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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of ionization peak location on measured opaqueness in DIII-D H-mode plasmas

This study investigates the relationship between electron pedestal density and the location of the ionization peak on neutral penetration in DIII-D H-mode plasmas, utilizing a database of Lyman-α emission measurements. The high electron density leads to neutrals being ‘screened’ and the ionization front being pushed out into the Scrape-Off Layer (SOL). This is also referred to as the neutral opaqueness, which is heuristically expected to scale with edge plasma density and machine size. However, at lower electron pedestal density, the penetration depth of the neutrals varies, and measured opaqueness deviates from the heuristic scaling. The database reveals that at low density, when the ionization peak is located in SOL region, the linear relationship between the electron density and neutral penetration holds. However, when the peak is located inside the separatrix, the penetration of the neutrals (λ n 0 ) is much wider ~3.0–3.5 cm, breaking the heuristic opaqueness approximation. These findings provide valuable insights into fueling efficiency and plasma behavior, with implications for Fusion Pilot Plants where high pedestal densities are anticipated and where the neutral opaqueness behaves like its heuristic approximation. This analysis offers a framework to refine neutral opaqueness approximations, enhancing the predictive capability for advanced tokamak operations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Muon tracking in a LiquidO opaque scintillator detector

LiquidO is an innovative radiation detector concept. The core idea is to exploit stochastic light confinement in a highly scattering medium to self-segment the detector volume. In this paper, we demonstrate event-by-event muon tracking in a LiquidO opaque scintillator detector prototype. The detector consists of a 30 mm cubic scintillator volume instrumented with 64 wavelength-shifting fibres arranged in an 8 × 8 grid with a 3.2 mm pitch and read out by silicon photomultipliers. A wax-based opaque scintillator with a scattering length of approximately 0.5 mm is used. The tracking performance of this LiquidO detector is characterised with cosmic-ray muons and the position resolution is demonstrated to be 450 μm per row of fibres. These results highlight the potential of LiquidO opaque scintillator detectors to achieve fine spatial resolution, enabling precise particle tracking and imaging.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Characterization of a radiation detector based on opaque water-based liquid scintillator

Here, we present the characterization of a novel radiation detector based on an opaque water-based liquid scintillator. Opaque scintillators, also known as LiquidO, are made to be highly scattering, such that the scintillation light is effectively confined, and read out through wavelength-shifting fibers. The 1-liter, 32-channel prototype demonstrates the capability for both spectroscopy and topological reconstruction of point-like events. The design, construction, and evaluation of the detector are described, including modeling of the scintillation liquid optical properties and the detector’s response to gamma rays of several energies. A mean position reconstruction error of 4.4 mm for 1.6 MeV-equivalent events and 7.4 mm for 0.8 MeV-equivalent events is demonstrated using a simple reconstruction approach analogous to center-of-mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The Two Arctic Wintertime Boundary Layer States: Disentangling the Role of Cloud and Wind Regimes in Reanalysis and Observations During MOSAiC

The wintertime central Arctic atmosphere comprises a radiatively clear and a radiatively opaque state, which are linked to synoptic forcing and mixed-phase clouds. Weather and climate models often lack process representations surrounding these states, but prior work mostly treated the problem as an aggregate of synoptic conditions, resulting in partially overlapping biases. Here, we disaggregate the Arctic states and confront ERA5 reanalysis with observations from the MOSAiC campaign over the central Arctic sea ice during winter 2019/2020. Low-level winds and liquid water path (LWP) are combined to derive different synoptic classes. Results show that the clear state is primarily formed by weak/moderate winds and the absence of liquid-bearing clouds, while strong winds and enhanced LWP primarily form the radiatively opaque state. ERA5 struggles to reproduce these basic statistics, shows too weak sensitivity of thermal radiation to synoptic forcing, and overestimates thermal radiation for similar LWP amounts. The latter is caused by a warm bias, which has a pronounced inversion structure and is largest in clear and calm conditions. Under strong synoptic forcing, the warm bias is constant with height and discrepancies in mixed-phase cloud altitude appear. Separating synoptic conditions is regarded as useful for process-oriented evaluation of the Arctic troposphere in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Efficacy of Gadolinium Gallium Garnet (GGG) as a High-Impedance Optical Window for Shock Wave Experiments

