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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Bioengineered algal lipids enriched in structured medium- and long-chain triacylglycerols, linoleate, and sn -2 palmitate for human milk fat substitutes

Human milk fat (HMF) contains triacylglycerol (TAG) as its primary component, providing over 50% of the calories for infant nutrition, along with structural and bioactive lipids that are important for immune and nervous system development. Palmitic acid, comprising 20-25% of the fatty acid complement of HMF, is predominantly esterified to the sn -2 position on the glycerol backbone. This regiospecific positioning facilitates absorption as 2-palmitoyl-monoacylglycerol after hydrolysis of the fatty acids at sn -1 and sn -2 by gut lipases. Other features of HMF include enrichment in structured medium- and long-chain triglycerides (MLCTs), and variation in the ratio of oleic acid to linoleic acid with maternal diet and geography. We have engineered Auxenochlorella, an oleaginous green alga, for biosynthesis of an MLCT- and sn -2 palmitate-enriched HMF substitute for infant formula, matching the regioisomeric composition and proportions of the most abundant fatty acids in HMF.

Lin, Jon Y-T [University of California, Berkeley;]↗
DOE OSTI2025

Calcium is associated with specific soil organic carbon decomposition products at Blodgett Forest Research Center, Georgetown, California as analysed with scanning transmission X-ray microscopy carbon near-edge X-ray absorption fine structure spectroscopy

This data is from the paper calcium is associated with specific soil organic carbon decomposition products, published in SOIL. DOI: https://doi.org/10.5194/soil-11-381-2025, 2025.This file contains CSVs with spectral data and bulk soil data and there is no specific program required to open this data. The data includes Scanning transmission X-ray microscopy carbon near-edge X-ray absorption fine structure spectroscopy. data from the measurement of samples from the Whole-soil Warming project, run by the Belowground Biogeochemistry team at Blodgett Forest Research Center, Georgetown, California run by the University of California, Berkeley. It also includes bulk soil chemical properties. The University of California's Blodgett Forest Research Station (Forest) is situated in the Sierra Nevada foothills (1370 m a.s.l.) near Georgetown, California. The samples were collected from here: 38.912013, -120.661469, https://maps.app.goo.gl/291bCJ1zVqUhgktz6. The Forest soils were characterised as Alfisols, which are equivalent to Dystric Cambisols (IUSS Working Group WRB, 2015), and formed in granitic parent materials, in a temperate climate, under thinned, mixed-coniferous forest (Fig. S3; Gaudinski et al., 2009). With these analyses we aimed to answer the question, is calcium associated with a specific type of organic matter enriched in aromatic and phenolic carbon at the microscale in samples from Blodgett Forest Research Center? and how does this specific type of carbon respond to experiments targetted at removing and adding calcium to the soils, specifically cation exchange and incubation after calcium addition? Abstract from the paper can be found below: Calcium (Ca) may contribute to the preservation of soil organic carbon (SOC) in more ecosystems than previously thought. Here we provide evidence that Ca is co-located with SOC compounds that are enriched in aromatic and phenolic groups, across different acidic soil-types and locations with different ecosystem properties, differing in terms of climate, parent material, soil type, and vegetation. In turn, this co-localised fraction of Ca-SOC is removed through cation-exchange, and the association is then only re-established during decomposition in the presence of Ca (Ca addition incubation). Thus, highlighting a causative link between decomposition and the co-location of Ca with a characteristic fraction of SOC. Decomposition increases the relative proportion of negatively charged functional groups, which can increase the propensity for the association between SOC and Ca, and in turn, this association inhibits dissolved organic carbon export or further decomposition. We propose that this mechanism could be driven by Ca hotspots on the microscale shifting local decomposition processes and thereby explaining the colocation of Ca with SOC of a specific composition across different acidic soil environments. Incorporating this biogeochemical process into Earth System Models could improve our understanding, predictions, and management of carbon dynamics in soils, and account for their response to Ca-rich amendments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice Projects

