, corresponding to an integrated luminosity of 138 fb$^{−1}$. The differential measurements are extrapolated to the full phase space and combined to provide the differential spectra. A measurement of the total Higgs boson production cross section is also performed using the γγ and ZZ decay channels, with a result of $ {53.4}_{-2.9}^{+2.9}{\left(\textrm{stat}\right)}_{-1.8}^{+1.9}\left(\textrm{syst}\right) $ pb, consistent with the standard model prediction of 55.6 ± 2.5 pb. The fiducial measurements are used to compute limits on Higgs boson couplings using the κ-framework and the SM effective field theory.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurements of W + W − production cross-sections in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Measurements of W + W − → e ± νμ ∓ ν production cross-sections are presented, providing a test of the predictions of perturbative quantum chromodynamics and the electroweak theory. The measurements are based on data from pp collisions at $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider in 2015–2018, corresponding to an integrated luminosity of 140 fb −1 . The number of events due to top-quark pair production, the largest background, is reduced by rejecting events containing jets with b-hadron decays. An improved methodology for estimating the remaining top-quark background enables a precise measurement of W + W − cross-sections with no additional requirements on jets. The fiducial W + W − cross-section is determined in a maximum-likelihood fit with an uncertainty of 3.1%. The measurement is extrapolated to the full phase space, resulting in a total W + W − cross-section of 127 ± 4 pb. Differential cross-sections are measured as a function of twelve observables that comprehensively describe the kinematics of W + W − events. The measurements are compared with state-of-the-art theory calculations and excellent agreement with predictions is observed. A charge asymmetry in the lepton rapidity is observed as a function of the dilepton invariant mass, in agreement with the Standard Model expectation. A CP-odd observable is measured to be consistent with no CP violation. Limits on Standard Model effective field theory Wilson coefficients in the Warsaw basis are obtained from the differential cross-sections.

Accelerator Physics↗
DOE OSTI2024

Measurement of energy resolution with the NEXT-White silicon photomultipliers

The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses $^{83m}$Kr data from the NEXT-White detector to measure and understand the energy resolution that can be obtained with the SiPMs, rather than with PMTs. The energy resolution obtained of (10.9 ± 0.6)%, full-width half-maximum, is slightly larger than predicted based on the photon statistics resulting from very low light detection coverage of the SiPM plane in the NEXT-White detector. The difference in the predicted and measured resolution is attributed to poor corrections, which are expected to be improved with larger statistics. Furthermore, the noise of the SiPMs is shown to not be a dominant factor in the energy resolution and may be negligible when noise subtraction is applied appropriately, for high-energy events or larger SiPM coverage detectors. These results, which are extrapolated to estimate the response of large coverage SiPM planes, are promising for the development of future, SiPM-only, readout planes that can offer imaging and achieve similar energy resolution to that previously demonstrated with PMTs.[graphic not available: see fulltext]

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Measurement of beauty production via non-prompt charm hadrons in p–Pb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

The production cross sections of D 0 , D + , and Λ$^{+}_{c}$ hadrons originating from beauty-hadron decays (i.e. non-prompt) were measured for the first time at midrapidity in proton–lead (p–Pb) collisions at the center-of-mass energy per nucleon pair of $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV. Nuclear modification factors (R pPb ) of non-prompt D 0 , D + , and Λ$^{+}_{c}$ are calculated as a function of the transverse momentum (p T ) to investigate the modification of the momentum spectra measured in p–Pb collisions with respect to those measured in proton–proton (pp) collisions at the same energy. The R pPb measurements are compatible with unity and with the measurements in the prompt charm sector, and do not show a significant p T dependence. The p T -integrated cross sections and p T -integrated R pPb of non-prompt D 0 and D + mesons are also computed by extrapolating the visible cross sections down to p T = 0. The non-prompt D-meson R pPb integrated over p T is compatible with unity and with model calculations implementing modification of the parton distribution functions of nucleons bound in nuclei with respect to free nucleons. The non-prompt Λ$^{+}_{c}$/D 0 and D + /D 0 production ratios are computed to investigate hadronisation mechanisms of beauty quarks into mesons and baryons. The measured ratios as a function of p T display a similar trend to that measured for charm hadrons in the same collision system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

The metamorphosis of semi-classical mechanisms of confinement: from monopoles on ℝ 3 × S 1 to center-vortices on ℝ 2 × T 2

