Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distillation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Ceramic Membrane Solar Desal: Pilot System Design and Full-Scale System Techno-Economic Analysis (CRADA Final Report)

This work is conducted in support of the American Made Challenges Solar Prize. The current scope addresses the Design portion of the overall prize competition. This team, led by the University of Connecticut, will design a solar powered, pilot scale, ceramic-based membrane distillation system that can operate on high salinity waters and propose that design for construction in the Test phase of the Prize competition.

14 SOLAR ENERGY↗
DOE OSTI2026

Aviation Fuel Characterization at Operationally Relevant Conditions

To accelerate approval and potentially expand the allowable property range for aviation fuels, we are using high performance computing simulations to reveal fuel property effects on aviation combustor performance. These simulations are supported by fuel property measurements over temperatures and pressures that the fuel experiences in an aircraft engine and by validated chemical kinetics models for SAF combustion. Here we report density, viscosity, and surface tension results for conventional jet fuel and multiple synthetic fuels from -30 degrees Celsius to 200 degrees Celsius (-40 degrees Celsius for viscosity) and 1 atm to 70 atm including an assessment of method repeatability. Properties of surrogate mixtures are also investigated. Distillation, ICN, LHV, flashpoint, and Cp are also reported, and data are being used to develop models to predict fuel properties from composition (GCxGC).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

TCF Base Technology-Specific Final Report: Engineering Enzymes for Crystalline PET Substrate

The primary objective of this project was to develop a new polyethylene terephthalate (PET) hydrolase enzyme to depolymerize relevant PET substrates for Birch Biosciences, using high-throughput protein expression, purification, and assaying systems and machine learning-guided enzyme design. As a secondary project objective, we also aimed to develop a more energy-efficient ethylene glycol (EG) recovery strategy relative to distillation.

09 BIOMASS FUELS↗
DOE OSTI2024

Solid Sorbent Cost Sensitivity Analysis: A Framework for UNF Reprocessing Sorbent Cost Comparison

Solid sorbents have been the subject of research and development across the U.S. Department of Energy national laboratory complex for many years. They are generally accepted as a safer alternative to cryogenic distillation for noble gas capture, and they present an easier pathway in development of long-term waste forms after iodine capture. As more types of sorbents have been proposed for capture of volatile radionuclides, it has become necessary to compare them based on performance and cost criteria. This report details cost and performance information for three promising sorbents and provides a cost sensitivity analysis. The goal of this analysis is to establish a framework which can be utilized to directly compare future sorbents, with differing properties, to the sorbents discussed in this report. Direct comparison is instrumental to making informed decisions to efficiently guide research and minimize laborious detours. In all cases, sorbent capacity is a major cost driver as it influences the mass of sorbent, operational footprint, and disposal cost requirements., However, sorbent price can greatly increase the cost of a capture technology. For sorbents used in krypton capture, the purity of krypton released to storage is the highest cost driver.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Field Insights: Strengthening Digital Assurance Through On-Site Network Monitoring

The accelerating deployment of digital energy infrastructure, ranging from inverter-based resources (IBRs), battery energy storage systems (BESS), to advanced grid control platforms, has brought unprecedented visibility, flexibility, and efficiency to the electric grid. However, this digital transformation also introduces new cybersecurity challenges, particularly in the form of supply chain risks and operational blind spots at the grid edge. Over the past year, the Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), through its Rapid Risk Assessment initiative, along with the Grid Deployment Office (GDO), through its Technical Assistance for Digital Assurance (TADA) initiative, have supported a series of on-site network engagements led by Idaho National Laboratory (INL). These engagements, conducted in partnership with asset owners across the country, have focused on identifying real-world vulnerabilities and misconfigurations in operational environments, many of which are not detectable through remote assessments or traditional compliance audits. The goal of this report is to distill key findings and lessons learned during network hunt engagements from INL’s fiscal year (FY) 2024 - 2025. It is intended to help asset owners—regardless of their participation in the program—better understand the evolving threat landscape and adopt practical measures to secure their digital energy infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

3 He Separation from US Liquid Natural Gas-Derived 4 He (Final Report)

This final report summarizes the technical and economic feasibility study conducted by Interlune, Lisbon Group, and Pacific Northwest National Laboratory on separating 3 He from 4 He derived from liquid natural gas (LNG) using magnetocaloric liquefaction (MCL), superleak heat-flush, and fractional distillation techniques. The project aims to address the growing shortage of 3 He, a critical resource for quantum computing and national security, by exploring a new domestic source, the bulk helium supply.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2025

