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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fundamental Research Aimed at Diverting Excess Reducing Power in Photosynthesis to Orthogonal Metabolic Pathways

Photosystems are incredible biological machines that use sunlight to drive the conversion of carbon dioxide to sugar. The amount of sunlight available for photosynthesis sometimes exceeds the amount of energy plants can use. This excess energy has to be safely dissipated through non-productive biological processes. The ultimate goal of this project is to understand whether we can utilize that otherwise unused excess energy. In our previous work, we showed that, in principle, it is possible to attach a catalyst to photosystem I and generate H2 using light. Our current strategy is to genetically fuse parts of the photosystem I complex with a recently discovered oxygen-tolerant [FeFe] hydrogenase. Our rationale is that such chimeric proteins may potentially result in the natural incorporation of the photosystem I-hydrogenase link using the inherent genetic machinery of the cell. In this project, we aim to verify that light-driven hydrogen production in this construction is possible. Throughout the project, we designed nanoconstructs that showcase the plausibility of this technology, at least in vitro. We take advantage of these constructs to investigate details of the coupling between photosystem I and a H2-producing enzyme called [FeFe] hydrogenase. This part of the project reveals details of the electron transfer between photosystem I and the attached hydrogenase, providing information that can lead to new strategies for improved biological photocatalysis. We also researched efficient and robust tethering of the [FeFe] hydrogenase to photosystem I in cyanobacteria. This work will highlight successful design strategies to guide the future development of photosynthetic biohybrids. Uncovering the principles governing the utilization of otherwise unusable energy significantly further our understanding of cyanobacterial photosynthesis. The work proposed establishes the feasibility of diverting excess energy under high light conditions to orthogonal enzymatic pathways and set design rules for efficient utilization of such a strategy for scientific and industrial applications in biosensing, renewable energy, and high-value chemicals production. The work addresses the DOE-BES Photosynthetic Systems program goal to develop a multidimensional understanding of photosystems that would provide specific metrics that instruct strategies for improving biological photosynthesis and for guiding the future development of bioreactors and biomimetic energy systems.

Photosynthetic systems, hydrogenase, cyanobacteria↗
DOE OSTI2025

Greater Toledo Community Foundation Sustainable Communities Educational Initiative (Final Technical Report)

The Greater Toledo Community Foundation’s (GTCF) Sustainable Communities Educational Initiative leveraged the two recently constructed Overland Solar Arrays to launch a diverse collection of educational activities designed to promote the intersection between workforce development, renewable energy and community activism to showcase emerging avenues for greater prosperity in communities that have experienced historic disinvestment. In addition to educational programming focused on employment in the clean energy and advanced manufacturing fields, the project also activated storytelling approaches to explain sustainable energy’s role in philanthropy, grantmaking and resident-led neighborhood enhancements.

14 SOLAR ENERGY↗
DOE OSTI2026

The Hydropower Game: An Interactive Learning Tool for the Future Hydropower Workforce

This report documents a Seedling project supported by the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO), conducted by Argonne National Laboratory (ANL) and the National Laboratory of the Rockies (NLR). The Seedling and Sapling Program provides small grants over short duration to enable early-stage research at national laboratories. This funding mechanism is intended to cultivate innovative ideas and expand research road maps in hydropower and marine energy technologies. Under this Seedling award, the project team developed an educational outreach tool, or “serious game”, built in Python and powered by the Pygame library, aimed at teaching fundamental hydropower concepts through an engaging, interactive learning experience. The game features multiple lessons covering several hydropower topics while allowing for expansion and customization in the instance of future funding availability: • Hydropower Plant Types: Players explore and compare the mechanics and applications of run-of-river, dam hydropower, and pumped-storage hydropower. • Flow Dynamics and Power Generation: Interactive tasks demonstrate how release rate and hydraulic head combine to determine power output. • Grid Operations and Load Following: Simulations illustrate how hydropower facilities respond to fluctuating electricity demand to balance the grid. • Market Integration: Levels scaffold understanding of how hydropower interfaces with the broader energy market, including operational and economic considerations. The tool was showcased at several science, technology, engineering, and mathematics (STEM) outreach events, where it was demoed to students, educators, and the general public. These events provided valuable opportunities to collect feedback on the gameplay, storyline, and educational objectives from a wide audience. The game was presented at STEMapalooza, Introduce a Girl to Engineering Day, STEMCON, and Clean Currents 2025. Insights gathered during these demonstrations informed refinements to the game’s user experience and strengthened its effectiveness as an educational tool for teaching hydropower concepts. This report outlines the game’s design philosophy, educational objectives, technical implementation, user experience insights, and potential for broader deployment within educational and workforce development contexts. It emphasizes how gamified learning can demystify complex hydropower science and inspire interest in water-power technologies. The project’s success demonstrates the value of Seedling funding in fostering creative, lowcost educational tools that support DOE’s mission to advance energy literacy and innovation. The report concludes with recommendations for expanding the tool by adding new levels, integrating assessment metrics, and exploring commercialization or deployment pathways through future Sapling funding. The official webpage of the Hydropower Game, which includes a link to the educational tool, can be accessed at www.anl.gov/hydropower/hydropower-game.

