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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Enhanced Catalyst Durability for the Oxidative Production of Biobased Chemicals (Cooperative Research and Development Final Report)

This CRADA will facilitate technology maturation for NREL-developed atomic layer deposition (ALD) coated catalyst materials that are tailored for durability during the oxidative production of biobased chemicals. This project will address optimizing process parameters for scaling aluminum oxide (Al 2 O 3 ) ALD coated catalysts, demonstrating ALD coated catalyst performance for biomass oxidation, and validating economic models that project significant cost benefits for ALD-enhanced catalytic processes. This work will strengthen private-public partnerships in the area of advanced catalyst manufacturing for energy-related technology. Critical information will be collected to elevate the Technology Readiness Level and increase our competitiveness for cooperative R&D agreements and licensing. Success of this work will be crosscutting as it can facilitate advanced catalyst development for both renewable and conventional processes.

09 BIOMASS FUELS↗
DOE OSTI2025

Toward a sustainable circular economy of multilayer plastic films: Life cycle and techno-economic assessment with a focus on end-of-life treatment and multiple recovery cycles

This study presents a life cycle assessment (LCA) and techno-economic analysis (TEA) of end-of-life technologies for treating polyethylene–polyamide barrier film waste, focusing on quality degradation across recovery cycles. Novel treatment methods are experimentally validated, while others are drawn from literature and industry consultations. A displacement approach, assuming no quality loss, is first applied. Results show that solvent-based recycling via the solvent-targeted recovery and precipitation (STRAP) process outperforms alternatives across environmental indicators, reducing global warming potential (GWP) by 40% compared to landfilling. Incineration performs worst in most categories, particularly eutrophication (80% higher than landfilling), due to nitrogen emissions. Experimentally validated downcycling (pelletizing) proves more economically viable. The assumption of infinite recoverability is overly optimistic. To address this, we propose a mathematical framework accounting for a finite number of recovery cycles. This refined model shows reduced GWP and cost savings for solvent recovery, making its benefits less pronounced than initially estimated. Sensitivity and uncertainty analyses reveal strong dependence on recovered material quality and solvent recovery efficiency, underscoring the need for optimized process design. Finally, hotspot analysis identifies greenhouse gas emissions from the polyamide supply chain as the dominant GWP contributor. In conclusion, the results underscore potential trade-offs across pathways and show that solvent-based recovery’s sustainability depends heavily on process conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatial Atomic Layer Deposition to Scale Manufacturing of Robust Catalysts for Biomass Conversion Applications: Cooperative Research and Development Final Report, CRADA Number CRD-17-715

This CRADA will facilitate technology maturation for NREL-developed ALD-coated catalyst materials that are tailored for durability during harsh biomass conversion chemistries. This project will address optimizing process parameters for scale-up of Al2O3 ALD-coated catalysts, demonstrating ALD-coated catalyst performance for muconic acid hydrogenation, and validating economic models that project significant cost benefits for ALD-enhanced catalytic processes. This work will strengthen private-public partnerships in the area of advanced catalyst manufacturing for energy-related technology. Critical information will be collected to elevate the Technology Readiness Level and increase our competitiveness for cooperative R&D agreements and licensing. Success of this work will be crosscutting as it can facilitate advanced catalyst development for both renewable and conventional processes.

09 BIOMASS FUELS↗
DOE OSTI2022

Techno-Economic Analysis Using REopt for Community Solar on Multifamily Affordable Housing Properties [Slides]

Multifamily affordable housing (MFAH) providers can identify and prioritize properties in their portfolios for which community solar development is feasible by following portfolio screening steps in a process outlined by NREL. Once MFAH providers have identified the most feasible sites, they can conduct more detailed analyses for a select number of sites to assess how distributed energy resources such as solar plus storage can help them meet their goals. The step is completed using NREL's REopt (https://reopt.nrel.gov/tool), a free techno-economic optimization model that determines DER sizes and dispatch strategies that minimize the life cycle cost of energy at a site. This presentation describes how to perform this step using REopt to help MFAH providers answer questions such as: What size solar PV system will result in the most energy bill savings at this site? What size solar-plus-storage system would be needed to power critical loads through a utility grid outage? What is the financial impact of rate switching, net metering, and/or meter aggregation? What percentage of the site's load can be offset with renewable energy? What are the emissions benefits of this renewable generation?

