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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 OSTI2023

Leveraging staggered tessellation for enhanced spatial resolution in high-granularity calorimeters

Here we advance the concept of high-granularity calorimeters with staggered tessellations, underscoring the effectiveness of a design incorporating multifold staggering cycles based on hexagonal cells to enhance position resolution. Moreover, we introduce HEXPLIT, a sub-cell re-weighting algorithm tailored to harness staggered designs, resulting in additional performance improvements. By combining our proposed staggered design with HEXPLIT, we achieve an approximately twofold enhancement in position resolution for neutrons across a wide energy range, as compared to unstaggered designs. These findings hold the potential to elevate particle-flow performance across various forthcoming facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Leveraging the High Flux Isotope Reactor for nuclear fuel development: a review of experiments, facilities, and capabilities

Materials testing reactors (MTRs) have been used to develop in-core nuclear fuels and materials since the outset of the nuclear power industry. However, the closure of prominent MTRs worldwide and protracted construction timelines for new facilities have increased reliance on existing infrastructure for near-term irradiation testing needs. One facility that can support these needs is the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. HFIR boasts the highest steady-state neutron flux in the Western Hemisphere and, among other roles, has been used to rapidly administer high fluences on fuels and materials for fission and fusion reactor applications. This paper reviews HFIR facilities and infrastructure, fuel-bearing irradiation experiments conducted in HFIR, and select nonfueled experiments that demonstrate advanced techniques transferable to fuels experiments. Collectively, these examples underscore HFIR's potential role as a nuclear fuels testbed supporting both the existing reactor fleet and advanced reactor fuel development.

Fuel qualification↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Leveraging computational thermodynamics to guide SiC-ZrC chemical vapor deposition process development

Here using the CALPHAD approach to understand zirconium carbide deposition, a series of phase equilibria were calculated from a custom thermodynamic database based on a literature source, and the equilibria were used to explore the potential chemical vapor deposition (CVD) processing space in the ZrCl 4 -CH 3 SiCl 3 -CH 4 -H 2 system as a function of pressure, temperature, and gas composition. Several gas ratios were considered. At a given ZrCl 4 :CH 3 SiCl 3 ratio within the range studied, the most important factor was found to be the ratios of CH 4 :ZrCl 4 , wherein the nature of the composition – carbide vs. silicide – could be controlled. A pure binary composition of ZrC and SiC is expected to form by increasing the initial amount of methane and decreasing the amount of hydrogen from values predicted purely based on thermodynamic equilibrium. Rietveld analysis of the x-ray diffractograms from corresponding experimental depositions confirmed that increasing the CH 4 :ZrCl 4 ratio increased the fraction of carbon-containing species (SiC, ZrC) and decreased the fraction of non-carbides (ZrSi, ZrSi 2 , etc.), as predicted from the CALPHAD results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Metabolic engineering of β-oxidation to leverage thioesterases for production of 2-heptanone, 2-nonanone and 2-undecanone

Medium-chain length methyl ketones are potential blending fuels due to their cetane numbers and low melting temperatures. Biomanufacturing offers the potential to produce these molecules from renewable resources such as lignocellulosic biomass. Here, we designed and tested metabolic pathways in Escherichia coli to specifically produce 2-heptanone, 2-nonanone and 2-undecanone. We achieved substantial production of each ketone by introducing chain-length specific acyl-ACP thioesterases, blocking the β-oxidation cycle at an advantageous reaction, and introducing active β-ketoacyl-CoA thioesterases. Using a bioprospecting approach, we identified fifteen homologs of E. coli β-ketoacyl-CoA thioesterase (FadM) and evaluated the in vivo activity of each against various chain length substrates. The FadM variant from Providencia sneebia produced the most 2-heptanone, 2-nonanone, and 2-undecanone, suggesting it has the highest activity on the corresponding β-ketoacyl-CoA substrates. We further tested enzyme variants, including acyl-CoA oxidases, thiolases, and bi-functional 3-hydroxyacyl-CoA dehydratases to maximize conversion of fatty acids to β-keto acyl-CoAs for 2-heptanone, 2-nonanone, and 2-undecanone production. In order to address the issue of product loss during fermentation, we applied a 20% (v/v) dodecane layer in the bioreactor and built an external water cooling condenser connecting to the bioreactor heat-transferring condenser coupling to the condenser. Finally, using these modifications, we were able to generate up to 4.4 g/L total medium-chain length methyl ketones.

