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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Science & Technology Review: March 2026 - The Future of Lasers

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Science & Technology Review June 2026: Predicting Tsunamis

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Lawrence Livermore National Laboratory – Innovation and Partnerships Office (IPO): FY24 Technology Transfer Report (2025 Report V3)

Lawrence Livermore National Laboratory’s (LLNL) Innovation and Partnerships Office (IPO) is the focal point for Laboratory engagement with industry. Organizations partner with LLNL to unlock the commercial viability of breakthrough technologies invented at the Laboratory and collaborate to mature them in order to solve complex industry challenges. LLNL technology transfer connects cutting-edge science with real-world applications, empowering businesses to thrive while advancing the nation’s technological edge. We do this by identifying new economic opportunities, protecting LLNL intellectual property (IP) and transferring it to the private sector through licensing and partnerships

42 ENGINEERING↗
DOE OSTI2025

Leveraging a Southern California Energy Innovation Cluster to Pilot and Validate Emerging Energy Technologies

The EPIC Pilot Program, led by the Los Angeles Cleantech Incubator (LACI), was designed to help early-stage clean technology startups accelerate technical validation, investment readiness, and market entry. The project focused on supporting startups in designing and deploying small-scale pilots with the guidance of EPIC Partners, a network of regional stakeholders in the Southern California energy ecosystem, including but not limited to: the Los Angeles Department of Water and Power (LADWP), the California Energy Commission (CEC), Los Angeles County Metropolitan Transportation Authority (LA Metro), and Edison International, among others. Through mentorship, funding, and site access for technology deployments, the program provided a structured pathway for startups to test and refine their technologies in real-world settings.

14 SOLAR ENERGY↗
DOE OSTI2026

Partially Ionized Plasma Physics and Technological Applications

Partially ionized plasma physics has attracted increased attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of manufacturing for computer components. Partially ionized plasma is characterized by a significant presence of neutral particles in contrast to the fully ionized plasma. The theoretical analysis is based upon solutions of the kinetic Boltzmann equation, yielding the non-Maxwellian electron energy distribution function (EEDF), thereby emphasizing the difference with a fully ionized plasma. The impact of the effect on discharges in inert and molecular gases is described in detail, yielding the complex nonlinear phenomena resulting in plasma selforganization. A few examples of such phenomena are given, including the non-monotonic EEDFs in the discharge afterglow in a mixture of argon with the molecular gas NF3; the explosive generation of cold electron populations in capacitive discharges, hysteresis of EEDF in inductively coupled plasmas. Recently, highly advanced computer codes were developed in order to address the outstanding challenges in plasma technology. These developments are briefly described in general terms.

non-Maxwellian electron energy distribution functi↗
DOE OSTI2025

Enhancing lifetime, forecasting, and economic benefits of photovoltaic technologies undergoing UV-induced degradation with optical filtering

Ultraviolet-induced degradation (UV-ID) of various PV cell types was analyzed under optical UV filters with different cutoff wavelengths. Cell types studied included interdigitated back contact (IBC), passivated emitter and rear totally diffused (PERT), and heterojunction technology (HJT) based on crystalline Si (c-Si), and metal halide perovskite (MHP) cells. Analyzing degradation rates in two distinct regimes proved beneficial for all cell types. We used empirical linearizing functions ln(t) for c-Si technologies and 2 √t for MHP samples where t is time. These were applied to extrapolate UV-induced degradation over the lifetime of PV modules under various levels of optical UV filtering and used to predict the relative economic benefits for PV power plants. Degradation rates for all technologies were generally faster under the long pass optical filters having shorter cutoff wavelengths transmitting more UV irradiation and at elevated temperatures when testing MHP samples in the range between 60 °C and 90 °C.

14 SOLAR ENERGY↗
DOE OSTI2024

The Future of Land-Based Wind Turbine Rotor Technology: The Perspective from NREL

The Big Adaptive Rotor (BAR) Project looks at potential innovative pathways for future land-based wind turbine technology. BAR is led by the National Renewable Energy Laboratory and is sponsored by the Wind Energy Technology Office of the US Department of Energy. This talk will describe innovations and challenges faced by the teams working on BAR. The innovations include numerical models predicting the stability and performance of highly flexible blades, downwind rotors to maximize wind farm power, and controlled bending of 100-meter-long blades during rail transport. The challenges of developing predictive numerical models and formulating successful value propositions supporting new technologies such as distributed aero control devices will also be discussed.

BAR↗
DOE OSTI2024

Cybersecurity Considerations for Emerging Energy Technologies

AI, cloud computing, post-quantum cryptography, zero-trust architectures, microgrids, and virtual power plants. What do these things have in common? They are all emerging technologies in the clean energy space that will be a critical part of grid modernization efforts. As we work towards clean energy and decarbonization targets, these technologies, developed to solve real-world problems, will help us reach goals and achieve new efficiencies as the paradigm of grid operation shifts. However, there are growing concerns about the cybersecurity risks associated with these trending topics as they are used in critical infrastructure applications. This talk will cover gaps, challenges, and opportunities for the secure implementation of grid modernization solutions and novel energy applications of state-of-the-art networking and communications. Proactive risk mitigation strategies, including the application of cyber-informed engineering, will be discussed. Practical applications of these techniques will help provide countermeasures to the impact of cyberattacks on critical infrastructure technologies in a new, digitized grid landscape.

