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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrophobic Carbon Nanosheets as Passivation Layer for Perovskite Solar Cells with High Efficiency and Stability (Final Technical Report)

Hybrid organic-inorganic metal halide perovskites have continually demonstrated competitive advantages for photovoltaic applications due to their high light absorption coefficient, tunable bandgaps, ambipolar charge mobility and high tolerance of defects. However, there are many challenges yet to overcome to bring this technology to the market. For instance, bridging the ‘scaling gap’ and transitioning PSCs from a lab scale to an industrial scale is a serious challenge. This project developed a high stability and inexpensive perovskite solar cell (PSC) by incorporating hydrophobic carbon nanosheets as counter electrodes (CEs). We demonstrated that the hydrogenated carbon nanosheets with tunable hydrophobic properties as CEs can improve life-time stability of PSCs in operating conditions (i.e. humid air) while achieving impressive power conversion efficiency (PCE).

14 SOLAR ENERGY↗
DOE OSTI2020

Nanometal-Interconnected Carbon Conductors (NICCS) for Advanced Electric Machines (RIT Final Technical Report)

Recent advancements in carbon nanotube (CNT) research have enabled lightweight, conductive wires as a transformative technology. Metal-carbon nanotube (CNT) hybrid conductors aim to combine the high conductivity of traditional metals with the low mass and temperature coefficient of resistance (TCR) of carbon nanotubes. The high conductivity of copper makes it a promising candidate to combine with CNTs in a hybrid structure, but there is limited physical and electrical interaction between copper and CNTs. The use of an interfacial layer offers one method of improving the interconnection of a Cu-CNT hybrid conductor. Over the course of this grant, a joule heating-driven chemical vapor deposition (CVD) technique was developed to deposit nanometal seeds throughout a porous, low-density (0.12 g/cm 3 , ~10 tex or mg/m) CNT roving template. Modification of the applied current to the CNT roving allows for the tuning of depositions towards either hot-spot site-specificity or overall uniformity. The effects of temperature, pressure, precursor mass, and the interval of applied current were investigated, demonstrating nanometal depositions ranging from less than 5 % w/w to over 85 % w/w. The versatility of CVD allows for a wide variety of metals to be deposited including copper, titanium, nickel, silver, tungsten, palladium, platinum, ruthenium, rhodium, and iridium. One preferred demonstration involved using platinum depositions to improve the electrical properties of metal-seeded CNTs across all mass loadings studied. Moreover, when the metal-seeded CNT wires were electroplated with copper, densified, and annealed under hydrogen/argon; the result was a Cu-CNT hybrid conductor with the highest conductivities reported to date. The good interconnection of the metal and CNT portions results in a stable conductor. An electrical conductivity of 16-20 MS/m was achieved for multiple Cu-CNT hybrid conductors at 150 °C, which exceeds the program goal for conductivity of greater than 15 MS/m for a 1 m long CNT wire. Overall, the research outcomes from the project showed improvements in CNT wire fabrication from roll-to-roll CVD grown carbon nanotube wires using nanometal interconnects to bridge CNT-to-CNT junctions, thus mitigating network contact resistances. Advanced nanometal interconnected carbon conductors (NICCs) were developed as a means to achieve novel light-weight wiring appropriate for applications that require the conductivity of metallic (i.e., Cu, Al, etc.) wires at elevated temperatures. High conductivity, low TCR electrical conductors such as the nanometal interconnected Cu-CNT hybrids have numerous future applications towards high efficiency motors, generators, and transformers. Specifically, the advanced wires have the ability to operate at lower resistance during conditions for standard electric motors at 150 °C, which would improve the electrical efficiency while lowering energy needs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Immersive Digital Twin Laboratory for Engineering Education (CRADA Final Report)