There are two distinct regimes of Yang-Mills theory where we can demonstrate confinement, the existence of a mass gap, and the multi-branch structure of the effective potential as a function of the theta angle using a reliable semi-classical calculation. The two regimes are deformed Yang-Mills theory on ℝ 3 × S 1 , and Yang-Mills theory on ℝ 2 × T 2 where the torus is threaded by a ’t Hooft flux. The weak coupling regime is ensured by the small size of the circle or torus. In the first case the confinement mechanism is related to self-dual monopoles, whereas in the second case self-dual center-vortices play a crucial role. These two topological objects are distinct. In particular, they have different mutual statistics with Wilson loops. On the other hand, they carry the same topological charge and action. We consider the theory on ℝ × T 2 × S 1 and extrapolate both the monopole and vortex regimes to a quantum mechanical domain, where a cross-over takes place. Both sides of the cross-over are described by a deformed ℤ N TQFT. On ℝ 2 × S 1 × S 1 , we derive an effective field theory (EFT) of vortices from the EFT of monopoles in the presence of a ’t Hooft flux. This construction is based on a two-stage Higgs mechanism, reducing SU(N) to U(1) N−1 in 3d first, followed by reduction to a ℤ N EFT in 2d in the second step. This result shows how monopoles transmute into center-vortices, and suggests adiabatic continuity between the two confinement mechanisms. The basic mechanism is flux fractionalization: the magnetic flux of the monopoles splits up and is collimated in such a way that 2d Wilson loops detect it as a center vortex.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Reflected entropy in random tensor networks. Part III. Triway cuts

For general random tensor network states at large bond dimension, we prove that the integer Rényi reflected entropies (away from phase transitions) are determined by minimal triway cuts through the network. This generalizes the minimal cut description of bipartite entanglement for these states. A natural extrapolation away from integer Rényi parameters, suggested by the triway cut problem, implies the holographic conjecture S R = 2EW, where S R is the reflected entropy and EW is the entanglement wedge cross-section. Minimal triway cuts can be formulated as integer programs which cannot be relaxed to find a dual maximal flow/bit-thread description. This sheds light on the gap between the existence of tripartite entanglement in holographic states and the bipartite entanglement structure motivated by bit-threads. In particular, we prove that the Markov gap that measures tripartite entanglement is lower bounded by the integrality gap of the integer program that computes the triway cut.

AdS-CFT correspondence↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2025

Equation of State for the Thermodynamic Properties of Trans-1,2-dichloroethene [R-1130(E)]

We present an empirical equation of state in terms of the Helmholtz energy for trans-1,2-dichloroethene [R-1130(E)]. The range of validity is from the triple-point temperature, 223.31 K to 525 K with pressures up to 30 MPa. It may be used to calculate all thermodynamic properties in the fluid phase, including liquid, gas, and supercritical regions. Comparisons are given with existing literature data and estimated uncertainties are provided. In addition, checks were made for correct extrapolation behavior so that the equation behaves in a physically realistic manner when used outside of its range of validity, enabling its use in mixture models. The estimated uncertainties (at a k = 2 or 95 % level of confidence) are based on comparisons with critically assessed data and are 0.25 % for vapor pressure for temperatures in the range 300 K < T < 454 K, rising to 1.5 % as the temperature decreases from 300 K to 265 K. For density in the liquid phase the estimated uncertainty is 0.14 % for temperatures 270 K < T < 410 K and for pressures up to 30 MPa. For the vapor phase the estimated uncertainty in density is 3 %. The uncertainty for liquid-phase heat capacity is 1 % at atmospheric pressure over the temperature range 268 K < T < 309 K, and the uncertainty for the speed of sound in the liquid phase is 0.25 % for temperatures 230 K < T < 420 K and for pressures up to 30 MPa. The uncertainties are larger outside of these specified ranges and in the critical region.

