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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 721 records · Page 40

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DOE OSTI2026

Nanoscale Amorphization of Poly(Triarylamine) for Efficient and Stable Inverted Perovskite Photovoltaics

Perovskite solar modules require hole-selective layers that combine efficient charge extraction, interfacial uniformity and scalable processing. Poly(triarylamine) (PTAA) is widely used in high-performance inverted perovskite photovoltaics, but its nanoscale crystallization and aggregation on indium tin oxide can disrupt film continuity, increase interfacial recombination and limit module stability. Here we show that 4-fluorobenzylphosphonic acid (4FBPA) modifies the surface of indium tin oxide to induce nanoscale amorphization of PTAA, forming a uniform sub-10-nm hole-selective layer. The molecule binds to indium tin oxide through a dehydration reaction, tunes the work function and surface free energy of the photoanode, and improves energy-level alignment with PTAA. The resulting amorphous PTAA film shows enhanced conductivity and hole extraction, suppresses non-radiative recombination at the buried interface and promotes more uniform perovskite growth. Inverted perovskite solar cells reach a power conversion efficiency of 26.63%, while blade-coated modules achieve a certified quasi-steady-state efficiency of 23.01%. The modules retain 95.9% of their initial efficiency after 2,600 hours of maximum-power-point operation under 1-sun illumination at 65 +- 5 degrees C in nitrogen. These results identify nanoscale amorphization of polymeric hole-selective layers as a route to efficient and stable inverted perovskite photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2021

Arrays of Si vacancies in 4 H -SiC produced by focused Li ion beam implantation

Point defects in SiC are an attractive platform for quantum information and sensing applications because they provide relatively long spin coherence times, optical spin initialization, and spin-dependent fluorescence readout in a fabrication-friendly semiconductor. The ability to precisely place these defects at the optimal location in a host material with nano-scale accuracy is desirable for integration of these quantum systems with traditional electronic and photonic structures. Here, we demonstrate the precise spatial patterning of arrays of silicon vacancy (${V}_{Si}$) emitters in an epitaxial 4H -SiC (0001) layer through mask-less focused ion beam implantation of Li + . We characterize these arrays with high-resolution scanning confocal fluorescence microscopy on the Si-face, observing sharp emission lines primarily coming from the ${V1}^{{\prime}}$ zero-phonon line (ZPL). The implantation dose is varied over 3 orders of magnitude, leading to ${V}_{Si}$ densities from a few per implantation spot to thousands per spot, with a linear dependence between ZPL emission and implantation dose. Optically-detected magnetic resonance (ODMR) is also performed, confirming the presence of V2 ${V}_{Si}$. Our investigation reveals scalable and reproducible defect generation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Scaling Ultrahigh-Resolution E3SM Land Model for Leadership-Class Supercomputers

This paper presents advancements in scaling the ultrahigh-resolution E3SM Land Model (uELM) for deployment on leadership-class supercomputers, addressing the increased demand for km-scale Earth system modeling. By focusing on km-scale ELM simulations, we enhance predictive capabilities for climate interactions, facilitating improved responses to climate change impacts on energy systems, agriculture, and water resources. Our approach leverages innovative software architecture optimizations, sophisticated data handling techniques, and advanced parallel processing, achieving strong scalability on two leadership supercomputers (2400 nodes (105,600 cores) on Summit, and 1200 nodes (76,800 cores) on Frontier). Results from extensive scalability assessments on the Summit and Frontier also demonstrate outstanding I/O performance (close to 400 GB/s write throughput) and the model's ability to efficiently handle increasing computational demands. This study not only establishes uELM's capability for high-resolution simulations over vast geographical domains, but also sets a foundation for future Earth system modeling breakthroughs.

Wang, Dali [ORNL] (ORCID:0000000168065108)↗

DOE OSTI2020

Monitoring of dielectric permittivity in accelerated alkali-silica reaction concrete with microwave backscattering

Deterioration of concrete due to the alkali–silica reaction (ASR) involves a reaction between alkaline ions in the cement pore solution and non-crystalline silica found in many aggregates. The product of reaction is a porous hydrophilic ASR gel, which causes expansion and cracking of concrete structure. Currently, there is no reliable method for non-destructive evaluation of ASR. We have investigated the feasibility of using relative dielectric permittivity obtained from X-band microwave backscattering of concrete. Advantages of this method of concrete evaluation are that measurements are non-contact and one-sided, and the approach is scalable to arbitrary-size concrete structures. The exploratory study was performed using an unrestrained set of accelerated ASR concrete prism specimens developed according to the ASTM1293 standard. One set of specimens contained no entrained air, while another set was contained entrained air. The specimens were removed from the environmental chamber at different times during a year-long study to create a set of different ASR maturity specimens. Strain measurements were performed on all specimens. Strain data both air entrained and non-air entrained sets were shown to be in close agreement with Larive model of isothermal unrestrained ASR expansion with similar fitting parameters. Furthermore, relative dielectric permittivity of concrete specimens was correlated with strain data. Both sets show correlation between dielectric permittivity and strain, with stronger linear correlation observed for the air-entrained specimens. Development of such correlation would enable estimating expansion of actual concrete structures from microwave backscattering measurements, which could be used for risk stratification to guide and minimize coring.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Power Electronics Manufacturing Improvements for Heavy-Duty Fuel Cell Vehicles