14 SOLAR ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: Siva Powers America Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Siva Powers America Inc. for a Technology Commercialization Award. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: EWT Americas Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by EWT Americas, Inc., for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2026

U.S. Department of Energy Competitiveness Improvement Project 2024 Technology Commercialization Awardee: Intelligent Energy Systems

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Intelligent Energy Systems for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed-generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

Clay Reimagined: Phyllosilicates as Future Membrane Technologies

Membrane technologies have made critical advances in resource recovery, water purification, and energy systems. However, it is difficult to systematically tune properties of traditional polymer membranes, and researchers have struggled to deliver challenging, advanced separations using these platforms. In recent years, membranes based on 2D materials have drawn attention for molecular-scale separations due to their unique properties, most notably their tunable nanoscale interlayer properties. Among the diverse family of 2D materials, phyllosilicates, a broad class of naturally abundant clay minerals, offer significant advantages in cost and scalability over synthetic 2D materials, positioning them as promising candidates for advanced membrane technologies. Their inherent structural and chemical properties, strategies for tailoring selective transport pathways, and recent advancements across applications including ion separation, water treatment, and energy conversion are discussed. Finally, key challenges and opportunities are outlined to guide future research in leveraging phyllosilicate membranes for high-performance separation technologies.

energy storage and conversion↗
DOE OSTI2025

Uncertainty in inventories for life cycle assessment: State‐of‐the‐art, challenges, and new technologies

Uncertainty is a critical factor that can hinder the quality and potential applications of life cycle assessment (LCA) results. A prominent source of uncertainty stems from the life cycle inventory (LCI) data. Various methodologies exist to estimate the uncertainty associated with LCI data, primarily based on the widely used structured pedigree matrix approach or the computationally intensive Monte Carlo simulation. This perspective review explores how new technologies (e.g., computational algorithms and data collection methods) from data science and related fields can contribute to identifying, quantifying, and reducing uncertainty in LCI modeling. A brief overview of the sources of uncertainty in LCI modeling and how they are addressed in current LCA practice is provided. Additionally, several new technologies are identified, and the potential benefits of their implementation in reducing uncertainties in LCI modeling are discussed. This perspective review concludes by identifying potential areas that require further development for these technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Investigation of heat pump technologies for high-temperature applications above 250 °C

This paper examines heat pump (HP) technologies to achieve heat supply temperatures above 250 °C, referred to as ultra-high-temperature heat pump (UHTHP). UHTHP, as a low-carbon heat delivery and heat augmentation technology, can offer an alternative to traditional combustion heating for decarbonizing high-temperature industrial processes. However, the heat supply temperature of over 250 °C surpasses the temperature range typically covered in the high-temperature HP literature and existing capabilities of commercial HP systems. This paper reviews HP technologies with the potential to evolve into UHTHP. Additionally, UHTHP studies in the literature are analyzed to compare the pros, cons, and technical potentials of various HPs using different thermodynamic cycles and fluids. Furthermore, we then pinpoint the technical gaps and challenges that have hindered widespread adoption of UHTHP in industry, explore potential improvements and solutions, and discuss the feasibility. Current efforts aim to lay the ground for future research, development, and commercial deployment for UHTHP.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2025

Disruptive thermal-spectrum molten salt breeder reactor fuel cycle technologies

Multiple, disruptive fuel salt processing technologies for thermal-spectrum molten salt breeder reactors (TS-MSBRs) have been conceived of and brought to laboratory levels of maturity since the termination of the historic U.S. government program. This paper describes these technologies and the role that they could serve in decreasing the remaining technical risks for TS-MSBR deployment. In conclusion, the focus of the paper is on describing how the technologies could integrate into a dramatically simpler, proliferation resistant fuel salt processing system as well as identifying remaining development hurdles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

A framework and calculator for evaluating the impacts of shelf life extension and other food loss and waste reduction technologies

Optimization of the food supply chain (FSC) depends on reducing food waste, especially at the consumer stage, where a substantial portion of food is not eaten, but instead disposed of via landfill, incineration, or in-sink disposals. One key strategy is to increase the time that consumers have before food goes bad or expires. This study developed a framework to assess the efficacy of shelf-life extension (SLE) technologies for mitigating food loss and waste (FLW), such as packaging improvements. The impact flows through the entire FSC, reducing FLW, energy use, and other inputs at each stage. The framework and resulting calculator can be used to evaluate the impact of FLW reduction at any stage for any food commodity. As shown by two SLE cases, the calculator is valuable for policy-makers, government entities, and professionals, specifically those in marketing, business development, and capital projects teams, to comprehensively evaluate the impacts of FLW reduction technologies and practices. The framework and calculator are sensitive to the shape of the consumption curve, the fraction of inedible waste, and the current shelf life. The calculator was used to assess the impacts of the United States goal of reducing food waste by consumers through various SLE lengths. It was found that uptake of several near-ready-to-deploy SLE technologies would reduce annual food production demand by about 10–19 MMT and supply chain energy consumption by 240–410 PJ in the United States.

Food loss and waste (FLW)↗