Integrated Biorefinery of Brewer’s Spent Grain for Second-Generation Ethanol, Mycoprotein, and Bioactive Vinasse Production

Brewer’s spent grain (BSG), the main lignocellulosic by-product of the beer industry, represents an abundant yet underutilized resource with high potential for valorization. This study presents an integrated biorefinery approach to convert BSG into second-generation (2G) ethanol, bioactive vinasse for plant growth promotion, and fungal biomass as a potential mycoprotein source. The biomass was first subjected to biological delignification using the white-rot fungus Ganoderma lucidum, after which two valorization routes were explored: (i) evaluation of the fungal biomass as a mycoprotein candidate and (ii) alcoholic fermentation for ethanol production. For the latter, three pretreatment strategies were assessed (diluted sulfuric acid and two deep eutectic solvents (DESs) based on choline chloride combined with either glycerol or lactic acid) followed by a one-pot enzymatic saccharification and fermentation using Kluyveromyces marxianus SLP1. The highest ethanol yield on substrate (YP/S) was achieved with [Ch]Cl:lactic acid pretreatment (0.46 g/g, 89.32% of theoretical). Vinasse, recovered after distillation, was characterized for organic acid content and tested on Solanum lycopersicum seed germination, showing promising biostimulant activity. Overall, this work highlights the potential of BSG as a sustainable feedstock within circular economy models, enabling the production of multiple bio-based products from a single residue.

Ganoderma lucidum↗
DOE OSTI2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

FOS: Physical sciences↗
DOE OSTI2024

Position-specific kinetic isotope effects for nitrous oxide: a new expansion of the Rayleigh model

Nitrous oxide (N 2 O) is a potent greenhouse gas and the most significant anthropogenic ozone-depleting substance currently being emitted. A major source of anthropogenic N 2 O emissions is the microbial conversion of fixed nitrogen species from fertilizers in agricultural soils. Thus, understanding the enzymatic mechanisms by which microbes produce N 2 O has environmental significance. Measurement of the 15 N/ 14 N isotope ratios of N 2 O produced by purified enzymes or axenic microbial cultures is a promising technique for studying N 2 O biosynthesis. Typically, N 2 O-producing enzymes combine nitrogen atoms from two identical substrate molecules (NO or NH 2 OH). Position-specific isotope analysis of the central (N α ) and outer (N β ) nitrogen atoms in N 2 O enables the determination of the individual kinetic isotope effects (KIEs) for N α and N β , providing mechanistic insight into the incorporation of each nitrogen atom. Previously, position-specific KIEs (and fractionation factors) were quantified using the Rayleigh distillation equation, i.e., via linear regression of δ 15 N α or δ 15 N β against [–f In f / (1 – f)], where f is the fraction of substrate remaining in a closed system. This approach, however, is inaccurate for N α and N β because it does not account for fractionation at N α affecting the isotopic composition of substrate available for incorporation into the β position (and vice versa). Therefore, we developed a new expansion of the Rayleigh model that includes specific terms for fractionation at the individual N 2 O nitrogen atoms. By applying this Expanded Rayleigh model to a variety of simulated N 2 O synthesis reactions with different combinations of normal, inverse, and/or no KIEs at N α and N β , we demonstrate that our new model is both accurate and robust. We also applied this new model to two previously published datasets describing N 2 O production from NH 2 OH oxidation in a methanotroph culture (Methylosinus trichosporium) and N 2 O production from NO by a purified Histoplasma capsulatum (fungal) P450 NOR, demonstrating that the Expanded Rayleigh model is a useful tool in calculating position-specific fractionation for N 2 O synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1961

THERMAL DECOMPOSITION OF HYDRAZINE

The Thermal Stability of Hydrazine has been studied in the temperature and pressure intervals of 175" to 250"C. and 300 to 430 p.s.i., Respectively. Previous studies of the decomposition of hydrazine indicated that several factors affect its stability (2, 5, 7). the rate of decomposition is increased by the presence of certain surfaces (especially metals and salts). pH, oxygen, and carbon dioxide. Further explosive decomposition Is attributable to the uncontrolled heterogeneous, gas-phase decomposition. These factors were considered in selecting the following experimental conditions: highly purified hydrazine was prepared in a nitrogen atmosphere, and this product was decomposed over triply distilled mercury in outgassed borosilicate glass tubes at ullages that were initially zero. This study also included the effects of added quantities of ammonia on the rate of decomposition and an investigation of the products of the decomposition.

Harold W. Lucien↗