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DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗

DOE OSTI2026

Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials

Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.

Puga, Christina L.↗

DOE OSTI2025

Carbon in GaN as a nonradiative recombination center

Trap-assisted nonradiative recombination has been shown to limit the efficiency of optoelectronic devices. While substitutional carbon (⁠C N ) has been suggested to be a nonradiative recombination center in GaN devices, a complete recombination cycle including the two charge-state transition levels has not been previously described. In this work, we investigate the trap-assisted recombination process due to C N in GaN, including multiphonon emission, radiative recombination, trap-assisted Auger–Meitner (TAAM) recombination as well as thermal emission of holes. Our study shows the key role of TAAM processes at the high carrier densities relevant for devices. We also reveal the carrier-density regimes where thermal emission and radiative recombination are expected to play an observable role. Our results highlight that carbon concentrations exceeding ~10 17 cm −3 can have a noticeable impact on device efficiency, not just in GaN active layers but also in InGaN and AlGaN. Furthermore, our comprehensive formalism not only offers detailed results for carbon but also provides a general framework for assessing the multiple processes that participate in trap-assisted recombination in semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Mechanism of H 2 plasma-enabled reduction of hematite thin films

Hydrogen plasma is gaining significant interest as a promising pathway for direct iron ore reduction and for lowering process temperatures but the reduction mechanism remains poorly understood. In this work we analyzed the plasma and thermal reduction of thin-film hematite (Fe 2 O 3 ) at temperatures below 340 °C using X-ray diffraction and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Plasma reduced the incubation period by an order of magnitude and increased the reduction rate by a factor of 2.6 compared to thermal reduction. Plasma-produced H-atoms facilitate the formation of numerous iron nucleation sites, bypassing the energetically unfavorable dissociative adsorption of H 2 on iron oxide. These iron nuclei can autocatalyze the reduction of the surrounding hematite via a hydrogen spillover mechanism. Our results demonstrate that plasma-derived H-atoms primarily impact the initial nucleation-limited stage. These new insights provide a mechanistic framework that can aid the implementation and optimization of hydrogen plasma-assisted iron oxide reduction at reduced temperatures.

08 HYDROGEN↗

DOE OSTI2026

Two-dimensional non-equilibrium melting of charged colloids

Thermodynamic two-dimensional melting has been extensively studied in experiments and simulations, and is well predicted by theory. For systems in equilibrium, this transition is well described by the Kosterlitz–Thouless–Halperin–Nelson–Young theory, where melting is directly linked to the unbinding of topological defects. For driven, non-equilibrium melting and other non-equilibrium phase transitions, the picture is less clear. Here, in this work, we study the two-dimensional melting of a crystal of charged colloids. By randomly replacing some charged colloids with magnetic colloids, we can melt our system by rotating a fraction of the particles to create non-equilibrium, hydrodynamic random flows and local stresses. We can also melt it thermally by changing the particle number density. We find that an effective temperature approach cannot explain the results of our driven system. Rather, in both experiments and simulations, we observe that plotting the hexatic order parameter and the hexatic correlation’s exponent versus the density of disclinations and dislocations, respectively, yields universal curves. This implies that in our systems, two-dimensional melting depends directly on the density of topological defects and is independent of whether thermal or non-equilibrium forces generate them.

critical phenomena↗

DOE OSTI2026

Prioritizing Uncertainties in Hydrogen Contribution to Risk in Post-Crash Outcomes for Rail

This report presents analysis from Sandia National Laboratories predicting contributions to risk associated with the use of hydrogen technology for rail. Event sequence diagrams are used to describe possible accident scenarios and progressions. Initiating event frequencies and branch event probabilities for each scenario are quantified with uncertainty using distributions fit to Federal Railroad Administration and U.S. Department of Transportation Pipeline and Hazardous Materials Safety Administration data on applicable accidents from 2000 to 2020. Uncertainty is propagated through the event sequence diagram to estimate the frequency and conditional probability of accident end states. The analysis identifies four scenarios with significant contributions to risk from hydrogen that are predicted to occur relatively frequently, which may inform priorities for reducing uncertainty. These scenarios are 1) overpressure events resulting from collisions with hydrogen release due to mechanical damage and delayed ignition, 2) jet fire events resulting from collisions with hydrogen release due to mechanical damage and immediate ignition, 3) jet fires resulting from fire or explosion initiating events involving the hydrogen tank and correct operation of the thermally-activated pressure relief device (TPRD) subsequent to the thermal insult, and 4) pressure burst resulting from fire or explosion initiating events involving the hydrogen tank and failure of the TPRD. Delayed and immediate hydrogen ignition probabilities are identified as being highly uncertain and potential candidates for reducing conservatism in the predicted frequencies for these two scenarios.

