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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2021

Sparse Approximate Multifrontal Factorization with Butterfly Compression for High-Frequency Wave Equations

In this work, we present a fast and approximate multifrontal solver for large-scale sparse linear systems arising from finite-difference, finite-volume or finite-element discretization of high-frequency wave equations. The proposed solver leverages the butterfly algorithm and its hierarchical matrix extension for compressing and factorizing large frontal matrices via graph-distance guided entry evaluation or randomized matrix-vector multiplication-based schemes. Complexity analysis and numerical experiments demonstrate $\mathcal{O}(N\log^2 N)$ computation and $\mathcal{O}(N)$ memory complexity when applied to an $N\times N$ sparse system arising from 3D high-frequency Helmholtz and Maxwell problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Biochemical Conversion of Herbaceous Biomass to Renewable Diesel: Biorefinery Marginal Air Quality Impacts and Comparison to Feedstock Production

This study assesses the air quality impacts of an advanced biorefinery that produces renewable diesel blendstock (RDB) from lignocellulosic biomass via aerobic respiration (Davis et al. 2022) by estimating fine particulate matter (PM2.5) impacts from biorefinery emissions. It continues a prior analysis that used a geospatial assessment to identify source regions for biomass feedstocks and studied the impact of feedstock production emissions on air quality (Thind et al. 2022). Thind et al. (2022) identified RDB biorefineries that can use corn stover feedstocks of 2,000; 5,200; and 9,100 dry metric tons per day (DMT/day), based in Iowa, and suggested 7 unique counties can serve as hosts for a biorefinery that draws biomass feedstock from neighboring counties. Given 13 unique county-biorefinery size combinations and two waste lignin end uses at the biorefinery (lignin as a fuel for electricity generation and lignin for pellet production), the air-quality-related sustainability aspects of each of these 26 scenarios are assessed by estimating the annual average impacts of biorefinery emissions on the dispersion and formation of secondary PM2.5 in the atmosphere using a novel reduced-complexity air quality model called the Intervention Model for Air Pollution (InMAP). The 26 biorefinery design combinations help capture how a biorefinery's emissions of air pollutants and their resulting impact on local and regional air quality are influenced by the magnitude of production scale, lignin utilization strategy, and location of a proposed biorefinery. Methods developed in Thind et al. (2022) are applied to estimate the constraints on primary PM2.5 and secondary PM2.5 precursor emissions based on compliance with U.S. Environmental Protection Agency's (EPA's) annual primary National Ambient Air Quality Standard (NAAQS) of PM2.5 (i.e.12.0 micrograms per cubic meter (microgram/m3)) at downwind receptors of a biorefinery. Incremental PM2.5 concentrations caused by the emission of biorefining corn stover into RDB are assessed and compared to those of corn stover production. To illuminate which upstream supply chain stage of renewable diesel production contributes most to air quality impacts, marginal PM2.5 concentrations are compared between both stages at multiple downwind air quality monitor locations. In addition, through a hotspot analysis, we identify the primary contributing factors of emissions within the feedstock production and biorefinery stage operations. In doing so, we provide insights for improving the air pollutant emission-related sustainability of advanced lignocellulosic biofuel production.

09 BIOMASS FUELS↗
DOE OSTI2021

SANS quantification of bound water in water-soluble polymers across multiple concentration regimes