14 SOLAR ENERGY↗
DOE OSTI2025

Use of Captured CO2 for Production of Sustainable Polyurethane Foams in Automotive Applications

Captured CO2 has been investigated as a feedstock for the production of polyurethane (PU) foams for automotive applications. Previous work has shown successful incorporation of CO2 into polyols via three distinct reaction pathways. These CO2-derived polyols, as well as several commercially available polyols with varying CO2 content, were used to produce PU foams for automotive seating and NVH (noise, vibration, and harshness) applications. Foam formulations were optimized to maximize sustainable content while maintaining manufacturability constraints and performance requirements for each end use application. PU foams were first formulated at lab scale to confirm free rise and molded foam properties, then scaled up to pilot and industrial scales to evaluate production manufacturing feasibility. Finally, CO2-derived foams were used to produce molded components for end use product validation. Life cycle assessment (LCA) was used to quantify the environmental impact of using captured CO2 and bio-renewable content into polyols and PU foams through the metrics of global warming potential and embodied energy. Tradeoffs among environmental impacts, energy usage, manufacturability, and PU foam performance from the incorporation of CO2 and bio-renewable content will be discussed.

Lee, Ellen [Ford Motor Company]↗
DOE OSTI2025

A Hybrid Fuel Cell and Battery Storage Power Management for Grid-Interactive EV Charging Station

With the increasing adoption of renewable energy sources in grid-interactive Electric Vehicle (EV) charging stations, the role of energy storage systems has become critical. While large energy storage systems have mitigated the intermittency of renewable energy, integrating multi-source energy management with prioritized charging can further enhance the reliability of charging stations (CS). This paper presents a decentralized energy management (DEM) approach combining battery energy storage (BES) and fuel cell (FC) systems using a rule-based line resistance correction droop (LRCD) control technique. The proposed droop control dynamically adjusts the gain to balance the state-of-charge (SoC) of the BES, enhancing power support longevity and improving battery life under varying capacity conditions by reducing current stress. Additionally, the paper addresses the challenges of using fuel cells in linear regions to optimize efficiency and manage various charging scenarios. The CS integrates unity power factor grid interaction, and power support for auxiliary loads, maintaining harmonic distortion within 5% during grid islanding. The approach evaluates DC bus voltage regulation under various scenarios of PV array power fluctuations and dynamic load variations, in both grid-connected and standalone operations. In conclusion, the proposed control strategy is validated on a laboratory prototype through various dynamic load variation and grid islanding scenarios.

Khalid, Mohd [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Technical Routes to Achieve High Circular Polarized Luminescence in Chiral Perovskites: A Mini‐Review

Chiral hybrid organic-inorganic perovskites (HOIPs) have emerged as promising materials for optoelectronic and spintronic applications, leveraging unique properties such as circularly polarized luminescence (CPL), circularly polarized nonlinear optical (NLO) emission, and chiral-induced spin selectivity (CISS). However, challenges remain in stabilizing these materials under environmental stresses and precisely controlling chirality for scalable use. This review summarizes five main strategies to induce chirality in HOIPs, i.e., direct incorporation of chiral cations, surface modification with chiral ligands, ion doping, template-induced chirality, and chiral metasurfaces. Each approach offers distinct advantages for optimizing CPL efficiency and device stability, paving the way for next-generation applications in CPL detectors, information encryption, spintronic devices, etc.

14 SOLAR ENERGY↗
DOE OSTI2025

Self‐Assembled Monolayer Templating for Engineered Nanopinholes in Passivated Contact Solar Cells