We conducted a series of plate impact experiments to examine the efficacy of < 111>-oriented gadolinium gallium garnet (GGG) single crystals as high-impedance optical window for Photonic Doppler velocimetry (PDV) under shock and double-shock loading. At ~ 123 GPa, shocked GGG remains fully transparent to 1550 nm light for at least 250 ns without any signal degradation. Above 135 GPa, PDV data measured through GGG exhibit a gradual loss of fringe contrast following shock entrance, which eventually leads to transparency loss. The duration for which shocked GGG remains transparent decreases with increasing pressure, and at ~ 148 GPa, it becomes opaque within ~ 20–30 ns. This limits the use of GGG as an interferometry window between ~ 110–140 GPa under single shock loading. Within this pressure range, the refractive index of GGG increases linearly with density: n = 1.552 + 0.054ρ. In contrast to single shock loading, where GGG becomes opaque rapidly above ~ 140 GPa, double-shocked GGG remains optically transparent for over 100 ns when it is first shocked to ~ 123 GPa and then reshocked to significantly higher pressures (215–233 GPa). Our findings raise the exciting possibility of GGG being used as a high-impedance optical window in multi-shock and shock-ramp loading experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗
DOE OSTI2026

Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

Farre-Kaga, Hiro J. [Princeton Univ., NJ (United S↗
DOE OSTI2025

Experimental and theoretical investigation into the high pressure deflagration products of 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105)

Diamond anvil cell (DAC) laser ignition experiments and reactive ab initio molecular dynamics (AIMD) simulations were performed on the high explosive (HE) LLM-105 to investigate its high pressure (HP) deflagration chemistry. Raman and optical spectroscopy measurements reveal LLM-105 reacts into an opaque carbonaceous product at 4–25 GPa. At pressures >~ 27 GPa, the reaction product consists of an amorphous optically transparent solid and nitrogen (N 2 ) in the solid phase. While not a one-to-one comparison due to the small time and length scales, the HP AIMD simulations show that some of the product is molecular N 2 , in qualitative agreement with experiment, while above 20 GPa most of the product consists of large amorphous C x H y N z O k clusters. Clustering is enhanced with pressure and reduces with temperature. In the experiments with initial sample pressure >~ 25 GPa, the pressure within the DAC decreases with minimal change in DAC cavity area. At initial sample pressures of 43.9 GPa, when quenched to 0 K, simulations predict a product experiencing a lower pressure consistent with the experimental measurement at lower load pressures. In conclusion, the results are important for understanding the HP deflagration chemistry of LLM-105.

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

Two-dimensional burn velocity and analysis of burn products of nitromethane at high pressure

We have studied the laser-initiated deflagration of nitromethane (CH 3 NO 2 ) under high static pressure in the diamond anvil cell. Time-resolved images of the deflagrations have been obtained using intensified CCDs (ICCD). In contrast with previous work, we rely on spontaneous emission from the reaction, rather than changes in the speckle pattern produced by artificial illumination of the sample. Furthermore, as opposed to the 1D records obtained previously with streak cameras, ICCDs permit imaging of the burn in two dimensions providing the ability to directly observe anisotropic deflagration behavior. Here, we report several examples of this behavior and discuss its possible origins. We have also investigated the products of the reaction using Raman spectroscopy. At pressures below 25 GPa, the burn product is observed to be opaque and has a Raman spectrum consistent with a carbonaceous soot. At pressures above 25 GPa, the burn product is observed to be transparent (consistent with earlier reports) and its Raman spectrum reveals the presence of molecular N 2 and a nitrogen-carbonate species. The latter species can be recovered to ambient pressure and has a Raman spectrum consistent with ammonium carbonate or ammonium bicarbonate. To aid interpretation of the Raman spectra, measurements have also been made on the isotopologues CH 3 15 NO 2 and 13 CH 3 NO 2 . This work establishes the importance of advanced time-resolved imaging to reveal the details of deflagration under high pressure and further advances our understanding of the corresponding chemistry.

Deflagration↗
DOE OSTI2025

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗
DOE OSTI2025

Plausible indication of gamma-ray absorption by dark matter in NGC 1068

NGC 1068 is the brightest extragalactic source in high-energy neutrinos as seen by IceCube, yet the accompanying gamma-ray flux is orders of magnitude weaker. It has been argued that this indicates that the bulk of neutrinos and gamma rays are emitted in the innermost vicinity of the central supermassive black hole, which is transparent to neutrinos, but opaque to gamma rays. Even in such extreme scenarios for the acceleration of cosmic rays, astrophysical models typically overestimate the low-energy gamma-ray flux and/or require some fine-tuning in the physical parameters. Here, in this work, we suggest instead that the dark matter surrounding the supermassive black hole may absorb the gamma rays, inducing the observed deficit. We show that for a dark matter–photon scattering cross section in the range σ DM−γ /m DM ≃ 10 −28 −10 −30 cm 2 /GeV, Fermi-LAT measurements can be well reconciled with IceCube data. We also present some simple particle physics examples that achieve the correct spectral energy dependence while respecting complementary constraints.