Contrast-variation small-angle neutron scattering (CV-SANS) is a widely used technique for quantifying hydration water in soft matter systems, but it is predominantly applied in the dilute regime or for systems with a well-defined structure factor. Here, CV-SANS was used to quantify the number of hydration water molecules associating with three water-soluble polymers with different critical solution temperatures and types of water–solute interactions in dilute, semidilute, and concentrated solution through the exploration of novel methods of data fitting and analysis. Multiple SANS fitting workflows with varying levels of model assumptions were evaluated and compared to give insight into SANS model selection. These fitting pathways ranged from general, model-free algorithms to more standard form and structure factor fitting. In addition, Monte Carlo bootstrapping was evaluated as a method to estimate parameter uncertainty through simulation of technical replicates. The most robust fitting workflow for dilute solutions was found to be form factor fitting without CV-SANS ( i.e. polymer in 100% D 2 O). For semidilute and concentrated solutions, while the model-free approach can be mathematically defined for CV-SANS data, the addition of a structure factor imposes physical constraints on the optimization problem, suggesting that the optimal fitting pathway should include appropriate form and structure factor models. The measured hydration numbers were consistent with the number of tightly bound water molecules associated with each monomer unit, and the concentration dependence of the hydration number was largely governed by the chemistry-specific interactions between water and polymer. Polymers with weaker water–polymer interactions ( i.e. those with fewer hydration water molecules) were found to have more bound water at higher concentrations than those with stronger water–polymer interactions due to the increase in the number of forced water–polymer contacts in the concentrated system. This SANS-based method to count hydration water molecules can be applied to polymers in any concentration regime, which will lead to improved understanding of water–polymer interactions and their impact on materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

LA17-V-InverseRadTransportProblem-PD2Kb. Final Project Report

In the inverse radiation transport problem, a radiation signal emanating from an unknown object is used to reconstruct geometric and material parameters of the object , e.g., the mass of special nuclear material (SNM), the isotopic composition of the SNM (e.g., the enrichment of 235U), the thickness of shielding, etc. The inverse radiation transport problem is one of the most fundamental and important in the emergency response mission, and has been addressed in previous NA-22 and NA-42/84 investments by using derivative-free optimization methods. Our project was aimed at (i) consolidating previous work by applying polynomial chaos expansion (PCE) for reducing significantly the number of forward model simulations required by derivative-free optimization methods; (ii) developing a general-purpose framework and stand-alone software for solving the inverse radiation transport problem by applying a new “predictive modeling” methodology (predictive modeling of coupled multi-physics systems, PM_CMPS) which enables the fusing of experimental and computational information; and (iii) developing several benchmarks (for unscattered gamma rays) for demonstrating the quantification of non-Gaussian features (e.g., skewness and kurtosis, which characterize asymmetries and long tails) of response distributions by deterministically and efficiently propagating parameter uncertainties (covariances and higher-order moments, where available) using high-order sensitivities computed by the recently developed comprehensive adjoint sensitivity analysis methodology (C-ASAM). With regard to (i), application of PCE, the most significant accomplishment of this project was an implementation of PCE into INVERSE that significantly reduced computation time for the DiffeRential Evolution Adaptive Metropolis (DREAM) and Differential Evolution (DE) methods. For inverse transport problems with measurements of neutron leakage, neutron multiplication, and neutron-induced gamma-rays, the solution times for the DREAM and DE methods were reduced by factors of 30-300, reducing run times from hours to minutes while maintaining the same accuracy as the standard (non-PCE) DREAM and DE. Speedups were further improved by exploring sparse grid quadrature sets using the Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (Tasmanian) toolset. An initial exploration of sparse grids indicated that further speedups factors of 5-10 are obtainable. The implementation of PCE in INVERSE is not in a form that users can easily access. With regard to (ii), solving the inverse problem using a predictive modeling paradigm, the most significant accomplishment of this project were the derivation and implementation of adjoint-based second derivatives in neutron and photon transport theory. There are two codes that compute these derivatives. SENSPG computes them for uncollided gamma rays and SENSMG computes them for neutron leakage. SENSMG is open-source code available on github. Other significant accomplishments included a stand-alone code, MULTI-PRED, that implements the predictive modeling paradigm, along with a detailed manual. The code was implemented in the existing INVERSE code for inverse problems, but a head-to-head comparison of inverse methods was not achieved. We also derived and implemented the capability to compute sensitivities for multiplicity measurements into the SENSMG code, and this capability was used in a data assimilation application, but it was not used for solving inverse problems. With regard to (iii), developing benchmarks to demonstrate the importance of high-order sensitivity and uncertainty analysis, the most significant accomplishment of this project were two test problems that showed how important second-order sensitivities are.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2024

