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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Effect of pressure cycling and compression rate on the bcc-hcp transition in an FeNi alloy

Here, we investigate the body-centered cubic (bcc) to hexagonal close-packed (hcp) phase transition in Fe-10wt. %Ni alloy, combining pressure cycling and fast compression with time-resolved synchrotron x-ray diffraction in a dynamic diamond anvil cell. Three pressure cycles were conducted with compression rates ranging from 0.1 to nearly 10 3 GPa/s. During the first cycle with the slowest compression, the observed orientations in the bcc and hcp phases are consistent with the Burgers mechanism, followed by c-axis rotation of the hcp phase consistent with {$10$ $\bar{1}2$} twinning. During the following cycles with fast compression at 10 2 –10 3 GPa/s, the hcp phase exhibits negligible c-axis rotation with a nearly constant c/a ratio of ∼1.61 up to ∼30 GPa, indicating suppression of plastic deformation (especially, twinning) due to sample confinement from the gasket. Notably, the onset pressure of the transition decreases with additional pressure cycling and faster compression, which normally leads to over-pressurization. This suggests that defects or shear induced from the pressure cycling reduces the transition kinetics even during fast compression. These insights into the deformation and transition behavior in an FeNi alloy under multiple dynamic loading cycles can offer guidance for future design of advanced structural alloys and improve our understanding of planetary core processes.

FeNi alloy↗
DOE OSTI2025

Phenylpropanoid methyl esterase unlocks catabolism of aromatic biological nitrification inhibitors

Microbial nitrification of fertilizers represents is a significant global source of greenhouse gas emissions. This process increases emissions, fosters toxic algal blooms, and raises crop production costs. Some plants naturally release biological nitrification inhibitors to suppress ammonium-oxidizing microbes and reduce nitrification. Engineering nitrification inhibitor production into food and bioenergy crops via synthetic biology offers a promising mitigation strategy, but its success depends on addressing gaps in our understanding of inhibitor degradation in soil. This study begins to fill this gap by identifying a previously unknown microbial pathway for degrading phenylpropanoid methyl esters, a key class of aromatic nitrification inhibitors. Using transcriptomics and high-throughput functional genomics, we discovered genes essential for phenylpropanoid methyl ester degradation. Genetic and biochemical analyses revealed two novel enzymes, including a newly identified phenylpropanoid methyl esterase, that direct phenylpropanoid methyl esters into known metabolic pathways. Importantly, transferring these genes into bacteria capable of metabolizing other phenylpropanoids enabled them to use the methyl esters as a carbon source. This work provides critical insights into microbial nitrification inhibitor degradation, a poorly understood element of the nitrification cycle.

Genetic Engineering↗
DOE OSTI2024

Design, Synthesis, and Validation: Genome Scale Optimization of Energy Flux through Compartmentalized Metabolic Networks in a Model Photosynthetic Eukaryotic Microbe (Final Report)

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methodology and modeling, coupled with high throughput genome engineering strategies, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require significant advances in methodological throughput as well as improvements in detailed systems biology understanding of photosynthetic processes and cellular metabolism. The ability of diatoms to thrive in upwelling-induced, periodically nutrient-rich conditions makes them the base for the world’s shortest and most energy-efficient food webs. Diatom photosynthesis is estimated to account for between 25% and 40% of the 45-50 billion tons of organic carbon fixed annually in the sea.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

First Solar Thermal Energy Planner (STEP 1) and Nationwide Industrial Heat and Power Analysis

The First Solar Thermal Energy Planner (STEP 1) and Nationwide Industrial Heat and Power Analysis (aka the STEP 1 Project or the Project) aimed to (1) developed a brand-new web tool that could provide decision support through free, rapid techno-economic analysis of behind-the-meter solar+storage systems for industrial process heat and (2) conduct high-level analyses of the cost-competitiveness of the same systems across the US in key sectors. The STEP 1 web tool collects key location, land availability, thermal load profile, proccess heat temperature and media, and other key parameters through an easy-to-use user interface (UI). The UI was designed to meet the user at their level of understanding by minimizing the number of required inputs as much as possible while including options for more nuanced inputs if the user desires. STEP 1 advises users on which solar thermal tehcnologies that could fit their needs based on the inputs provided (namely process media and temperature). The tool can model a wide range of solar thermal technologies including flat plate collectors, evacuated tubes, parabolic troughs, linear Fresnel, and molten salt towers all with corresponding thermal energy storage (TES) - solar PV with resistive heating and TES is also included. Once the parameters are collected, a nominal thermal energy production profile for the facility's location is generated using NREL's System Advisory Model (SAM) and then passed to a modified version of NREL's REopt platform to optimize the size (capacity) and dispatch of the solar+storage system to minimize lifecycle costs subject to energy balance, fuel and electricity rates, emission reductions goals, and other constraints. This entire process takes less than 20 minutes, is completely free, requires zero coding skills, and provides the user with a high-level assessment on the techno-economic feasability of deploying solar+storage systems for their energy needs. In addition to the development of the STEP 1 web tool, the project completed two complementary analyses focused on leveraging the backend code of STEP 1, public industrial facility locations and fuel consumption data, and key sector information to assess the economic opportunity of reducing fuel costs at various levels of capacity factor around the US. Both analyses found that there are key markets, locations, industrial sectors (namely food and beverage), and degrees of offset where solar thermal technologies could be cost-effectively deployed, highlighting a key market entry point for these technologies, and assessing deployment potential. In summary, the STEP 1 project improved the opportunities for solar+storage systems to expand into the industrial process heat market through breaking down barriers to assessing these technologies.