The U.S. Department of Energy (DOE) is a long-standing leader in research and development of high-performance computing (HPC) in the pursuit of science. However, we face daunting challenges in fostering a robust and diverse HPC workforce. Basic HPC is not typically taught at early stages of students' academic careers, and the capacity and knowledge of HPC at many institutions are limited. Even so, such topics are prerequisites for advanced training programs, internships, graduate school, and ultimately for careers in HPC. To help address this challenge, as part of the DOE Exascale Computing Project's Broadening Participation Initiative, we recently launched the Introduction to HPC Training and Workforce Pipeline Program to provide accessible introductory material on HPC, scalable AI, and analytics. We describe the Intro to HPC Bootcamp, an immersive program designed to engage students from underrepresented groups as they learn foundational HPC skills. Here, the program takes a novel approach to HPC training by turning the traditional curriculum upside down. Instead of focusing on technology and its applications, the bootcamp focuses on energy justice to motivate the training of HPC skills through project-based pedagogy and real-life science stories. Additionally, the bootcamp prepares students for internships and future careers at DOE labs. The first bootcamp, hosted by the advanced computing facilities at Argonne, Lawrence Berkeley, and Oak Ridge National Labs and organized by Sustainable Horizons Institute, took place in August 2023.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Entanglement Generation in Weakly Driven Arrays of Multilevel Atoms via Dipolar Interactions

We investigate the driven-dissipative dynamics of multilevel atomic arrays interacting via dipolar interactions at subwavelength spacings. Unlike two-level atoms in the weakly excited regime, multilevel atoms can become strongly entangled. Here, the entanglement manifests as the growth of spin waves in the ground-state manifold and survives after turning off the drive. We propose the 2.9 μ⁢m transition between 3 P 2 ↔ 3 D 3 in 88 Sr with 389 nm trapping light as a platform to test our predictions and explore many-body physics with light-matter interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Files and scripts to support manuscript Needham et al. Canopy Gradients of Respiration

This dataset includes the parameter files, relevant output files, and scripts to perform analysis with Jupyter notebooks that support the manuscript Needham et al 2025 “Canopy Gradients of Respiration Drive Plant Carbon Budgets and Leaf Area Index.” We add functionality to the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) to allow flexible vertical gradients of leaf maintenance respiration (Rdark) and maximum carboxylation rate (Vcmax) through the canopy. We test the sensitivity of FATES to canopy gradients in Rdark, both in global simulations to assess broad scale impacts on leaf area index (LAI) and vegetation carbon, and in single site simulations where we assess impacts on plant functional type (PFT) competitive dynamics. Parameter files are netcdf files that can be converted to human readable .cdl files using NCO tools. Analysis scripts are Jupyter notebook files. These can be opened and run using the open source Jupyter notebook software. Model outputs are netcdf files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

Solicitation and Selection of Partner Projects, Technical Team Leads, and Measurement and Validation Contractors for the American Recovery and Reinvestment Act (ARRA) Funded Commercial Building Partnerships (CBP-2)

In March 2010, Pacific Northwest National Laboratory (PNNL) joined two other labs receiving ARRA funding, Lawrence Berkeley National Laboratory (LBNL) and the National Renewable National Laboratory (NREL), to began weekly conference calls with the goal of coordinating a joint lab solicitation to support the ARRA-funded CBP project. Two solicitations were identified for: 1) new CBP Partners; 2) technical contractors to provide technical assistance and measurement and verification (M&V) contractors. The M&V contractors support the work by providing model reviews and conducting monitoring studies to verify building performance. This report documents the process used by the labs for the solicitations, and describes the process and outcomes for PNNL, selection of candidate Partners, technical teams, and M&V contractors.

Nesse, Ronald J.↗
DOE OSTI2024

Laboratory Efficiency Strategies and the Smart Labs Program

Focusing on critical spaces, such as labs, will enable agencies to prioritize federal energy efficiency and decarbonization goals. FEMP's Smart Labs program is an example of emerging efficient laboratory building strategies. The benefits of this program include improved safety and health, reduced energy consumption and carbon emissions, lower operating costs, reduced degradation, and increased retention and recruitment of top talent researchers and sciences. In this session, with the help of our national lab partners, Sandia National Laboratory and Lawrence Berkeley National Laboratory, you will learn about the steps to implement a Smart Labs program of your own and the methods behind the high-performance laboratory building. The partners will share best practices in implementation, practical advice for building a team, and how to address these critical facilities.