2-Heptanone↗
DOE OSTI2020

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Engineering improved ethylene production: Leveraging systems biology and adaptive laboratory evolution

Ethylene is a small hydrocarbon gas widely used in the chemical industry. Annual worldwide production currently exceeds 150 million tons, producing considerable amounts of CO 2 contributing to climate change. The need for a sustainable alternative is therefore imperative. Ethylene is natively produced by several different microorganisms, including Pseudomonas syringae pv. phaseolicola via a process catalyzed by the ethylene forming enzyme (EFE), subsequent heterologous expression of EFE has led to ethylene production in non-native bacterial hosts including E. coli and cyanobacteria. However, solubility of EFE and substrate availability remain rate limiting steps in biological ethylene production. We employed a combination of genome scale metabolic modelling, continuous fermentation, and protein evolution to enable the accelerated development of a high efficiency ethylene producing E. coli strain, yielding a 49-fold increase in production, the most significant improvement reported to date. Furthermore, we have clearly demonstrated that this increased yield resulted from metabolic adaptations that were uniquely linked to the EFE enzyme (WT vs mutant). Our findings provide a novel solution to deregulate metabolic bottlenecks in key pathways, which can be readily applied to address other engineering challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging Radiofrequency Identification Success Beyond Hazardous Material Inventory Management at a National Laboratory

Effective inventory management can be overshadowed by conflicting priorities in organizational procedures, particularly in research-focused institutions such as national laboratories that handle expensive, delicate, and hazardous materials. Here, this study investigated the potential of radiofrequency identification (RFID) technology, currently used for hazardous chemical inventory, in applications with higher metal interference and absorption, specifically pressure release device (PRD) compliance and nuclear container management, at Lawrence Livermore National Laboratory (LLNL). This study was done to document best practices to enhance inventory identification speeds for inventory reconciliation and inventory recall and to explore optimal configurations for RFID implementation compared to traditional manual methods of equipment management. Tests were conducted to determine the ideal RFID tag orientation (read at angles of 0°, 90°, and 270°), various container layouts (linear, separated, curved, operational), and ID methods such as manual, barcode, and RFID performing three trials per method per orientation. Results indicated that 0° was the optimal read angle for minimizing metallic interference, and the operational and curved arrangements significantly outperformed the linear and separated configurations in read speed. 3D printed mounts were developed and tested, increasing the read range of the RFID reader by up to 235% in cases of high metallic interference. The RFID technology demonstrated an average speed increase of 65% over a simplified manual identification, which supports the conclusion that RFID is a more efficient method for large hazardous inventory management and equipment reconciliation. Additionally, capturing meta-data, such as location and date, can be used to query for inventory recall and automated updating of record information.

42 ENGINEERING↗
DOE OSTI2024

Unleashing the Potential of Fast Charging Batteries: Leveraging Anion Redox Chemistry in Ni- and Co-Free Cathodes

Designing Li-ion battery cathodes free from critical raw materials such as Co and Ni has a huge technological and societal impact. Though anion redox-based Li-rich oxide cathodes allow designing Co and Ni free cathode compositions, the Li-rich oxides witnessed voltage fade, voltage hysteresis, and irreversible oxygen release despite their high capacity. Conversely, anion redox through highly covalent chalcogenides (S/Se) is emerging due to the improved covalency between metal d and ligand p bands. Here, we investigate the tuning of multi-chalcogen (S/Se) p-band and redox-active metal d-band in a model Li-rich chalcogen composition Li 1.13 Ti 0.57 Fe 0.3 S 2-y Se y (y = 0 - 1) through in-depth electrochemical, X-ray spectroscopy, and DFT-based electronic structure investigations. Introducing the appropriate amount of Se p band character in anion redox sulfides increases interlayer distance and metal - ligand covalency without modifying the original crystal structure, promoting significant electrochemical reversibility through mixed anionic (Se 2- /Se n- , S 2- /S n- , wherein n<2) and cationic (Fe 2+ /Fe 3+ ) redox reactions. Here we show the detailed Fe, S, and Se redox contributions during Li insertion/extraction through X-ray Absorption (XAS) and Hard X-ray Photoemission Spectroscopy (HAXPES) measurements. The orbital tuning approach improves rate capability for more than 10 C charge-discharge rate, exhibiting more than 50% of its original capacity obtained at C/20 rate. The buffer cation in the lattice (Ti 4+ ) remains electrochemically inactive even after significant Se p-band introduction in the sulfide framework. Overall, this work takes advantage of multi-anion redox chemistry to uncover practically demanding fast charging-discharging characteristics in intercalation cathodes. The obtained knowledge of this design can be extended to other oxide and chalcogen cathodes for high performance Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Leveraging Polymorphism in YbCuBi to Map Transport and Elastic Properties

AMX Zintl compounds with the hexagonal ZrBeSi structure have gained significant attention for their remarkable vacancy tolerance and low thermal conductivity. Their 2D honeycomb sublattice, composed of M–X covalent bonds, is believed to contribute to high anharmonicity and unusual thermal transport properties. In this study, we explore the temperature-dependent polymorphism of YbCuBi as a model system to investigate the relationship between the structure and elastic and thermal transport properties in AMX Zintls. YbCuBi undergoes a structural transition from the “flat” Cu–Bi layers in the ZrBeSi structure to corrugated layers in the LiGaGe structure below 410 K, resulting in a distortion of its centrosymmetric structure. To probe the effects of this crystallographic transition, we employ inelastic neutron scattering and temperature-dependent resonant ultrasound spectroscopy. These experimental findings, coupled with first-principles calculations and thermal conductivity measurements, allow us to elucidate a direct relationship between corrugation of the honeycomb lattice and the observed changes in elastic and thermal transport properties. These insights can be extended to other Zintl phases with similar structure types, providing a platform for the rational design of functional materials with tailored thermal properties.

Chemical structure↗
DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