O-GlcNAc transferase regulates collagen deposition and fibrosis resolution in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic pulmonary disease that is characterized by an excessive accumulation of extracellular matrix (ECM) proteins (e.g. collagens) in the parenchyma, which ultimately leads to respiratory failure and death. While current therapies exist to slow the progression, no therapies are available to resolve fibrosis. We characterized the O-linked N-Acetylglucosamine (O-GlcNAc) transferase (OGT)/O-GlcNAc axis in IPF using single-cell RNA-sequencing (scRNA-seq) data and human lung sections and isolated fibroblasts from IPF and non-IPF donors. The underlying mechanism(s) of IPF were further investigated using multiple experimental models to modulate collagen expression and accumulation by genetically and pharmacologically targeting OGT. Furthermore, we hone in on the transforming growth factor-beta (TGF-β) effector molecule, Smad3, by co-expressing it with OGT to determine if it is modified and its subsequent effect on Smad3 activation. We found that OGT and O-GlcNAc levels are upregulated in patients with IPF compared to non-IPF. We report that the OGT regulates collagen deposition and fibrosis resolution, which is an evolutionarily conserved process demonstrated across multiple species. Co-expression of OGT and Smad3 showed that Smad3 is O-GlcNAc modified. Blocking OGT activity resulted in decreased phosphorylation at Ser-423/425 of Smad3 attenuating the effects of TGF-β1 induced collagen expression/deposition. OGT inhibition or knockdown successfully blocked and reversed collagen expression and accumulation, respectively. Smad3 is discovered to be a substrate of OGT and its O-GlcNAc modification(s) directly affects its phosphorylation state. These data identify OGT as a potential target in pulmonary fibrosis resolution, as well as other diseases that might have aberrant ECM/collagen accumulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time-of-Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple-phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light- and voltage-dependent time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of lateral perovskite heterostructures. While ToF-SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non-negative matrix factorization and no-negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light- and voltage-dependent dynamics. Finally, combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lethality is Local, but Survival is Systemic: Temporal and Multi-Organ Responses to Chlorine Gas Exposure in a Murine Model

Chlorine gas (Cl2) is a highly toxic chemical associated with both localized lung injury and systemic health effects. While pulmonary damage has been well characterized, the systemic inflammatory and metabolic responses remain poorly understood. We aimed to define the temporal and multi-organ responses to Cl2 exposure in a murine model, with a focus on identifying spatiotemporal inflammation and its impact on survival and lethality. SKH1 mice were exposed for 10 min to varying concentrations of Cl2 (94.4–810 ppm, representative of non-lethal, LD10, and LD50 doses) and monitored for respiratory function, perfusion, and acidosis using organ-specific imaging. At multiple time points (40 min, 6 h, 24 h, and 7 d), we measured phosphoproteins, cytokines, chemokines, growth factors, and metabolic hormones in the lungs, heart, cortex, and plasma. Statistical modeling and logistic regression were used to identify biomarkers associated with lethality and survival. We found that lung injury was the primary cause of potential lethality, particularly via early phosphoprotein signaling disruptions. However, survival correlated with early systemic coordination of inflammatory and metabolic signals across organs. Perfusion and acidosis imaging were strongly associated with chemokine and hormone responses. Key survival-associated plasma biomarkers included decreased insulin, increased ghrelin, and decreased eotaxin. While potential lethality from Cl2 exposure is locally driven by pulmonary injury, survival depends on systemic, multi-organ responses that occur rapidly post-exposure. Within this model, our findings identify a potential therapeutic window to enhance survival and suggest candidate biomarkers that may be explored translationally for both triage and treatment of chlorine-related incidents.

chlorine gas↗
DOE OSTI2022

The Impact of Freeze‐Thaw History on Soil Carbon Response to Experimental Freeze‐Thaw Cycles