13 HYDRO ENERGY↗
DOE OSTI2024

Toward 2035: Forging a High-Ambition U.S. Climate Pathway

This analysis takes an integrated, innovative approach to develop plausible, high-ambition pathways through 2035 to inform the third U.S. NDC target setting. This approach combines: (1) an extensive consultation and engagement process with a broad set of federal and subnational stakeholders to build a robust U.S. policy platform; and (2) an open-source, field-leading integrated assessment model focused on the U.S. containing subnational details. The modeling tool assesses existing and possible new policies from both federal and subnational levels to assess the overall emissions reduction potential and the policy pathways to deliver the ambition needed for 2035 and leading on to 2050. In addition, we highlight how different actors can contribute to the needed transition in each sector and showcase successful implementation stories by various subnational actors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

InDEEP, DEEC-Tec, and Direct Generation Synergies for Ocean Wave Energy: Wave Energy Scotland's Direct Generation Programme Review

The presentation highlights: (i) how DEEC-Tec, advanced through the U.S. DOE's InDEEP Prize, holds the potential to redefine ocean wave energy conversion through modular, embedded energy conversion; (ii) a global scan that identified 158 participants and 35 promising concepts, showcasing strong international momentum; (iii) InDEEP's innovation-first approach and how it complements Wave Energy Scotland's structured Direct Generation research; and (iv) the ongoing and possible future collaborative pathways for co-funded trials, joint metric/standards developments, and maturation of these technologies toward and real-world deployment.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

14 SOLAR ENERGY↗
DOE OSTI2025

From Data to Discovery: AI's Transformative Role in Thin Film Research

The advancement of thin film technologies is pivotal for progress in numerous fields, including energy, electronics, and quantum computing. However, the traditional trial-and-error approach to materials discovery is inherently slow and inefficient. This presentation will showcase how artificial intelligence (AI) is transforming thin film research by enabling a data-driven paradigm shift. We will highlight our past successes in applying AI to understand radiation damage in thin film oxides, demonstrating how graph analytics can unravel complex material behavior. Additionally, we will provide insights into our current work at the National Renewable Energy Laboratory, where we are leading the charge in autonomous materials science. Backed by a $14M investment in our characterization facility, we are developing AI-guided workflows that seamlessly integrate experimentation and AI-guided decision-making. By harnessing the power of AI, we aim to accelerate the discovery and design of high-performance thin films, propelling innovation across a multitude of industries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers

This project developed the LAPIS compiler framework, built on the Multilevel Intermediate Representation (MLIR), to optimize sparse linear algebra operations and support performance portability across diverse architectures. The main innovation of LAPIS is the Kokkos dialect, which allows for lowering codes from a high productivity language to different architectures in an elegant way. The dialect also allows the conversion of lower-level MLIR code to C++ Kokkos code, facilitating the integration of scientific machine learning (SciML) models into applications. To extend LAPIS for distributed memory architectures, a new partition dialect was created to manage the distribution of sparse tensors and express communication patterns for sparse linear algebra operations. This dialect also supports the distributed execution of operators and includes algorithmic optimizations to minimize communication to improve performance. The project also demonstrates that MLIR can enable effective linear algebra-level optimizations, improving performance on different GPUs for both sparse and dense linear algebra kernels. Key applications of LAPIS include sparse linear algebra and graph kernels, TenSQL, a relational database management solution built on GraphBLAS, and the development of subgraph isomorphism and monomorphism kernels, showcasing performance portability. In summary, the LAPIS framework supports productivity, performance, portability, and distributed memory execution, while also enabling linear algebra-level optimizations that are challenging in traditional programming languages, with successful applications ranging from simple sparse linear algebra to complex graph kernels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NREL Tools Webinar Series: Session 1 - Overview

Webinar with NARC/Solsmart to showcase NREL tools related to energy with local governments interested in Solsmart designation. NREL staff will present on SAM, PV Watts, REopt, Procurement Analysis Tool, Slope, SolarAPP+, and SolarTRACE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Research on Integrated Energy Systems (ARIES) Cyber Range Overview and Threat-to-Consequence Demonstration