08 HYDROGEN↗

DOE OSTI2025

Revealing the evolution of order in materials microstructures using multi-modal computer vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Enhancing NO Reduction by CO Over NiO/CeO 2 Catalyst by Optimizing the Metal Oxide–Support Interaction

Noble metal catalysts are widely used in three-way catalysts (TWCs) due to their high efficiency, but their scarcity and high cost drive the need for effective, low-cost alternatives. Here, in this work, a series of NiO/CeO 2 catalysts were engineered by modulating the OH content and crystallite size of the CeO 2 support. This approach yielded Ni species ranging from isolated single atoms to small clusters and larger aggregates, with varied NiO/CeO 2 interaction strengths. Evaluation of their performance in NO reduction by CO, CO oxidation, and the water–gas shift reaction revealed that the NiO/CeO 2 -300 catalyst, featuring small CeO 2 crystallites and highly dispersed NiO clusters, delivered superior activity for NO reduction and CO oxidation. It was demonstrated that small NiO clusters, due to their efficient CO adsorption and activation, were more active than Ni single atoms. Furthermore, the presence of water was found to influence the catalyst stability, with NiO clusters less stable than single atoms likely due to the hydroxylation effect leading to the formation of Ni(OH) 2 . Oxygen storage capacity measurements revealed that performance was governed by a combination of CeO 2 crystallite size and the abundance of NiO/CeO 2 interfaces. These findings demonstrated that the catalytic performance of Ni/CeO 2 systems could be maximized by optimizing the Ni nanostructure and its interaction with CeO 2 support, positioning them as a promising, multifunctional nonprecious alternative for three-way catalysis application.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Time-Resolved Neutron Imaging for Hydrogen Uptake in Subsurface Lithologies

Geologic hydrogen production and underground storage are increasingly important for meeting rising energy demands while providing clean-combustion advantages. However, hydrogen’s high diffusivity and propensity for leakage through porous media necessitate direct evaluation of its transport behavior in subsurface materials. Whereas X-ray microcomputed tomography (μCT) studies often employ contrast agents or surrogate gases, this study leverages neutron transmission radiography/CT to observe hydrogen migration in situ. This work represents the first demonstration of real-time neutron radiography of hydrogen migration in reservoir and caprock lithologies. Cylindrical cores of Indiana limestone, Amherst Gray sandstone, and Tumey shale were subjected to constant-pressure hydrogen charging and scanned in real time using high-resolution neutron radiography. Results indicate immediate hydrogen infiltration in sandstone and limestone, with homogeneous distribution detected throughout their pore structure. In contrast, hydrogen remained largely absent from fine-grained shale under the same pressure, except in an apparently localized fracture zone, where neutron signatures confirmed the presence of hydrogen. Subsequent neutron CT of the sandstone sample, using image subtraction against an uncharged reference, corroborated hydrogen distribution patterns. Even under lowpressure, single-phase conditions, distinct neutron imaging signatures of hydrogen were achieved. These preliminary findings underscore the potential of neutron imaging for advancing subsurface hydrogen migration research.

08 HYDROGEN↗

DOE OSTI2026

Heat exposure and maternal stress: evidence from the GRAPHS pregnancy cohort in Ghana