This project aimed to create an immersive digital twin laboratory that incorporates advanced tracking and visualization capabilities. In collaboration with Fort Lewis College, NREL designed a state-of-the-art physical visualization laboratory, developed a software platform to enable interaction with tracked physical objects in the laboratory, and provided proof-of-concept curricula that included manipulating these tracked objects. The project was initiated to address the growing need for innovative educational tools in engineering education. As renewable energy systems, particularly solar installations, become more complex, there is a pressing need to bridge the gap between theoretical knowledge and practical application. Traditional methods of teaching solar engineering concepts often fall short of providing students with a comprehensive, hands-on understanding. This immersive digital twin laboratory was conceived to fill that gap by creating a safe, non-energized setting where students can interact with augmented solar installation objects, gaining valuable insights into system performance, design, and maintenance. The project utilized extended reality (XR) technologies, including head-mounted displays (HMDs) and a whole-room optical motion tracking system, to connect physical objects with their digital twins in real time. The laboratory was equipped with MagicLeap 2 HMDs, supported by a Vicon Vero 2.2 Optical Tracking System, which provided precise 6-degrees-of-freedom (6-DOF) tracking. We developed a software platform to manage the interaction between the tracked physical objects and their virtual counterparts, enabling real-time data synchronization, object recognition, and virtual overlays. We designed the system to be flexible and extendable, allowing for future integration of additional objects and curriculum. This research advances the field of engineering education by demonstrating the potential of immersive digital twin environments. The laboratory provides a dynamic learning space where students can experiment, collaborate, and learn without the risks associated with live experimentation. The ability to simulate and manipulate solar installation objects under various conditions has broad implications for workforce development, particularly in renewable energy. The project also highlights the economic feasibility of using XR technologies in educational settings, offering a cost-effective solution for institutions looking to enhance their curriculum. By fostering a deeper understanding of solar energy systems, this work contributes to the broader goal of supporting the global energy transition and preparing the next generation of engineers and technicians.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

UBW (USLCI-Brightway2) [SWR-25-169]

Life cycle inventory (LCI) data are critical for robust life cycle assessment (LCA), yet many widely used datasets such as the U.S. Life Cycle Inventory (USLCI) are not natively compatible with advanced modeling frameworks like Brightway2. This work presents an automated pipeline to transform USLCI data into a fully functional Brightway2 project. The workflow performs systematic data cleaning, resolves duplicate process and exchange identifiers, and applies allocation to multi-output processes. Technosphere and biosphere flows are harmonized through unit conversions and a bridge mapping to the biosphere3 database, with comprehensive logging of missing flows and cutoff issues. The resulting Brightway2 database is validated using matrix diagnostics to ensure consistency of the technosphere, and is benchmarked via life cycle impact assessment (LCIA) methods such as ReCiPe and IPCC GWP. Outputs include reproducible CSV exports of corrected processes, elementary flows, characterization factors, and LCIA results, alongside backup utilities for project sharing. This pipeline lowers barriers for integrating USLCI data into open-source LCA workflows, enabling reproducible, validated LCA inventories within the Brightway 2 framework.

Ghosh, Tapajyoti [National Laboratory of the Rocki↗
DOE OSTI2020

Probing Charge Ordering in Fractional Mixed-Valence Charge Density Wave Systems with Oriented HERFD-XANES Spectroscopy

In this paper, we present oriented Pt L III edge high energy resolution fluorescence detected X-ray absorption near edge structure (HERFD-XANES) measurements on a series of structurally tunable quasi-one-dimensional mixed-valence platinum-halide linear chain materials, [Pt(en) 2 ][Pt(en) 2 X 2 ](ClO 4 ) 4 with X = Cl, Br, I, together with ab initio modeling of the spectral components and electronic structure. The materials exhibit a commensurate charge density wave (CDW) with an amplitude controlled by the bridging halide ion, giving rise to fractional mixed valence states. Ab initio FEFF9 modeling of the spectra and the associated angular momentum projected density of states (lDOS) are used to interpret the results, revealing systematic changes in the platinum orbital occupancy through the series of materials that reflect the fractional mixed valence states of the CDW. A significant contribution to a post-edge feature in the oriented spectra is identified as resulting from quantum interference between the final states of the X-ray absorption transition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AMReX and pyAMReX: Looking beyond the exascale computing project