The Marel Power Solutions project, funded by the U.S. Department of Energy under Award DE-SC0023801, focused on advancing manufacturing techniques for power electronics in heavy-duty fuel cell vehicles. The research aimed to enhance system efficiency, reduce costs, and support broader adoption of hydrogen fuel cell technology. Key areas of investigation included power topology, thermal modeling, system architecture, and accessibility through software tools. Key Accomplishments: 1. Power Topology: - Developed an interleaved boost converter with optimized phase count, leveraging Marel’s proprietary Power Stacks. - Achieved reduced parasitic inductance and resistance, enabling high efficiency in DC-DC converters. 2. Thermal Modeling: - Integrated innovative cooling systems into compact Silicon Carbide (SiC) modules. - Simulations demonstrated the ability to dissipate significant heat (up to 7.5 kW), ensuring device reliability under heavy loads. 3. System Architecture: - Utilized simulation tools to analyze the impact of various fuel cell and vehicle parameters on efficiency. - Highlighted the role of smaller, modular improvements, such as enhanced DC-DC converters, in achieving system-wide gains. 4. Accessibility: - Evaluated and implemented MATLAB/Simulink code generation tools for real-world hardware applications. - Demonstrated the potential for rapid prototyping of custom power systems with reduced development costs. Impact and Benefits: - Efficiency and Cost Reduction: Marel’s cooling technology enhances SiC die performance, reducing the number of dies required and overall system size. - Scalability and Flexibility: The innovations support tailored solutions for diverse applications, from mass transit to mining vehicles. - Sustainability: The research promotes the integration of electrification technologies, helping meet rising energy demands sustainably. Conclusion: The project’s outcomes advance the state of power electronics for hydrogen fuel cell vehicles, enabling more efficient, compact, and cost-effective solutions. These developments lay a foundation for future innovation, contributing to the broader adoption of clean energy technologies in transportation and other industries.

08 HYDROGEN↗

DOE OSTI2025

High-Energy and High-Power NMP-Free, Designer NMC 811 Cathodes with Ultra-Thick Architectures Processed by Electrophoretic Deposition

This project focused on electrode engineering and reducing the weight of inactive components in the battery cells. Additionally, the project focused on optimizing the ‘electrophoretic deposition’ technique developed by PPG industries for aqueous processing of electrodes. During this project team has screened several binders suitable for electrophoretic deposition of electrodes. Additionally, we developed a nice strategy to deposit a low cost and scalable oxide coating on the surface of cathode materials. ORNL team performed the research under this CRADA at DOE’s Battery Manufacturing Facility (BMF) at ORNL.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

High-Energy and High-Power NMP-Free, Designer NMC 811 Cathodes with Ultra-Thick Architectures Processed by Electrophoretic Deposition

25 ENERGY STORAGE↗

DOE OSTI2025

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

Multi-physics Preconditioning for Thermally Activated Batteries

Thermal batteries, also known as molten-salt batteries, are single-use reserve power systems activated by pyrotechnic heat generation, which transitions the solid electrolyte into a molten state. The simulation of these batteries relies on multiphysics modeling to evaluate performance and behavior under various conditions. This paper presents advancements in scalable preconditioning strategies for the Thermally Activated Battery Simulator (TABS) tool, enabling efficient solutions to the coupled electrochemical systems that dominate computational costs in thermal battery simulations. We propose a hierarchical block Gauss-Seidel preconditioner implemented through the Teko package in Trilinos, which effectively addresses the challenges posed by tightly coupled physics, including charge transport, porous flow, and species diffusion. The preconditioner leverages scalable subblock solvers, including smoothed aggregation algebraic multigrid (SA-AMG) methods and domain-decomposition techniques, to achieve robust convergence and parallel scalability. Strong and weak scaling studies demonstrate the solver’s ability to handle problem sizes up to 51.3 million degrees of freedom on 2048 processors, achieving near sub-second setup and solve times for the end-to-end electrochemical solve. These advancements significantly improve the computational efficiency and turnaround time of thermal battery simulations, paving the way for higher-resolution models and enabling the transition from 2D axisymmetric to full 3D simulations.

25 ENERGY STORAGE↗

DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Photocatalytic water splitting for large-scale solar-to-chemical energy conversion and storage

Sunlight-driven water splitting allows renewable hydrogen to be produced from abundant and environmentally benign water. Large-scale societal implementation of this green fuel production technology within energy generation systems is essential for the establishment of sustainable future societies. Among various technologies, photocatalytic water splitting using particulate semiconductors has attracted increasing attention as a method to produce large amounts of green fuels at low cost. The key to making this technology practical is the development of photocatalysts capable of splitting water with high solar-to-fuel energy conversion efficiency. Furthermore, advances that enable the deployment of water-splitting photocatalysts over large areas are necessary, as is the ability to recover hydrogen safely and efficiently from the produced oxyhydrogen gas. This lead article describes the key discoveries and recent research trends in photosynthesis using particulate semiconductors and photocatalyst sheets for overall water splitting, via one-step excitation and two-step excitation (Z-scheme reactions), as well as for direct conversion of carbon dioxide into renewable fuels using water as an electron donor. We describe the latest advances in solar water-splitting and carbon dioxide reduction systems and pathways to improve their future performance, together with challenges and solutions in their practical application and scalability, including the fixation of particulate photocatalysts, hydrogen recovery, safety design of reactor systems, and approaches to separately generate hydrogen and oxygen from water.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Machine Learning Predictions of Transition Probabilities in Atomic Spectra

Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS1992

NHT-1 I/O Benchmarks

The NHT-1 benchmarks am a set of three scalable I/0 benchmarks suitable for evaluating the I/0 subsystems of high performance distributed memory computer systems. The benchmarks test application I/0, maximum sustained disk I/0, and maximum sustained network I/0. Sample codes are available which implement the benchmarks.

Carter, Russell↗

NASA NTRS2001

Automation of Data Traffic Control on DSM Architecture

The design of distributed shared memory (DSM) computers liberates users from the duty to distribute data across processors and allows for the incremental development of parallel programs using, for example, OpenMP or Java threads. DSM architecture greatly simplifies the development of parallel programs having good performance on a few processors. However, to achieve a good program scalability on DSM computers requires that the user understand data flow in the application and use various techniques to avoid data traffic congestions. In this paper we discuss a number of such techniques, including data blocking, data placement, data transposition and page size control and evaluate their efficiency on the NAS (NASA Advanced Supercomputing) Parallel Benchmarks. We also present a tool which automates the detection of constructs causing data congestions in Fortran array oriented codes and advises the user on code transformations for improving data traffic in the application.

Frumkin, Michael↗

NASA NTRS2015

Architectures Toward Reusable Science Data Systems

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Data Processing↗

NASA NTRSDate not supplied

Experimental Results of a Rapidly Cycled Temperature Swing Adsorption Pump in a Terrestrial Environment

To improve the increasing CO 2 levels on Earth, technology for carbon capture and utilization must be efficient and cost effective. One major contributor of atmospheric CO 2 is post-combustion flue gas, which can contain up to 20% CO 2 . Scrubbing this CO 2 could reduce the environmental impact of this common industrial process. In off-Earth environments, capturing and utilizing CO 2 from space resources is useful for crewed spaceflight life support. Specifically, the Bosch process, which produces water and carbon powder by reacting CO 2 and hydrogen, is a possible in-situ resource utilization (ISRU) architecture for the Moon and Mars. The Rapid Cycle Temperature Swing Adsorption (RC-TSA) pump is an innovative technology designed to capture CO 2 efficiently. The RC-TSA leverages a stacked plate geometry to cycle multiple zeolite-coated beds. The thin layers allow for rapid heat transfer of the zeolite coating, which improves performance over previously tested pump designs. The design is favorable for scalability and manufacturability, which are forward work. Initial testing of the sorption pump using a simulated flue gas environment shows favorable breakthrough capacities and reduced cycle times. Results show promise for on- and off-Earth applications, such as a Martian surface and crewed spaceflight environments.

adsorption pump↗

DOE OSTI2026

Achieving strength-ductility synergy in hierarchical aluminum metal matrix composites via friction extrusion

We report the fabrication of aluminum metal matrix composites (Al-MMCs) with hierarchical architectures via friction extrusion (FE), a scalable, single-step, solid-phase processing technique. Precursor pucks containing 0–15 vol% Al₂O₃ particles were extruded into fully dense AA6061-based composite rods. The FE induced a tree-ring-like architecture of concentric particle-rich and particle-lean bands, yielding refined grains in particle-rich regions and coarser grains elsewhere. At the nanoscale, magnesium in AA6061 selectively reacted with Al₂O₃ particles to form virus-like nodes, improving particle–matrix bonding. This multi-scale design strategy, combining mesoscale architecture, microscale grain refinement, and nanoscale interface engineering overcome the conventional strength–ductility trade-off. Tensile testing showed substantial increases in yield and ultimate tensile strengths while retaining high ductility ( > 20%). Enhanced strain hardening, driven by the accumulation of geometrically necessary dislocations at interfaces, contributed to the performance. The hierarchical microstructure produced by FE demonstrates a promising pathway for scalable fabrication of lightweight MMCs for structural applications requiring a combined high strength and ductility.

Kalsar, Rajib [Pacific Northwest National Laborato↗

NASA NTRS1996

Toward Scalable Benchmarks for Mass Storage Systems

This paper presents guidelines for the design of a mass storage system benchmark suite, along with preliminary suggestions for programs to be included. The benchmarks will measure both peak and sustained performance of the system as well as predicting both short- and long-term behavior. These benchmarks should be both portable and scalable so they may be used on storage systems from tens of gigabytes to petabytes or more. By developing a standard set of benchmarks that reflect real user workload, we hope to encourage system designers and users to publish performance figures that can be compared with those of other systems. This will allow users to choose the system that best meets their needs and give designers a tool with which they can measure the performance effects of improvements to their systems.

Miller, Ethan L.↗