decarbonization↗
DOE OSTI2022

Durable Module Materials Consortium (DuraMAT) FY 2021 Annual Report: New Results and a Renewed Consortium

The Durable Module Materials Consortium (DuraMAT) is a multi-lab consortium led by the National Renewable Energy Laboratory, with Sandia National Laboratories, SLAC National Accelerator Laboratory, and Lawrence Berkeley National Laboratory as core research labs. DuraMAT's overarching goal is to discover, develop, de-risk, and enable the rapid commercialization of improved materials, designs, predictive tests, and models for photovoltaic (PV) modules that increase performance, extend lifetime, and enable new applications. Technical results are highlighted throughout this report, and the new projects awarded for FY 2022 address many of the challenges to making 50-year, high-energy-yield modules that were identified in these working groups.

backsheet↗
DOE OSTI2025

The Impact of Alfvénic Shear Flow on Magnetic Reconnection and Turbulence

Magnetic reconnection is a fundamental and omnipresent energy conversion process in plasma physics. Novel observations of fields and particles from Parker Solar Probe (PSP) have shown the absence of reconnection in a large number of current sheets in the near-Sun solar wind. Using near-Sun observations from PSP encounters 4–11 (2020 January–2022 March), we investigate whether reconnection onset might be suppressed by velocity shear. We compare estimates of the tearing mode growth rate in the presence of shear flow for time periods identified as containing reconnecting current sheets versus nonreconnecting times, finding systematically larger growth rates for reconnection periods. Upon examination of the parameters associated with reconnection onset, we find that 85% of the reconnection events are embedded in slow, non-Alfvénic wind streams. We compare with fast, slow non-Alfvénic, and slow Alfvénic streams, finding that the growth rate is suppressed in highly Alfvénic fast and slow wind, and reconnection is not seen in these wind types, as would be expected from our theoretical expressions. These wind streams have strong Alfvénic flow shear, consistent with the idea of reconnection suppression by such flows. This could help explain the frequent absence of reconnection events in the highly Alfvénic, near-Sun solar wind observed by PSP. Finally, we find a steepening of both the trace and magnitude magnetic field spectra within reconnection periods in comparison to ambient wind. We tie this to the dynamics of relatively balanced turbulence within these reconnection periods and the potential generation of compressible fluctuations.

slow solar wind↗
DOE OSTI2023

DuraMAT FY 2022 Annual Report: Towards Predicting Lifetime

The Durable Module Materials Consortium (DuraMAT) launched in November 2016 with five years of funding from the U.S. Department of Energy s (DOE's) Solar Energy Technologies Office (SETO). The program renewed in 2022 for an additional 6 years. DuraMAT is a multi-lab consortium led by the National Renewable Energy Laboratory, with Sandia National Laboratories (Sandia) and Lawrence Berkeley National Laboratory (LBNL) as core research labs. DuraMAT's overarching goal is to accelerate a sustainable, just, and equitable transition to zero-carbon electricity generation by 2035. We work in partnership with our 22-member industry advisory board and the technical management team at SETO. DuraMAT transitioned from our first five-year program into a new six-year program in 2022. It was a very busy year; DuraMAT wrapped up projects from DuraMAT 1 and kicked off new laboratory-led projects for DuraMAT 2. This transition brings new goals and a renewed focus on accelerating the energy transition by improving photovoltaic (PV) module reliability.

accelerated stress testing↗
DOE OSTI2024

DuraMAT FY 2023 Annual Report: Toward Reliability Forecasting

The Durable Module Materials Consortium (DuraMAT) launched in November 2016 with five years of funding from the U.S. Department of Energy s (DOE's) Solar Energy Technologies Office (SETO). The program renewed in 2022 for an additional 6 years. DuraMAT is a multi-lab consortium led by the National Renewable Energy Laboratory, with Sandia National Laboratories (Sandia) and Lawrence Berkeley National Laboratory (LBNL) as core research labs. DuraMAT's overarching goal is to accelerate a sustainable, just, and equitable transition to zero-carbon electricity generation by 2035. 2023 has been a wild ride in the solar industry. Photovoltaic (PV) manufacturing is coming back to the United States, and deployment is booming again. DuraMAT has a unique opportunity to support flourishing manufacturing and deployment over the next couple of years.