We present a novel self-assembled monolayer (SAM)-based technique to make nanopinhole-enabled passivated contacts on silicon solar cells by tuning the SAM coverage area and etch selectivity. We deposit trimethyl-silyl Si(CH 3 ) 3 groups using hexamethyldisilazane (HMDS) as the precursor over passivating dielectric layers and their stacks (SiO 2 , SiN x , SiO 2 /SiN x ) and interrupt the HMDS attachment chemistry shortly before a full monolayer is formed on its surface. Subsequent etching in dilute HF produces pinholes through the dielectric layers due to the higher etch resistance of the SAM to HF etching. The pinhole areal density (10 4 –10 8 /cm 2 ) and size (10–1000 nm) can be tuned both by duration of HMDS attachment and HF etch time. Pinholes were characterized by atomic force microscopy, tetramethylammonium hydroxide (TMAH) selective etch, and Ag decoration by electroless plating. Polysilicon (poly-Si) passivated contacts enabled by pinholes were formed by subsequent deposition of doped amorphous silicon (a-Si:H) followed by thermal crystallization and dopant drive-in. At optimal areal pinhole density ≈10 7 /cm 2 , contacts exhibit both passivation and carrier transport via pinholes as evidenced by electron beam induced current, transmission line measurements, and carrier lifetime measurements. Solar cells based with these pinhole contacts show V oc = 723 mV and FF = 80.3%. The remaining SAM layer does not affect device performance.

14 SOLAR ENERGY↗
DOE OSTI2022

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. The plant reload optimization framework development project aims to build an artificial intelligence, i.e., Genetic Algorithm (GA), based reactor core designing tool taking into account reactor safety and fuel performance analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2012

Can Terrestrial Microbes Grow on Mars?

The theme for AbSciCon 2012 is "Exploring Life: Past and Present, Near and Far." The conference will address our current understanding of life - from processes at the molecular level to those which operate at planetary scales. Studying these aspects of life on Earth provides an essential platform from which to examine the potential for life on other worlds, both within our solar system and beyond. Mars exhibits a variety of extreme environments characterized by high UV and ionizing radiation flux, low pressure anoxic atmosphere, scarce or absent liquid water, extreme low temperatures, etc. The ability of terrestrial microorganisms to survive and adapt to the Mars environment has profound implications for astrobiology, planetary protection, and Mars life detection missions. At the NASA Ames Synthetic Biology Initiative, we believe that synthetic biology has the potential to revolutionize human space exploration. As such, the initiative is dedicated to applying the tools and techniques of synthetic biology to space exploration and astrobiology. Biological solutions will be invaluable for space exploration because they are not resource intensive, and they are versatile and self-renewing. An understanding of how to work with DNA in an unfavorable environment is paramount to utilizing biological tools on space missions. Furthermore, the ability to adjust life to the parameters of Mars is vital both to discovering what life on Mars might look like, and to using biological tools under such conditions. As a first step, we need an energy-efficient, low cost means of transporting, storing, and protecting genomic DNA, DNA parts, and whole microbial strains. Our goal is to develop and demonstrate viable and superior alternatives to standard DNA storage methods, which can be optimized to the conditions of space exploration, using synthetic biology as a tool. This includes protocols and kit designs for easy and repeatable DNA and strain recovery from protective storage conditions. We are constructing newly engineered genetic parts for different valuable host organisms, designed to increased long-term survival and functional retention. These methods should be applied for DNA and strain storage and transportation. In parallel, we seek inspiration from natural organisms that have developed means for survival in extreme environmental conditions. We are utilizing novel techniques for analysis of lipid biomarkers in the Antarctic Dry Valleys in order to identify resident microbes in the Antarctic soil and permafrost, as well as biomarker fossils of organisms that survived in the valleys in ages past. Through the identification of these life forms, we hope to understand and draw on new biological tools and strategies for synthetic biological applications on Mars.

Rothschild, Lynn↗
DOE OSTI2021

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal Wind Turbine Design for H2 Production

NREL and industrial partners GE and Nel seek to advance affordable green hydrogen production by optimizing wind turbine design specifically for hydrogen production and validating designs using NREL's ARIES facilities. This project will couple wind turbine, wind plant, solar plant, and electrolyzer models to predict hydrogen production from variable, renewable power sources. It will then use gradient-based optimization with exact-analytic gradients to optimize wind turbine rotor diameter, hub height, and power rating for optimal hydrogen production. Finally, it will validate the optimal turbine designs using the ARIES research platform, where it will compare a baseline turbine to the optimal design for a variety of scenarios and resources.

electrolyzer↗
DOE OSTI2020

Elucidating the differences in oxidation of high-performance α- and β- diisobutylene biofuels via Synchrotron photoionization mass spectrometry