This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. The threat-to-consequence demonstration showcases NREL's capability to model, simulate, test, and evaluate cyberattacks targeting energy systems that coincide with natural hazards, as well as the ramifications for the energy grid as a whole.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Development Fiber Optic Distributed System for Direct Detection of Subsurface Gases Leakages

Carbon, natural gas, and hydrogen gas storage is an emerging solution to safeguard us against pollution, support goals of negative carbon emission, and protect sources of renewable energy. Properly constructed storage wells provide a virtually impervious barrier to any unintended subsurface transmission. The ability to ensure the long-term integrity of such wells is vital to the success of any storage operation and be successful in the public eyes. Therefore, robust monitoring of any gas migration into the subsurface is highly sought. A fiber-optic distributed chemical sensor (DCS) enables monitoring of long-term well integrity along its depth, ensuring the success of any storage operation and bolsters public acceptance of the safety of the reservoir via leak early detection. The same technique can be applied to gas monitoring in pipeline networks and nuclear stockpile monitoring applications. Fiber based Raman spectroscopy enables DCS, as optical fibers can be deployed in virtually any environment and relay spectroscopic information over long distances back to the user. Hollow core fibers (HCF) make excellent DCSs as the air core of the fiber allows gas from the environment to diffuse into the core, which interacts with the laser signal that is carried in the air core. This work builds upon the previous LDRD project, Fiber Optic System for Direct Detection of Carbon Dioxide Leakage in Carbon Storage Wells (21-FS-003), in which the feasibility of Raman spectroscopy detection of Carbon Dioxide (CO2) in HCF detection was demonstrated. We mitigated the risk of this DCS technology by establishing and completing five objectives. The first objective was to model and optically characterize HCF uptake of CO2, establishing the relationship between HCF length, gas diffusion time, detectable gas concentration, and measured Raman intensity. In objective two, we developed a fiber core drilling recipe to enable additional diffusion ports in the fiber core and established a method for maintaining fiber strength and integrity post drilling. Objective three characterized the drilled fibers against the undrilled fibers, establishing the differences in the gas mechanics and optical properties and provided parameters to iterate the drilling process. In objective four, a fusion splicing technique was developed to join the HCF to conventional single-mode fibers, localizing the gas detection point at the drilled HCF hole, emulating a DCS. Lastly, objective five was the testing of the sensor in Edgar Mines at Colorado School of Mines on a CO2 pipeline with a simulated leak, to showcase the ability to detect CO2 leaks. This capstone result showed CO2 leak detection in < 10 minutes, raising the technology readiness level of HCF segments as deployable DCS.

organic↗
DOE OSTI2025

Implementation of INCL nuclear model in GENIE Generator

The Liège Intranuclear Cascade (INCL) model is a nuclear-physics model that simulates hadron (baryon, anti-baryon and meson) reactions on nuclei, for incident energies ranging from a few tens of MeV to 10-20 GeV. The INCL model has been well validated by hadron scattering data. In my work, I implement an interface in GENIE to use the INCL nuclear model in the simulations of both the initial state of the target nucleus and the Final State Interaction in neutrino-nucleus interaction. It has a consistent treatment of nuclear models in both neutrino interaction and hadron rescattering. A full event record including neutrino vertex and each vertex of hadron rescattering has been accomplished. Several processes, e.g. cluster production, Delta transportation and de-excitation will also be included as benefits of the implementation of the INCL model in GENIE. I will show some initial simulation results showcasing the new GENIE features and discuss plans for making them available for use in experimental analyses.

Liu, Liang [Fermilab] (ORCID:000000026753925X)↗
DOE OSTI2024

New MeV-Scale Capabilities in Large Neutrino LArTPCs Using Ambient Radiogenic and Cosmogenic Activity in MicroBooNE

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions present in their data. We use data from MicroBooNE, an 85 tonnes LArTPC exposed to Fermilab neutrino beams from 2015 until 2021, to demonstrate new calorimetric and particle discrimination capabilities for isolated $\sim \mathrm{O}(1 \mathrm{MeV})$ energy depositions referred to as blips. We observe a concentration of low energy ( $<3$ MeV ) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV Tl-208 decay gamma rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of Tl-208 in the fiberglass composing these struts. Cosmogenicallyproduced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions.

