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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scintillator Manufacture

This paper will be a literature review of composite scintillators, and their applications in neutron detection. Due to the shortage of 3 He, the traditional medium for neutron detection, new materials need to be explored for this purpose. Composite scintillating particle materials can serve as a solution to replace 3 He, offering faster response times, and all-solid-state form factors for diversified applications in the field. This project will include manufacturing Europium (Eu) and Sodium (Na) doped LiCaAlF 6 (LiCAF) scintillating particles for neutron detection.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Anion exchange membrane test protocol validation

This study presents the validation of protocols for measuring ion exchange capacity (IEC) and alkaline stability of anion exchange membranes (AEMs) for low-temperature water electrolysis. While protocols are often tested within individual laboratories, their results across multiple laboratories with varying equipment, environmental conditions, and personnel qualification remain unverified. The validation involved Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), and University of Oregon (UO) using the same commercially available AEM to assess reproducibility and reliability of the protocols under diverse conditions. For the IEC protocol, results across laboratories were consistent within ±10% of the NMR-determined reference value. The alkaline stability protocol could pose greater challenges due to factors such as variations in sample collection timing, preservation methods, and analytical techniques, but consistent test results for percentage IEC loss were demonstrated across institutions. These results highlight the reliability and applicability of the protocols, emphasizing the importance of validation to ensure consistency in diverse research environments.

08 HYDROGEN↗
DOE OSTI2025

An Integral-based Technique to Accelerate the Monte Carlo Radiative Transfer Computation for Supernovae

We present an integral-based technique (IBT) algorithm to accelerate supernova (SN) radiative transfer calculations. The algorithm utilizes “integral packets,” which are calculated by the path integral of the Monte Carlo (MC) energy packets, to synthesize the observed spectropolarimetric signal at a given viewing direction in a 3D time-dependent radiative transfer program. Compared to the event-based technique (EBT) proposed by M. Bulla et al., our algorithm significantly reduces the computation time and increases the MC signal-to-noise ratio (S/N). Using a 1D spherical symmetric Type Ia SN ejecta model DDC10 and its derived 3D model, the IBT algorithm has successfully passed the verification of spherical symmetry and cross comparison on a 3D SN model with the direct-counting technique and EBT. Notably, with our algorithm implemented in the 3D MC radiative transfer code SEDONA, the computation time is faster than EBT by a factor of 10−30, and the S/N is better by a factor of 1.5−3, with the same number of MC quanta.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quantum chaos, integrability, and late times in the Krylov basis

Quantum chaotic systems are conjectured to display a spectrum whose fine-grained features (gaps and correlations) are well described by random matrix theory (RMT). We propose and develop a complementary version of this conjecture: quantum chaotic systems display a Lanczos spectrum whose local means and covariances are well described by RMT. To support this proposal, we first demonstrate its validity in examples of chaotic and integrable systems. We then show that for Haar-random initial states in RMTs the mean and covariance of the Lanczos spectrum suffice to produce the full long-time behavior of general survival probabilities including the spectral form factor, as well as the spread complexity. In addition, for initial states with continuous overlap with energy eigenstates, we analytically find the long-time averages of the probabilities of Krylov basis elements in terms of the mean Lanczos spectrum. This analysis suggests a notion of eigenstate complexity, the statistics of which differentiate integrable systems and classes of quantum chaos. Lastly, we clarify the relation between spread complexity and the universality classes of RMT by exploring various values of the Dyson index and Poisson distributed spectra.

Combinatorics↗
DOE OSTI2026

Chromaticity compensation of a ghost collider

The GHOST collider final focus system is a pure quadrupole-drift beamline targeting ¿* = 2 mm at four serial interaction points, with peak ¿-functions reaching ~65 km in the final triplet. Beam tracking simulations reveal that a nominal bunch develops a pronounced C-shape in longitu- dinal phase space at IP1, with ¿¿ growing from 0.15 mm to ~2.3 mm — a factor of ~15× increase that directly re- duces luminosity. The mechanism is identified as chromat- ically amplified betatron path length: off-momentum par- ticles acquire enlarged betatron amplitudes in the high-¿ final triplet, generating excess path length via the geometric ¿¿ = - 1/2 ¿ (¿'2 + ¿'2) ¿¿ integral. Within the monoenergetic Balandin framework, ¿2¿2 with ¿ = - 1/2 ¿ ¿ ¿¿ dominates ¿2¿2 by over four million times; the full beam ¿¿ is a fur- ther factor of ~27 larger, driven by ¿¿ -induced chromatic amplitude growth. Phase-advance scans confirm that ¿¿ is insensitive to the apochromatic (¿ ˜ 0) condition. When combined with the geometric hourglass effect, this distor- tion poses a challenge to maximizing luminosity within the current lattice design.