14 SOLAR ENERGY↗
DOE OSTI2024

Quantifying Phospholipids in Organic Samples Using a Hydrophilic Interaction Liquid Chromatography–Inductively Coupled Plasma High-Resolution Mass Spectrometry (HILIC-ICP-HRMS) Method

Here, in this study, a novel method using hydrophilic interaction liquid chromatography (HILIC) coupled with inductively coupled plasma high-resolution mass spectrometry (ICP-HRMS) was introduced for the quantification of phospholipids in oil samples. The method employed a bridged ethyl hybrid (BEH) stationary phase HILIC column with a tetrahydrofuran (THF)/water mobile phase, enhancing the solubility and detection of phospholipids. During the study, a gradient/matrix effect on ICP-HRMS sensitivity was observed and successfully compensated for experimentally, ensuring reliable quantification results. This approach has proven effective for a wide range of different oil samples including vegetable oils, animal fats, and phospholipid supplements. Notably, this method allowed the direct quantification of phospholipids in oil samples, bypassing the need for prior sample preparation methods, such as solid phase extraction (SPE), thereby streamlining the analytical process. The precision, accuracy, and reduced need for extensive sample preparation offered by this method mark a significant advancement in lipids analysis. Its robustness and broad applicability have substantial implications for industries such as food and renewable energy production, where both efficient and accurate lipid identification and quantification are crucial.

09 BIOMASS FUELS↗
DOE OSTI2025

Demonstration and technoeconomic analysis of dodecanol production from acetate using metabolically engineered Escherichia coli

In a circular bioeconomy, the one-way conversion of petroleum to chemicals and CO 2 is replaced with processes that reduce CO 2 to energy carriers and useful materials that are returned to CO 2 upon combustion. A circular bioeconomy that relies on photosynthesis to generate sugars as the chief energy carrier and precursor to chemical building blocks has yet to overcome many recalcitrant aspects of plant-based photosynthesis, namely, high feedstock costs, arable land scarcity, food competition, and fertilizer overuse. Acetate is a potential sustainable energy carrier because it can be produced from CO 2 either electrocatalytically or by acetogens via the Wood-Ljungdahl pathway. Here, in this work, we conducted a metabolic engineering study of Escherichia coli 's ability to convert acetate into dodecanol as a model oleochemical product. We performed techno-economic and life cycle analyses to determine break-even points with alternative fossil fuel-based strategies and identified critical process performance parameters for supporting an industrial acetate-based bioprocess. These analyses showed that oleochemical yield is the primary driver of minimum oleochemical selling price and carbon intensity. Therefore, to increase yield on acetate, we deleted the aceBAK operon, which facilitates funneling of acetate into biomass instead of product. We performed additional strain engineering to increase flux towards dodecanol and increase acetate uptake. Finally, we demonstrated increased yield in controlled bioreactors, improving from 13% of the maximum theoretical yield to 37%. Rigorous uncertainty analyses assuming a range of market conditions and future technological performances resulted in 88% and 37% of simulated scenarios having lower carbon intensities than fossil fuel-based routes and lower minimum selling prices than the market price.

Acetate↗
DOE OSTI2026

Application of Peracetic Acid in Poultry Processing: Effects of Treatment Dynamics and Emerging Risk of Resistance Development in Salmonella spp.