Active Galactic Nuclei↗
DOE OSTI2026

End-To-End Decentralized Transmission Line Protection in IBR-Dominated Weak Grids Using Interpretable Data-Driven Methods

Traditional transmission line protection relies on predictable synchronous-based fault signatures, which frequently fail under the non-standard, current-limited fault characteristics of Inverter-Based Resources (IBRs). This study investigates how to achieve secure, communication-free fault isolation in IBR-dominated weak grids without relying on opaque, computationally heavy "black-box" machine learning algorithms. To address this, we propose a novel, standalone, and inherently interpretable data-driven protection framework. Unlike centralized methods requiring multi-terminal communication, this decentralized approach relies solely on local measurements using a hierarchical linear-kernel Support Vector Machine (SVM). The methodology decomposes the protection task into four sequential stages that mimic traditional protection elements: fault detection and fault direction identification, fault type classification, zone classification, and location estimation. This multi-stage architecture allows for specialized feature engineering at each stage, combining high computational efficiency with logic traceability. The framework's end-to-end performance was validated via C-code and PSCAD/EMTDC co-simulation, utilizing a real-world utility network and an OEM black-box IBR model. The proposed relay achieves 97.2% overall accuracy and provides a reliable trip decision within a 2.5-cycle window. The results confirm 100% accuracy in fundamental fault detection, reliable zone selectivity across low to moderate fault resistances, and robust security against non-fault transients, proving its immediate viability for integration into commercial numerical relays.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

BBN-simple: How to bake a universe-sized cake

Big Bang Nucleosynthesis (BBN), the process of creation of lightest elements in the early universe, is a highly robust, precise, and ultimately successful theory that forms one of the three pillars of the standard hot-Big-Bang cosmological model. Existing theoretical treatments of BBN and the associated computer codes are accurate and flexible, but are typically highly technical and opaque, and not suitable for pedagogical understanding of the BBN. Here we present BBN-simple - a from-scratch numerical calculation of the lightest element abundances pitched at an advanced undergraduate or beginning graduate level. We review the physics of the early universe relevant for BBN, provide information about the reaction rates, and discuss computational-mathematics background that is essential in setting up a BBN calculation. Here, we calculate the abundances of the principal nuclear species in a standard cosmological model, and find a reasonably good agreement with public precision-level BBN codes.

Big bang nucleosynthesis↗
DOE OSTI2024

Particle control via cryopumping and its impact on the edge plasma profiles of Alcator C-Mod

At the high n e proposed for high-field fusion reactors, it is uncertain whether ionization, as opposed to plasma transport, will be most influential in determining n e at the pedestal and separatrix. A database of Alcator C-Mod discharges is analyzed to evaluate the impact of source modification via cryopumping. The database contains similarly-shaped H-modes at fixed I P = 0.8 MA and B t = 5.4 T, spanning a large range in P net and ionization. Measurements from an edge Thomson scattering system are combined with those from a midplane-viewing Ly α camera to evaluate changes to n e and T e in response to changes in ionization rates, S ion ∙ $n^{sep}_e$ and $T^{ped}_e$ are found to be most sensitive to changes to $S^{sep}_{ion}$, as opposed to $n^{ped}_e$ and $T^{sep}_e$. Dimensionless quantities, namely α MHD and v*, are found to regulate attainable pedestal values. Select discharges at different values of P net and in different pumping configurations are analyzed further using SOLPS-ITER. It is determined that changes to plasma transport coefficients are required to self-consistently model both plasma and neutral edge dynamics. Pumping is found to modify the poloidal distribution of atomic neutral density, n 0 , along the separatrix, increasing n 0 at the active X-point. Opaqueness to neutrals from high n e in the divertor is found to play a role in mediating neutral penetration lengths and hence, the poloidal distribution of neutrals along the separatrix. Pumped discharges thus require a larger particle diffusion coefficient than that inferred purely from 1D experimental profiles at the outer midplane.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗
DOE OSTI2026

Self-Assembly Driven Microlithography via Near-Infrared Light Activation

Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