Andrade Aldana, Diego Armando↗
DOE OSTI2026

Resilience Through Data-Driven, Intelligent Designed Control: A Formal Methods Approach

The PNNL and GTRI team developed a strategy to integrate temporal logic rule specification for detection of cyber-intrusion in the source code and control algorithms of CPS using advanced cyber-data. The GTRI team utilized its capabilities in rule synthesis and temporal logic specifications for software assurance and verification to detect and predict impact of cyber-intrusions and malware in the computational and control algorithms of cyber-physical systems. The team also developed a testing and verification approach that could be used to validate the suggested approach against a realistic use-case CPS showcasing improvements in system impact prediction performance. Temporal logic offers a compact expression of events in absolute and relative time and has a formalized translation to state machines. As such, temporal logic rules can feasibly be synthesized to any system as a rule engine, with the process being formally verified to be correct. The goal here is to utilize temporal logic rules to detect cyber-attacks and manipulations in the computational algorithms and provide real-time software assurance and verification guarantees.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Large-Scale Hardware Experiment Demonstration of Operating High Inverter-Based Resource Power Systems With Grid-Forming Inverters

This paper presents experimental hardware results from a microgrid system as the penetration level of grid-forming (GFM) inverters increases. The experiment aims to showcase the advantages of GFM inverters in enhancing system stability and to investigate the operational challenges in systems relying entirely on inverter-based resources (IBRs). Five test scenarios are devised to progressively increase GFM inverter penetration levels: 0% (S1), 19% (S2), 37% (S3), 68% (S4), and 100% (S5). To ensure consistency, identical loading conditions and dynamic events are applied across all scenarios. The conducted tests include load step changes, output variation of grid-following (GFL) inverters during islanded mode, transition operations (such as synchronization to the grid and islanding), and rateof- change-of-frequency (ROCOF) and voltage jump tests during grid-connected mode. Key findings from the tests are summarized as follows: (1) Scenario S1 proved most challenging for load steps, and GFL variations because of insufficient GFM capacity, leaving the diesel generator unable to handle transient ridethrough; (2) Scenario S5 was the most difficult for transition operations, because the lack of a diesel generator to maintain stiff bus voltage resulted in unexpected reactive power flows during synchronization, causing the point of common coupling (PCC) breaker to trip; (3) Scenarios S4 and S5 were particularly challenging for ROCOF tests because of minimal system inertia, leading to large transients that triggered breaker trips; and (4) Scenario S4 exhibited the strongest voltage recovery capability because of the presence of the diesel generator and a higher number of GFM inverters, both of which possess the highest capacity for reactive power injection.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Precursor Analysis Report: Doppelpaymer Ransomware Attack on Petroleos Mexicanos (PEMEX) 2019

The DoppelPaymer Ransomware Attack on Petroleos Mexicanos (PEMEX) 2019 Precursor Analysis Report leverages publicly available information about the PEMEX cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The 2019 DoppelPaymer ransomware attack on PEMEX, Mexico’s nationalized petroleum corporation, highlights a unique threat that ransomware and cybercriminal extortion poses to Operational Technology (OT) environments in critical infrastructure. The incident began with an employee downloading commodity malware that allowed adversaries to gain initial access to PEMEX’s enterprise environment. After conducting privilege escalation, tool ingress, and data exfiltration, the adversaries deployed DoppelPaymer ransomware throughout the PEMEX enterprise environment, resulting in the company having to take dozens of systems offline for at least several days. Although PEMEX stated that their operations were not affected, the data exfiltrated from PEMEX was made available for download on DoppelPaymer’s leak site, as well as on other illicit criminal forums. This stolen data included not only company information, but also sensitive OT-specific configuration data. This incident showcases how cybercriminal exfiltration and posting of sensitive OT architecture documentation can pose security concerns for the targeted organization for years due to the long lifespan of OT assets and architectures. Researchers and analysts identified 18 unique techniques utilized during the attack with a total of 190 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Fifteen of the identified techniques used during the DoppelPaymer ransomware attack were precursors to the triggering event. Analysis identified 163 observables associated with these precursor techniques, 34 of which were assessed to have an increased likelihood of being perceived in the 60 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

PRIMO – The Oil & Gas Well Plugging Optimizer

This work presents PRIMO’s main capabilities and introduces the PRIMO web application, an intuitive user interface that leverages our sophisticated mathematical optimization model to rigorously optimize P&A priorities and plugging campaign efficiency. The web app simplifies user interaction, provides a powerful data management framework, and supports a broad user base (e.g., state agencies, well owners/operators, and plugging companies) to use PRIMO for decision-making. Specifically, we provide a demonstration of how to input the information on candidate wells, plugging campaign budget, user-defined priority and efficiency criteria to PRIMO. A real-world case study that consists of 1411 oil and gas wells and impact and efficiency priorities (e.g., well age, well proximity to schools/hospitals, well accessibility, distance between wells in projects) is presented to showcase PRIMO’s core capabilities: (i) ranking a candidate well population based on priorities, (ii) recommending high-impact and high-efficiency P&A projects, and (iii) assigning impact and efficiency scores to projects allowing for rigorous quantitative comparison among them.

02 PETROLEUM↗