durable module↗
DOE OSTI2024

Tackling the Giants: Applying Smart Labs Principles to Constant Air Volume Lab Buildings

Laboratories typically consume 3 to 10 times more energy than similarly sized commercial buildings, and as much as 50% of that energy is wasted by inefficient and poorly operating fume hoods and ventilation systems. One challenge faced by older laboratory buildings is the heating, ventilation, and air-conditioning systems serving many of these buildings. The older systems are usually constant air volume (CAV) systems that maintain constant ventilation rates that cause excess airflow and inefficient energy use. Variable air volume systems can be more efficient systems with sensors to detect the need for a change in volumetric flow rate; however; renovation of ventilation systems can create disruption to ongoing research and operations along with considerable up-front costs. When a Smart Labs program is implemented, an organization has a systems-based management approach that yields a high-performing laboratory building. As decarbonization continues as a priority for sites, buildings with CAV systems are difficult to address. This work centers around practical guidance for improving lab buildings with CAV. In conjunction with industry input on top technology solutions and best practices, recommendations will include performing a laboratory ventilation risk assessment in conjunction with robust retro-commissioning work, which is a crucial step in the Smart Lab process. By applying Smart Labs principles, the aging laboratory building stock of 153,343 (Lawrence Berkeley National Laboratory [LBNL] 2017), comprising roughly 500,000 lab spaces in the United States, can be brought to safe and high-performance operations.

building↗
DOE OSTI2018

Performance Analysis of a Biomass Gasifier Genset at Varying Operating Conditions

Abstract. An All Power Labs PP20 gasifier generation set (Berkeley, Calif.) was tested to evaluate its suitability for powering biomass conversion technologies (BCT) at remote forest operations sites. Feedstock of the species tanoak (), coast redwood (), and Douglas fir () were tested at moisture contents of 15% and 25% (wet basis). The PP20 was connected to a load bank with five different load profiles designed to simulate possible BCT loads. Two parameters of power quality, voltage variability, and frequency deviation, were determined to be within acceptable limits. The unit also successfully powered a remote biochar operation in Branscomb, California. Emissions of the PP20, when compared to diesel generator regulations, would meet non-methane hydrocarbons (NMHC) and NO X requirements but exceed the CO emissions limits by a factor of ten. The CO emissions could be reduced by adding a catalytic converter. The results indicate that it is possible to use a PP20 unit to provide electric power for the highly variable loads of a BCT system. Keywords: Bioenergy, Biomass conversion technology, Gasification, Renewable energy.

Agriculture↗
DOE OSTI2017

A Platform for Genome-Scale Design, Redesign, and Optimization of Bacterial Systems

This project was a collaboration between the Gill and Knight labs at University of Colorado Boulder, the Arkin lab at University of California Berkeley, and the Maness group at the National Renewable Energy Laboratory. There were two main objectives for this project: Objective 1: Integrated, genome-scale strategy for “chassis” strain design and construction. Objective 2: Genome “re-design” framework using multiplex synbio and genome-engineering technologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

DOE Data Day 2019 Report

The Department of Energy (DOE) has joined the larger scientific community in the promotion of data management as a means to higher quality, more efficient research and analysis, and as a critical component of data science. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. They also pose challenges that can be specific to DOE but are common across DOE mission areas and organizations. The DOE Data Day (D3) workshop was held on September 25–26, 2019, and organized by a team at Lawrence Livermore National Laboratory (LLNL) in an effort to gather the data management practitioners at the DOE labs together to share their work and results, facilitating knowledge transfers and best practices across project teams. Over 100 attendees participated from multiple national labs—LLNL, Sandia (SNL), Lawrence Berkeley (LBNL), Los Alamos (LANL), Oak Ridge (ORNL), Argonne (ANL), Pacific Northwest (PNNL), Stanford Linear Accelerator (SLAC)—along with Mission Support and Test Services (MSTS), National Energy Technology Laboratory (NETL), Pantex, National Security Complex (Y-12), National Nuclear Security Administration (NNSA), the DOE Office of Scientific and Technical Information (OSTI), and elsewhere in the DOE complex.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