Heat exposure has been linked to psychosocial stress, an established antecedent of perinatal depression; however, evidence on heat-related stress during pregnancy in sub-Saharan Africa remains limited. We analyzed psychosocial stress scores and covariate data from the Ghana Randomized Air Pollution and Health Study, linking daily maximum and minimum shaded wet bulb globe temperature (WBGT) metrics to participants’ stress scores derived from the Crisis in Family Systems-Revised Life Events Questionnaire. We evaluated associations using ordinal logistic regression of pregnancy-average and trimester-average exposures and distributed lag non-linear models (DLNMs) to assess time-varying associations across gestation. Higher average maximum WBGT exposure across pregnancy was associated with increased odds of higher psychosocial stress; each 1 °C increase in maximum WBGT was associated with 64% higher odds of belonging to a higher stress category (OR = 1.64; 95% CI = 1.17–2.31; p = 0.0040). In trimester-average models, higher first-trimester maximum WBGT was also associated with higher stress (OR = 1.44; 95% CI = 1.15–1.81; p = 0.0014). DLNMs suggested that relatively cooler daily maximum WBGT values (25th percentile) were associated with decreased odds of stress in early pregnancy, whereas extreme daily maximum WBGT values (99th percentile) showed a pattern consistent with increased odds of stress in mid-to-late gestation. These findings highlight gestational windows in which heat exposure may influence stress, emphasizing the need for further research into underlying mechanisms and effective interventions to protect maternal mental health in heat-vulnerable settings.

White, Lewis [Columbia University] (ORCID:00090005↗

DOE OSTI2026

Quantifying System Strength From Grid-Forming Resources Using Frequency Scan Approach: Preprint

Current industry practices for quantifying the system strength contribution from grid-forming (GFM) resources to ensure stability of power systems dominated by inverter-based resources (IBRs) are primarily based on iterative electromagnetic transient (EMT) time-domain simulation studies. While feasible, these approaches are resource-intensive, lack scalability and intuition, and might not evaluate the system strength contribution over the entire frequency range of interest. This paper introduces a novel, frequency-domain approach to quantify system strength support provided by a GFM resource using frequency scans. The proposed method uses transfer functions from the grid voltage magnitude (V) and phase (?), respectively, to the reactive (Q) and active power (P) output of a GFM resource for quantifying its contribution to system strength. These transfer functions provide a direct measure of the ability of a GFM resource to behave as a stiff voltage source behind a reactance over a specified frequency range, enabling robust quantification of its system strength contribution. The key innovation of this work is the development of a frequency domain system strength metric called the dynamic short-circuit ratio (dSCR) that is suitable for IBR-dominated power systems and is directly related with the familiar short circuit ratio (SCR) metric. The new metric, dSCR, enables the assessment of system strength contributions from both synchronous machines and converter-based GFM resources using a unified benchmark, which is not possible with the traditional SCR metric. The paper also demonstrates how impedance scans could identify if an unstable condition observed during weak grid conditions is a result of the lack active or reactive power support or both. By leveraging the proposed frequency-domain dSCR metric for quantifying system strength contribution from GFM IBRs, the paper demonstrates targeted mitigation strategies for weak grid instabilities without resorting to repeated, time-consuming time-domain simulations. The result is a scalable and efficient approach to remediate stability challenges in power systems with high shares of IBRs and accelerating the integration of GFM technologies for system strength support in power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗

DOE OSTI2025

Photosynthetic capacity is reduced by warming but unaffected by elevated CO2 in seedlings of five boreal tree species

Abstract Increasing atmospheric CO2 concentrations fuel global warming, with boreal regions warming at a faster rate than many other areas. Boreal forests are an important component of the global carbon cycle, yet we have little data on photosynthetic responses of boreal trees to elevated CO2 (EC) and warming. We grew seedlings of 5 widespread North American boreal tree species (from Betula, Larix, Picea, and Pinus) under current (410 ppm) or elevated (750 ppm) CO2 and either ambient (+0 °C) or increased (+4 °C or +8 °C) temperature, then measured photosynthetic traits over a range of leaf temperatures. Our results were generally consistent across species: photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax, and electron transport, Jmax) was unaffected by EC but decreased under +8 °C warming. Accordingly, net photosynthesis measured at the growth CO2 concentration (Agrowth) was reduced under warming and increased under EC. The thermal optimum for Agrowth (ToptA) increased by ∼1.8 °C with EC but increased with warming in only two species. In contrast, the activation energies and thermal optima for Vcmax and Jmax, which are used to estimate photosynthesis in Earth System Models, were unaffected by growth environment. There were a few interactions between growth, CO2, and warming. These results suggest increased photosynthesis of widespread boreal tree species under EC may be offset by future reductions in photosynthetic capacity related to warming. We also show that the temperature sensitivities of parameters used to estimate global photosynthesis in large-scale models are generally unaffected by simulated climate change in these species.