Gamage, B. [Thomas Jefferson National Accelerator ↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modeling frequency instability in high-quality resonant experiments

Modern resonant sensing tools can achieve increasingly high quality factors, which correspond to extremely narrow linewidths. In such systems, time variation of the resonator’s natural frequency can potentially impact its ability to accumulate power and its resulting sensitivity. One such example is the Dark SRF experiment, which utilizes superconducting radio frequency (SRF) cavities with quality factors of Q ∼ 10 10 . Microscopic deformations of the cavity lead to stochastic jittering of its resonant frequency with amplitude 20 times its linewidth. Naively, one may expect this to lead to a large suppression in accumulated power. In this work, we study in detail the effects of frequency instability on high-quality resonant systems, utilizing the Dark SRF experiment as a case study. We show that the timescale of jittering is crucial to determining its effect on power accumulation. Namely, when the resonant frequency varies sufficiently quickly, the system accumulates power as if there were no jittering at all. This implies that the sensitivity of a jittering resonator is comparable to that of a stable resonator. In the case of Dark SRF, we find that jittering only induces a ∼ 10 % loss in power. Our results allow the dark-photon exclusion bound from Dark SRF’s pathfinder run to be refined, leading to a constraint that is an order of magnitude stronger than previously reported (corresponding to a signal-to-noise ratio that is four orders of magnitude larger). This result represents the world-leading constraint on dark photons over a wide range of masses below 6 μ eV and translates to the best laboratory-based limits on the photon mass m γ < 2.9 × 10 - 48 g .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Circadian clock control of ribosome composition promotes rhythmic translation and termination fidelity

Ribosome composition is dynamic, shifting with cell state and stress, but whether it varies with circadian time is unknown. Here, we uncover circadian clock-driven changes in ribosome composition in Neurospora crassa . Mass spectrometry of ribosomes across circadian time identified six ribosomal proteins and one associated factor under clock control. Rhythms in eL31 abundance were validated in purified ribosomes, and deletion of el31 disrupted translation rhythms in nearly half of rhythmically translated mRNAs. N. crassa eL31 promotes circadian control of translation termination and impacts elongation fidelity while maintaining Mg homeostasis, a key determinant of translational accuracy. These findings reveal that the circadian clock reprograms ribosome composition to orchestrate rhythmic translation and fidelity, temporally expanding the proteome beyond the static genome to align cellular function with time of day.

circadian clock↗
DOE OSTI2026

Geometric decoherence time in Lindbladian dynamics

The onset of decoherence in open many-body systems lacks a dynamical timescale grounded in the loss of bipartite entanglement. Here, we introduce the geometric decoherence time, defined as the earliest moment the monotone relation between logarithmic negativity and Rényi-$\frac{1}{2}$ entropy—exactly equal across any bipartition for pure states—breaks down under open-system evolution, signaling entropy growth without accompanying entanglement growth. We establish this criterion in both single-particle Gaussian dynamics and many-body Lindbladian evolution. We show that quantum mutual information provides a complementary long-time diagnostic: Its asymptotic vanishing is equivalent to factorization of the steady state across the bipartition, a condition strictly stronger than separability, and whenever a product steady state is approached exponentially in trace norm, negativity and mutual information share the same decay rate. In the presence of a strong symmetry, this tracking can fail—residual classical correlations can survive after entanglement has vanished. In the Kitaev chain with balanced gain and loss, we derive a closed-form solution and show that the topological phase sustains longer coherence times than the trivial phase at identical dissipation, with a local minimum at the chiral-symmetric point. In the interacting XXZ chain, exact many-body evolution shows that local 𝑍 dephasing preserves residual classical correlations, whereas gain and loss restore the mutual-information tracking of negativity. Furthermore, our results establish the geometric decoherence time as a dynamical scale tracking the onset of decoherence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Modeling suggests SARS-CoV-2 rebound after nirmatrelvir-ritonavir treatment is driven by target cell preservation coupled with incomplete viral clearance

In a subset of SARS-CoV-2-infected individuals treated with the antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of N-R near the time of symptom onset, coupled with incomplete viral clearance, appears to be the main factor leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by the time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. A comparison with an untreated cohort suggests that early treatments with nirmatrelvir-ritonavir may be associated with a delay in the onset of an adaptive immune response. Nevertheless, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment to a 10-day regimen may greatly diminish the chance of rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Multiscale geographically and temporally weighted regression (MGTWR): exploring the spatiotemporal heterogeneity of EV market adoption

As an innovative vehicle technology, electric vehicles are experiencing growing sales and have made significant inroads into the traditional automotive market in the United States and around the world. However, EV adoption rates vary significantly across space and over time, influenced by a complex interplay of socio-economic and infrastructural factors alongside federal and state policies. Here, this paper presents a comprehensive spatial–temporal investigation of EV market adoption within one city in the US, that of Chicago, utilizing Multiscale Geographically and Temporally Weighted Regression (MGTWR) alongside Multiscale Geographically Weighted Regression (MGWR). The aim is to unravel the spatial and temporal dynamics affecting EV adoption and to explore how the influence of various determinants of EV adoption, such as demographic factors and economic conditions, vary spatially. Moreover, by utilizing MGTWR, we provide insights into the evolution of these relationships over time, offering a predictive outlook on future EV market growth. Our findings, with an 86.6% prediction accuracy for EV market adoption, tailored policy measures to support accelerated EV adoption. Methodologically, this work advances MGWR frameworks by integrating temporal dynamics to examine nonstationary processes in spatially disaggregated contexts. These findings offer evidence–based guidance for policymakers, urban planners, and stakeholders in the automotive industry, supporting the transition toward a more sustainable and efficient transportation system.