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

Myers, Andrew↗
DOE OSTI2020

Experiences with a Flexible User Research Process to Build Data Change Tools

Scientific software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientific knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientific end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientific software development work. The use of user research techniques in scientific environments can be challenging due to the nascent, fluid problem spaces of scientific work, varying scope of projects and their user communities, and funding/economic constraints on projects.In this paper, we reflect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientific environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes reflections and the lessons we have learned from our experiences. We offer key takeaways for scientific software project teams to effectively and flexibly incorporate similar processes into their projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multiscale Aerosol Modeling Across Space and Composition (Final Report)

The objective of this project was to harness the capabilities of our unique particle-resolved aerosol model PartMC as an integrating tool between models and measurements across scales. This particle-resolved model explicitly resolves aerosol aging processes that must be approximated in large-scale models, making it ideal for quantifying structural uncertainty and rigorously deriving simplified parameterizations (“coarse graining”) to be used in large-scale models. It also directly connects with laboratory and field measurements at both particle and bulk scales. The significant accomplishments of this project are as follows: 1. Advancing the state of the art in particle-resolved modeling; 2. Connecting modeling with ASR/ARM measurements; 3. Bridging from the process level to aerosol impacts on the global scale; 4. Community engagement through leading and participating in ASR program activities and publishing a review paper on aerosol mixing state that synthesizes experimental and modeling perspectives.

58 GEOSCIENCES↗
DOE OSTI2020

Annual Scientific Focus Area Report for the Energy Exascale Earth System Model Project: Phase II

The project's long-term goal is to assert and maintain an international scientific leadership position in the development of Earth system models that address the grand challenge of actionable predictions of Earth system variability and change, with an emphasis on the most critical scientific questions facing the nation and DOE. The grand challenge will be addressed by overcoming three major limitations: Pushing the high-resolution frontier of Earth system modeling through a combination of strategic model development and computational advances targeting exascale computers and beyond. Bridging the gap in scales and processes by developing a new class of models that couple the natural, managed, and man-made systems across scales to address energy sector vulnerability to variability and change. The project will achieve its goal through four intersecting project elements: A series of prediction and simulation experiments addressing scientific questions and mission needs; A well-documented and tested, continuously advancing, evolving, and improving system of model codes that comprise the E3SM Earth system model; The ability to use effectively leading (and “bleeding”) edge computational facilities soon after their deployment at DOE national laboratories; and An infrastructure to support code development, hypothesis testing, simulation execution, and analysis of results. To achieve the Grand Challenge of actionable Earth system predictions to address the most critical scientific questions facing our nation and DOE, the E3SM project defined its strategy in a fifteen-year roadmap with four intersecting project elements: A series of prediction and simulation experiments addressing scientific questions and mission needs; A well-documented and tested, continuously advancing, evolving, and improving system of model codes that comprise the E3SM Earth system model ; The ability to use effectively leading (and “bleeding”) edge computational facilities soon after their deployment at DOE national laboratories; and An infrastructure to support code development, hypothesis testing, simulation execution, and analysis of results. Figure 1 depicts the E3SM Project Roadmap, showing the relationships among the first three major project elements: the simulations, the modeling system to perform those simulations, and the machines on which they will be executed. Unlike the other three elements that have distinct but overlapping phases, the fourth element, the infrastructure, will evolve continuously based on the requirements imposed by project needs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

In-Situ Blade Strain Measurements and Fatigue Analysis of a Cross-Flow Turbine Operating in a Tidal Flow