Abstract Biofuels are a promising ecologically viable and renewable alternative to petroleum fuels, with the potential to reduce net greenhouse gas emissions. However, biomass sourced fuels are often produced as blends of hydrocarbons and their oxygenates. Such blending complicates the implementation of these fuels in combustion applications. Variations in a biofuel’s composition will dictate combustion properties such as auto ignition temperature, reaction delay time, and reaction pathways. A handful of novel drop-in replacement biofuels for conventional transportation fuels have recently been down selected from a list of over 10,000 potential candidates as part of the U.S. Department of Energy’s (DOE) Co-Optimization of Fuels and Engines (Co-Optima) initiative. Diisobutylene (DIB) is one such high-performing hydrocarbon which can readily be produced from the dehydration and dimerization of isobutanol, produced from the fermentation of biomass-derived sugars. The two most common isomers realized, from this process, are 2,4,4-trimethyl-1-pentene (α-DIB) and 2,4,4-trimethyl-2-pentene (β-DIB). Due to a difference in olefinic bond location, the α- and β- isomer exhibit dramatically different ignition temperatures at constant pressure and equivalence ratio. This may be attributed to different fragmentation pathways enabled by allylic versus vinylic carbons. For optimal implementation of these biofuel candidates, explicit identification of the intermediates formed during the combustion of each of the isomers is needed. To investigate the combustion pathways of these molecules, tunable vacuum ultraviolet (VUV) light (in the range 8.1–11.0 eV) available at the Lawrence Berkeley National Laboratory’s Advanced Light Source (ALS) has been used in conjunction with a jet stirred reactor (JSR) and time-of-flight mass spectrometry to probe intermediates formed. Relative intensity curves for intermediate mass fragments produced during this process were obtained. Several important unique intermediates were identified at the lowest observable combustion temperature with static pressure of 93,325 Pa and for 1.5 s residence time. As this relatively short residence time is just after ignition, this study is targeted at the fuels’ ignition events. Ignition characteristics for both isomers were found to be strongly dependent on the kinetics of C 4 and C 7 fragment production and decomposition, with the tert-butyl radical as a key intermediate species. However, the ignition of α-DIB exhibited larger concentrations of C 4 compounds over C 7 , while the reverse was true for β-DIB. These identified species will allow for enhanced engineering modeling of fuel blending and engine design.

09 BIOMASS FUELS↗
DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗
DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

AI-Based Protective Relays for Electric Grid Resiliency

The protection systems (circuit breakers, relays, reclosers, and fuses) of the electric grid are the primary component responding to resilience events, ranging from common storms to extreme events. The protective equipment must detect and operate very quickly, generally <0.25 seconds, to remove faults in the system before the system goes unstable or additional equipment is damaged. The burden on protection systems is increasing as the complexity of the grid increases; renewable energy resources, particularly inverter-based resources (IBR) and increasing electrification all contribute to a more complex grid landscape for protection devices. In addition, there are increasing threats from natural disasters, aging infrastructure, and manmade attacks that can cause faults and disturbances in the electric grid. The challenge for the application of AI into power system protection is that events are rare and unpredictable. In order to improve the resiliency of the electric grid, AI has to be able to learn from very little data. During an extreme disaster, it may not be important that the perfect, most optimal action is taken, but AI must be guaranteed to always respond by moving the grid toward a more stable state during unseen events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Probabilistic Cloud Optimized Day-Ahead Forecasting System Based on WRF-Solar (Final Report)

The most persistent challenge in both intraday and day-ahead solar forecasting is to get numerical weather prediction models to produce the right type of clouds with the right frequency at the right time and place. Another challenge is to understand and communicate the forecast uncertainty. The objective of this project was to develop an optimized ensemble-based solar irradiance forecasting system that will (1) demonstrably improve the current state-of-the-art solar forecasts from the deterministic Weather Research and Forecasting-Solar (WRF-Solar) model and (2) provide probabilistic forecasts for grid operations. This probabilistic solar forecasting system, referred to as the WRF-Solar Ensemble Prediction System (WRF-Solar EPS), aims to significantly enhance both the intraday and the day-ahead solar forecasting capability for grid operations. This technical report summarizes the work performed in the past 3 years through a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research as part of the U.S. Department of Energy's Solar Forecasting 2 program that aims to improve the accuracy of solar energy forecasts and enable increased deployment of solar energy on the electric grid.

14 SOLAR ENERGY↗