EV Market Adoption↗
DOE OSTI2024

Bottomonium suppression from the three-loop QCD potential

We compute the suppression of bottomonium in the quark-gluon plasma using the three-loop QCD static potential. The potential describes the spin-averaged bottomonium spectrum below threshold with a less than 1% error. Within potential nonrelativistic quantum chromodynamics and an open quantum systems framework, we compute the evolution of the bottomonium density matrix. The values of the quarkonium transport coefficients are obtained from lattice QCD measurements of the bottomonium in-medium width and thermal mass shift; we additionally include for the first time a vacuum contribution to the dispersive coefficient γ . Using the three-loop potential and the values of the heavy quarkonium transport coefficients, we find that the resulting bottomonium nuclear modification factor is consistent with experimental observations, while at the same time reproducing the lattice measurements of the in-medium width. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The Myth of Fungible FTE: A Quantitative Assessment of Matrixed Resource Allocation

Matrix organizations allow scientific facilities to share specialized personnel across projects, operations, maintenance, and strategic initiatives. Nominal staffing allocations, however, may not capture the schedule consequences of fragmented individual commitments, limited access to specialist groups, and intermittent availability of key decision makers. We developed a stochas- tic, daily-time-step simulation of a hypothetical medium-sized accelerator-facility project com- prising sequential phases and parallel tasks. Each task requires role-specific work measured in FTE-days. Ordinary personnel may be unavailable because they contribute concurrently to other institutional activities, while designated key roles have independently specified daily un- availability probabilities. An organization-wide priority factor scales the number of people from each functional group who can effectively contribute to the project. It is interpreted as a composite proxy for project access and workforce fragmentation across competing commit- ments. We examined project completion time as a function of this factor and Project Lead unavailability using 100 Monte Carlo runs per condition. Increasing priority factor from 0.1 to 1.0 reduced median completion time from 1708.5 days (interquartile range 1681.5–1735.25) to 390 days (interquartile range 379–399). At priority factor = 0.1, increasing Project Lead unavailability from 0.5 to 0.9 increased median completion time from 1713.5 days (interquartile range 1691–1733.25) to 4,417 days (interquartile range 4271.75–4550.5). The model quantifies the commonly expected sensitivity of project schedules to fragmented resource commitments and limited coordination availability. Within this model, the results also indicate a possible threshold regime in which small increases in workforce availability yield only modest sched- ule improvements until sufficient capacity becomes accessible, after which project performance improves sharply. With further validation and calibration, this quantitative framework could support resource-allocation decisions during initial project planning and subsequent schedule rebaselining.

Bai, Mei [SLAC National Accelerator Laboratory (SL↗
DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2024

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗
DOE OSTI2025

Combined molecular and spin dynamics simulation of BCC iron with vacancy defects

Utilizing an atomistic computational model, which handles both translational and spin degrees of freedom, combined molecular and spin dynamics simulations have been performed to investigate the effect of vacancy defects on spin wave excitations in ferromagnetic iron. Fourier transforms of space- and time-displaced correlation functions yield the dynamic structure factor, providing characteristic frequencies and lifetimes of the spin wave modes. A comparison of the system with a 5% vacancy concentration with pure lattice data shows a decrease in frequency and a decrease in lifetime for all transverse spin wave excitations observed. In addition, the clearly defined transverse spin wave excitations are distorted with the introduction of vacancy defects, and we observe reduced excitation lifetimes due to increased magnon–magnon scattering. We observe further evidence of increased magnon–magnon scattering, as the peaks in the longitudinal spin wave spectrum become less distinct. Finally, similar impacts are observed in the vibrational subsystem, with a decrease in characteristic phonon frequency and flattening of lattice excitation signals due to vacancy defects.

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

Towards high-efficiency particle detection using superconducting microwire arrays

Here, we present a detailed study of an 8-channel 1×1 mm 2 WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV hadron beam and 120 GeV muon beam at the CERN Super Proton Synchrotron H6 beamline. Following up on our first detailed characterization of the efficiency and response of an SMSPD fabricated on a 3 nm WSi film, we report measurements of enhanced particle detection efficiency using a sensor fabricated from a thicker 4.7 nm-thick WSi film. We also report the first SMSPD detection efficiency measurement made for muons. Measurements are enabled by a silicon tracking telescope providing 10 μm in-situ spatial resolution. The results show a fill factor-normalized detection efficiency of 75% and a time resolution of about 130 ps across pixels. These findings represent a significant advancement toward developing high-efficiency SMSPD charged particle tracking systems with simultaneous precision timing, with potential applications in future accelerator-based experiments such as the FCC-ee and Muon Collider.

Cryogenic detectors↗
DOE OSTI2024

Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (~7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.

54 ENVIRONMENTAL SCIENCES↗