Plant Sciences↗

DOE OSTI2026

Relativistic Magnetohydrodynamic Simulations of Giant Magnetar Bursts

Gradual crustal deformation can generate strongly twisted magnetic fields around magnetars, potentially triggering giant flares with total energies exceeding 10 44 erg. In this letter, we present the first relativistic magnetohydrodynamic simulation of a surface shear-driven magnetar eruption, capturing reconnection-driven plasma heating, the ejection of relativistically hot plasma, and the formation of a hot fireball confined within the inner magnetosphere. We find that magnetic reconnection in the equatorial current sheet launches a hot trailing outflow capable of powering the initial spike observed in giant flares, while simultaneously leaving behind a thermally stratified fireball with sufficient thermal energy to produce the pulsating, decaying tail. Together, these features provide a self-consistent physical framework for understanding the observed energetics of magnetar giant flares. The eruption also expels a magnetically dominated giant plasmoid carrying up to ∼9% of the magnetosphere’s total magnetic energy. Furthermore, our simulation demonstrates how the plasmoid drives the formation of a blast wave—an important ingredient in models linking magnetar eruptions to fast radio bursts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Emissivity measurements of CuCrZr alloy

The Facility for Rare Isotope Beams (FRIB) heavy-ion accelerator, in user operation since 2022, produces rare isotope beams via interactions of high-intensity stable ion beams with a graphite production target. Approximately 20–40% of the primary beam power is deposited in the target, while the remaining 60–80% is absorbed by the beam dump. The minichannel beam dump (MCBD), currently operated at 20 kW and designed for operation up to 50 kW, uses CuCrZr alloy absorber plates. Thermal validation and thermal cycling tests of the MCBD were conducted at the Applied Research Laboratory (ARL) at Pennsylvania State University. Temperature measurements were obtained from an infrared (IR) camera. Since accurate temperature determination requires reliable emissivity values, the emissivity of CuCrZr was measured using the IR camera validated against thermocouple reference temperatures up to approximately 650 °C. The measurements were conducted under a vacuum level of approximately 10 -5 torr to minimize emissivity variations due to surface oxidation. The emissivity of CuCrZr was determined to be 0.057 ± 0.009 using a constant fit to the measured data over the surface temperature range from 100–650 °C.

Accelerator Subsystems and Technologies↗

DOE OSTI2026

The 10 September 2025 M w 4.1 Earthquake in Northeastern Utah, United States: An Archetypal Continental Mantle Event

The 10 September 2025 M w 4.1 earthquake in northeastern Utah, United States, had a focal depth 68 km beneath sea level, which is ∼20–25 km greater than estimates of local crustal thickness, making it a rare example of a continental mantle earthquake (CME). The focal depth is well resolved from arrival-time inversion (nearest station ∼13 km away) and moment tensor inversion of regional waveforms. Similar to other CMEs in the Intermountain West, there were no obvious aftershocks or foreshocks, and the waveforms were enriched in high-frequency energy. Spectral modeling gives a stress drop of ∼80 MPa and a radiation efficiency of ∼0.08, albeit with large uncertainties. The high stress drop and low radiation efficiency are consistent with a dissipative source process such as thermal runaway. Also similar to previous Intermountain West CMEs, the event occurred along the boundary of the Archean Wyoming craton, where pressure–temperature conditions favor ductile deformation. We hypothesize that edge-driven or regional-scale mantle convection produces increased strain rates near the craton boundary that make either conventional brittle failure or thermal runaway feasible at relatively high pressure–temperature conditions. High conductivity inferred around the edge of the craton may suggest that fluids also contribute to CME occurrence.

Koper, Keith D. [Univ. of Utah, Salt Lake City, UT↗

DOE OSTI2026

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗

DOE OSTI2025

What is the Quark-Gluon Plasma made of?

This article surveys our present understanding of the internal structure of the fully developed quark-gluon plasma at temperatures outside the crossover region. The theoretical part of the review covers perturbative and nonperturbative approaches to quark-gluon plasma structure, in particular, hard-thermal loop effective theory, lattice QCD and the functional renormalization group. The phenomenological part of the review scrutinizes the information that has been derived from bulk observables and hard probes in relativistic heavy ion collisions in terms of how it informs our knowledge about the structure of the quark-gluon plasma. The final section lists possible avenues for future progress.

FOS: Physical sciences↗