Abstract Freeze‐thaw is a disturbance process in cold regions where permafrost soils are becoming vulnerable to temperature fluctuations above 0°C. Freeze‐thaw alters soil physical and biogeochemical properties with implications for carbon persistence and emissions in Arctic landscapes. We examined whether different freeze‐thaw histories in two soil systems led to contrasting biogeochemical responses under a laboratory‐controlled freeze‐thaw incubation. We investigated controls on carbon composition through Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS) to identify nominal carbon oxidation states and relative abundances of aliphatic‐type carbon molecules in both surface and subsurface soils. Soil cores (∼60 cm‐depth) were sampled from two sites in Alaskan permafrost landscapes with different in situ freeze‐thaw characteristics: Healy (>40 freeze‐thaw cycles annually) and Toolik (<15 freeze‐thaw cycles annually). FT‐ICR‐MS was coupled with in situ temperature data and soil properties (i.e., soil texture, mineralogy) to assess (a) differences in soil organic matter composition associated with previous freeze‐thaw history and (b) sensitivity to experimental freeze‐thaw in the extracted cores. Control (freeze‐only) samples showed greater carbon oxidation in Healy soils compared with Toolik, even in lower mineral horizons where freeze‐thaw history was comparable across both sites. Healy showed the most loss of carbon compounds following experimental freeze‐thaw in the lower mineral depths, including a decrease in aliphatics. Toolik soils responded more slowly to freeze‐thaw as shown by intermediary carbon oxidation distributed across multiple carbon compound classes. Variations in the response of permafrost carbon chemistry to freeze‐thaw is an important factor for predicting changes in soil function as permafrost thaws in high northern latitudes.

Rooney, Erin C.↗
DOE OSTI2022

Effect of the grain arrangements on the thermal stability of polycrystalline nickel-rich lithium-based battery cathodes

One of the most challenging aspects of developing high-energy lithium-based batteries is the structural and (electro)chemical stability of Ni-rich active cathode materials at thermally-abused and prolonged cell cycling conditions. Here, we report in situ physicochemical characterizations to improve the fundamental understanding of the degradation mechanism of charged polycrystalline Ni-rich cathodes at elevated temperatures (e.g., ≥ 40 °C). Using multiple microscopy, scattering, thermal, and electrochemical probes, we decouple the major contributors for the thermal instability from intertwined factors. Our research work demonstrates that the grain microstructures play an essential role in the thermal stability of polycrystalline lithium-based positive battery electrodes. We also show that the oxygen release, a crucial process during battery thermal runaway, can be regulated by engineering grain arrangements. Furthermore, the grain arrangements can also modulate the macroscopic crystallographic transformation pattern and oxygen diffusion length in layered oxide cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A gain series method for accurate EMCCD calibration

Abstract Calibration of the gain and digital conversion factor of an EMCCD is necessary for accurate photon counting. We present a new method to quickly calibrate multiple gain settings of an EMCCD camera. Acquiring gain-series calibration data and analyzing the resulting images with the EMCCD noise model more accurately estimates the gain response of the camera. Furthermore, we develop a method to compare the results from different calibration approaches. Gain-series calibration outperforms all other methods in this self-consistency test.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Co-regulation of water and energy in the spatial heterogeneity of drought resistance and resilience

Vegetation resistance and resilience to drought are linked to the stability of terrestrial ecosystems under climate change. However, the factors driving the spatial heterogeneity in drought resistance and resilience remain poorly understood. In the study, we utilized multiple satellite-derived vegetation indices to calculate and analyze changes in drought resistance and resilience across various biomes worldwide. Results indicated that drought resistance showed a significant increase with the increase in water availability, but no significant relationship was observed between drought resistance and energy. In contrast, drought resilience exhibited a significant increase with an increase in energy rather than in water. Furthermore, a negative correlation was observed between drought resistance and resilience across different biomes worldwide, indicating a trade-off between resistance and resilience. However, the strength of the negative correlation varied based on water and energy conditions. These findings provide compelling evidence that water and energy co-regulated the spatial heterogeneity in drought resistance and resilience across the globe. The robust linear relationship between drought resistance and resilience and available water and energy demonstrated in our study is critical to accurately predicting and assessing the impact of climate change on vegetation growth and terrestrial carbon cycling in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Thermophysical modeling of raw glaze liquidus temperature and viscosity