1,2-Dichloroethene↗
DOE OSTI2024

Depth and microtopography influence microbial biogeochemical processes in a forested peatland

Background and aims: Peat-accumulating wetlands have undulating surfaces of raised areas (hummocks) and depressions (hollows). Hummock-hollow microtopography in relation to the water table influences the distribution of plant species, root density, and microbial community composition, which could in turn alter carbon (C) and nitrogen (N) cycling within peatlands. We used paired hummock and hollow cores from a boreal, forested peatland to assess how microtopography influences peatland microbial function and, in turn, ecosystem C and N cycling. Methods: The peat was analyzed for microbial biomass and potential enzyme activity in 10 cm depth increments relative to the water table, resulting in two increments for hollows and three for hummocks, which has a raised increment above the water table. Results: Across hummocks and hollows, microbial C and N and fungal biomass generally decreased with depth from the peat surface. In contrast, potential enzyme activity often increased with depth, but this varied within enzyme functional groups according to topography, depth, or both. The potential enzyme activity of C-N degrading peptidases, for example, differed across the five topography × depth increments with the lowest rate in the aerated hummocks. Hummocks compose approximately 66% of the land area at our study site and would therefore underestimate C turnover by an average of 25% if solely used to extrapolate patterns across a forested bog. Conclusion: In conclusion, our results suggest that asynchrony in C and N cycling across the undulating surface of forested peatlands impacts our ability to accurately predict biogeochemical cycling across this important ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Microstructural Evolution of Tantalum During Deformation and Subsequent Annealing

Microstructure-aware models are necessary to predict the behavior of material based on process knowledge or to extrapolate mechanical properties of materials to environmental conditions which are not easily reproduced in the laboratory, e.g., nuclear reactor environments. Elemental Ta provides a relatively simple BCC system in which to develop a microstructural understanding of deformation processes which can then be applied to more complicated BCC alloys. In situ neutron diffraction during compressive deformation and subsequent heat treatment have been used to monitor the evolution of microstructural features in Ta throughout simulated processing steps. Crystallographic texture and dislocation density are determined as a function of first plastic strain, then temperature. Lattice strains are determined and attributed to stresses at macroscopic, grain and dislocation length scales. The increase of the dislocation density through deformation and subsequent recovery during heat treatment is monitored through the changing diffraction line profile. Also, randomization of the texture is used as a signature of recrystallization. The recovery of dislocations through annihilation is not observed to depend on the initial dislocation density in the range studied here. In contrast, recrystallization is observed to depend strongly on the initially dislocation density.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Taming nuclear mass models with Gaussian processes

We propose a new set of nuclear mass predictions based on multiple theoretical mass models. By employing Gaussian process regression with the Matérn kernel, we achieved root-mean-square (rms) deviations below 100 keV for the training dataset. The best-performing mass models achieved rms deviations below 150 keV for the new precise mass data from AME2020, whereas the ensemble average showed robust performance across the nuclear chart. Our approach uniquely combines: (1) systematic refinement of eight mass models through their residuals, (2) physics-informed features, including magic numbers, nucleon parity numbers, neutron excess, and nuclear collectivity, and (3) theory-to-theory validation demonstrating robust extrapolation capability. We find that the Matérn kernel provides superior uncertainty quantification compared to the RBF kernel, with a length-scale analysis revealing enhanced inter-nuclei correlations. We provide complete mass predictions for all unknown nuclides in AME2020, offering valuable constraints for nuclear structure studies and astrophysical modeling when used with proper uncertainty propagation.

Gaussian processes↗
DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗
DOE OSTI2026

Establishing an acoustic-property relationship in laser powder bed fusion with machine learning

Quality control of Laser Powder Bed Fusion (PBF-LB) additively manufactured parts is an important hurdle inhibiting the technology’s use structural applications. Acoustic monitoring of the laser powder bed fusion process can detect defects in-situ that are known to degrade mechanical properties. However, processing-structure-property (PSP) relationships are required to extrapolate from detected defects to part performance. Here, this study explores how acoustics may be a suitable signature linking processing conditions to properties, thus effectively substituting for structure in the PSP relationship. Establishing such a relationship would enable a part’s mechanical performance to be directly predicted from its acoustic signature, reducing the need for destructive testing or microstructural analysis to ensure a part will meet performance requirements. One hundred CoCrFeMnNi high entropy alloy tensile bars were printed across 13 process conditions in a series of 6 prints. The acoustic signatures of these tensile bars were used to train machine learning models to predict each part’s mechanical properties. By using both process information and acoustic information to predict mechanical properties, yield strength was predicted 18% more accurately and ductility to failure was predicted 10% more accurately than is achieved when using duplicate parts to predict part performance. Finally, individual acoustic frequencies were investigated to determine why acoustic signatures improve mechanical property predictions and the potential physical origins of these signatures. This work demonstrates how blending acoustics, process information, and machine learning can provide in-situ diagnostics of mechanical properties and improve the reliability of the PBF-LB process.

Acoustic emission↗