Nontyphoidal Salmonella is a leading cause of foodborne illness, with poultry representing a major source. Peracetic acid (PAA), a widely adopted antimicrobial in poultry processing, offers advantages over traditional disinfectants but has sparked interest in its combined use with other antimicrobials and potential resistance. This review evaluates the efficacy of PAA in mitigating Salmonella in combination with other food-grade antimicrobials, explores possible synergism, efficacy under varying treatment parameters, resistance development against PAA, and its role in resistance evolution. While PAA demonstrates broad-spectrum efficacy, its performance varies with environmental parameters; higher temperatures generally enhance antimicrobial action but also accelerate PAA degradation. Organic matter diminishes PAA efficacy by reactive quenching. Variability in concentration and contact time further influences outcomes. Despite its oxidative mode of action and presumed low risk for resistance, emerging studies indicate that Salmonella can develop adaptive tolerance and potential cross/coresistance following repeated or sublethal exposure to PAA. These adaptations may involve genetic upregulation of oxidative stress response pathways, efflux systems, and modifications in cell membrane integrity, raising concerns about the long-term sustainability of PAA use. Additionally, combinatorial treatments (e.g., PAA with UV-C, enzymes, or organic acids) show promise in enhancing efficacy while mitigating resistance risks. Despite recognition of PAA's safety and effectiveness, knowledge gaps remain regarding standardized resistance definitions, serotype-specific tolerance, and optimal intervention strategies in commercial settings. Therefore, there is a need for standardized testing protocols, robust studies on potential resistance, and further exploration of synergistic PAA applications to ensure sustained poultry product safety and public health protection.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2024

Site-specific Design Case Study for Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels

Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.

09 BIOMASS FUELS↗
DOE OSTI2026

Interaction between the emerging components of online shopping and in-person activities: insights from a behavioral survey

The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.

Digital Divide↗
DOE OSTI2025

Meeting liquid biofuel and bioproduct goals: biotechnological design of the intermediate oilseeds pennycress and camelina, and beyond

The European Union and the United States have set ambitious goals to produce biofuels as part of broader decarbonization and energy security initiatives. One of the more feasible routes to liquid biofuels production is the conversion of seed oils [triacylglycerols (TAGs)] to renewable diesel, biodiesel, and sustainable aviation fuel (SAF) using the hydrotreated ester and fatty acids (HEFA) process. Camelina and pennycress are attractive oilseed feedstocks in that they can be grown in the offseason as intermediate crops on tens of millions of hectares of farmland annually, providing ecosystem benefits and not competing with established food crops. Considerably more TAG could be produced by engineering vegetative portions of crops such as sorghum and miscanthus to accumulate economically-viable amounts. Here, this review highlights recent advances in developing pennycress and camelina as intermediate oilseed crops not only for biofuels production but for making higher value oils such as those enriched in astaxanthin, vitamin E, and medium-chain fatty acids. Given the magnitude of renewable liquid fuel demands, we also describe how advances in oil production from vegetative parts of biomass crops can complement intermediate oilseed cropping systems to meet biofuel and bioproduct targets.

biomass crops↗
DOE OSTI2025

Multigene engineering in plants: Technologies, applications, and future prospects

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AI-aided plant engineering↗
DOE OSTI2024

Low Field NMR Relaxometry Characterization of Water Adsorption in Corn Stover Anatomical Fractions

Low magnetic field 2 MHz NMR relaxometry is applied to measure water adsorption processes of intact corn stover anatomical fractions. Comparison with high magnetic field 250 MHz relaxometry of milled corn stover fractions as a function of water activity, i.e., relative humidity, provides insight into the microstructural changes of the biomass and varying water molecular dynamics during adsorption. As a result, the data presented establish a basis for low field portable NMR of biomass in situ in field and processing environments.

09 BIOMASS FUELS↗
DOE OSTI2024

Effect of Temperature and pH on Microbial Communities Fermenting a Dairy Coproduct Mixture

Organic-rich industrial residues can serve as renewable feedstocks for the generation of useful products by microbial fermentation. We investigated fermenting communities enriched in a mixture of ultra-filtered milk permeate (UFMP) and acid whey from cottage cheese (CAW), two dairy coproducts rich in lactose. To evaluate how operational pH and temperature affect microbial communities and fermentation products, we operated 12 bioreactors for 140 days, each fed a 1:1 mixture of UFMP and CAW at either 35 °C or 50 °C and at either a pH of 4.8 or 5.5. The bioreactors operated at a pH of 4.8 resulted in the incomplete conversion of lactose, while those operated at a pH of 5.5 consistently fermented lactose, primarily into lactic, acetic, and hexanoic acids. The metagenomic analyses revealed that microbial communities obtained at a pH of 5.5 were dominated by lactic acid-producing organisms. Additionally, an inverse relationship was found between the abundance of chain elongating organisms and lactic acid accumulation, with 50 °C reducing the abundance of these organisms and enhancing lactic acid yields. We conclude that the pH and temperature are important determinants of the fermentation of dairy coproducts with a pH of 5.5 and 50 °C yielding the most promising results for lactic acid production. Additional research is required to better understand the factors affecting functional consistency of the process.

Biotechnology & Applied Microbiology↗
DOE OSTI2025

Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi

Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.

Lipid production↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