Raw glazes, which do not contain pre-melted glass frits, are widely used due to their low cost. The natural variability of the mineral compositions used in raw glazes affects their viscosity during firing. Molten glaze viscosity plays an important role in determining the final surface quality; therefore, it is important to determine how raw glaze composition and firing conditions affect viscosity. Thermophysical modeling provides a way to evaluate the liquidus temperature and viscosity of molten glazes as a function of composition and oxygen activity. Equilibrium phase volume fraction and composition of 242 raw glaze recipes were evaluated under oxidizing and reducing conditions. Undissolved or precipitation of solid phases explain the secondary flux transition from anti-fluxing to fluxing near the liquidus temperature. The liquidus temperature and viscosity of iron glazes decrease as a function of increasing iron content under a reducing atmosphere. The empirical probability distribution of molten glaze viscosity follows a lognormal distribution, with lead glazes having significantly lower viscosity compared with glazes without lead. Here, the peak firing temperature viscosity of lead-free glazes is near the working point of glass. Multiple linear regression analysis shows that the peak firing temperature viscosity and liquidus temperature significantly predict the viscosity factor.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli–electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

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

BBO-10203 inhibits tumor growth without inducing hyperglycemia by blocking RAS-PI3Kα interaction

BBO-10203 is an orally available drug that covalently and specifically binds to the rat sarcoma (RAS)–binding domain of phosphoinositide 3-kinase α (PI3Kα), preventing its activation by HRAS, NRAS, and KRAS. Here, it inhibited PI3Kα activation in tumors with oncogenic mutations in KRAS or PIK3CA and in tumors with human epidermal growth factor receptor 2 (HER2) amplification or overexpression. In preclinical models, BBO-10203 caused significant tumor growth inhibition across multiple tumor types and showed enhanced efficacy in combination with inhibitors of cyclin-dependent kinase 4/6 (CDK4/6), estrogen receptor (ER), HER2, and KRAS-G12C mutant, including in tumors harboring mutations in Kelch-like ECH-associated protein 1 (KEAP1) and serine/threonine kinase 11 (STK11). Notably, these antitumor effects occurred without inducing hyperglycemia, because insulin signaling does not depend on RAS-mediated PI3Kα activation to promote glucose uptake.

Simanshu, Dhirendra K. [Frederick National Laborat↗
DOE OSTI2022

Reviewing MACCS Capabilities for Assessing Tritium Releases to the Environment

Tritium has a unique physical and chemical behavior which causes it to be highly mobile in the environment. As it behaves similarly to hydrogen in the environment, it may also be readily incorporated into the water cycle and other biological processes. These factors and other environmental transformations may also cause the oxidation of an elemental tritium release, resulting in a multiple order of magnitude increase in dose coefficient and radiotoxicity. While source term development and understanding for advanced reactors are still underway, tritium may be a radionuclide of interest. It is thus important to understand how tritium moves through the environment and how the MACCS accident consequence code handles acute tritium releases in an accident scenario. Additionally, existing tritium models may have functionalities that could inform updates to MACCS to handle tritium. In this report tritium transport is reviewed and existing tritium models are summarized in view of potential updates to MACCS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Dynamic Focusing with High-Quality-Factor Metalenses

Metasurface lenses provide an ultrathin platform in which to focus light, but weak light–matter interactions limit their dynamic tunability. Here we design submicron-thick, ultrahigh quality factor (high-Q) metalenses that enable dynamic modulation of the focal length and intensity. Using full-field simulations, we show that quality factors exceeding 5000 can be generated by including subtle, periodic perturbations within the constituent Si nanoantennas. Such high-Q resonances enable lens modulation based on the nonlinear Kerr effect, with focal lengths varying from 4 to 6.5 μm and focal intensities decreasing by half as input intensity increases from 0.1 to 1 mW/μm 2 . We also show how multiple high-Q resonances can be embedded in the lens response through judicious placement of the perturbations. Our high-Q lens design, with quality factors 2 orders of magnitude higher than existing lens designs, provides a foundation for reconfigurable, multiplexed, and hyperspectral metasurface imaging platforms.

36 MATERIALS SCIENCE↗