Cross-flow turbines (CFTs) are inherently unsteady devices with regards to operating principle and loading. By improving our understanding of the dynamic loading on these turbines, we hope to better inform CFT design, improve survivability, and reduce overall costs. The University of New Hampshire (UNH) and the National Renewable Energy Laboratory (NREL) collaborated on a project to instrument and test a four-bladed New Energy Corp. vertical axis cross-flow turbine in a real tidal flow. One blade from the 3.2 m diameter x 1.7 m height turbine was instrumented with eight full-bridge strain gauges along the span of the blade. The turbine was then deployed at the UNH-Atlantic Marine Energy Center (AMEC) Tidal Energy Test Site in Portsmouth, NH. Time-synchronized measurements of blade strain, inflow, thrust, rotational speed, and electrical output were obtained to characterize blade loading under various conditions. The blade strain was examined to assess the dynamic loading and conduct a fatigue analysis on the device.

blade strain↗
DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗
DOE OSTI2026

Development of Biofuel and Bioenergy Processes for Photosynthetic CO 2 Assimilation (CRADA Final Report)

In order to fully exploit algae carbon assimilation for fuels, a preqrequisite is high photosynthetic efficiency and carbon conversion to fuel precursors. In the context of this collaborative project between NLR and EMRE we aim to address aspects of both the biological constraints around photosynthesis-driven carbon assimilation flux as well as elements that bridge the gap between laboratory cultivation and meaningful outdoor deployment, building on the unique capabilities of NLR and EMRE’s algae bioengineering teams. The goal of this work is to improve our understanding of key cellular biochemistry, mass cultivation, and biomass conversion constraints that presently limit the potential bio-oil yield. The proposed first phase of this work will focus on biomass growth improvements by testing hypotheses for alleviating identified metabolic and photophysiological bottlenecks. The results of this work could inform decisions by stakeholders (e.g., industry, community, investors) on the design and deployment of a sustainable biofuel supply chain from algae.

09 BIOMASS FUELS↗
DOE OSTI2026

Modeling The Nucleosynthetic Imprint of Stellar Merger Phenomena - Final Technical Report

This DOE CAREER project developed a comprehensive, multi-physics framework for modeling stellar mergers and their observable consequences. The work successfully integrated analytical models, three-dimensional hydrodynamic simulations, stellar evolution calculations, nucleosynthesis, and radiation transport into a unified pipeline. Key scientific advances include demonstrating stellar mergers as a unifying explanation for systems such as Betelgeuse and R Coronae Borealis stars, quantifying merger-driven chemical signatures, and establishing the connection between merger physics and circumstellar environments. A major outcome of the project is the development of SuperLite, an open-source Monte Carlo radiation transport code that enables the generation of synthetic spectra for astrophysical transients. This work bridges dynamical, thermal, and radiative timescales and provides a powerful, DOE-relevant computational framework for interpreting observations of supernovae and related transient phenomena, while also contributing to workforce development through the training of postdoctoral researchers and graduate students.

Chatzopoulos, Emmanouil [Louisiana State Universit↗
DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Shaktoolik Battery System Construction Project

Alaska Village Electric Cooperative, Inc. (AVEC) and Shaktoolik IRA Tribal Council (Tribe) have created a Tribal Energy Development Organization (TEDO), known as AVEC/Shaktoolik Renewable Energy Joint Venture TEDO (ASRA), for building a community-scale energy storage “grid bridging system” (GBS) for the existing power plant and wind turbines on Tribal Lands in Shaktoolik, Alaska.

25 ENERGY STORAGE↗
DOE OSTI2025

G4VG

G4VG reads the in-memory Geant4 geometry (the volume hierarchy and physical shape descriptions defined by a user application) and produces an equivalent VecGeom representation. This provides a direct bridge between Geant4 detector descriptions and VecGeom-based applications, eliminating the need for manual export pipelines or specialized GDML workflows. The code is adapted from the Celeritas project and developed as an independent utility for broader reuse.

Johnson, SethR [Oak Ridge National Laboratory] (00↗
DOE OSTI2023

Holographic measurement in CFT thermofield doubles

We extend the results of arXiv:2209.12903 by studying local projective measurements performed on subregions of two copies of a CFT 2 in the thermofield double state and investigating their consequences on the bulk double-sided black hole holographic dual. We focus on CFTs defined on an infinite line and consider measurements of both finite and semi-infinite subregions. In the former case, the connectivity of the bulk spacetime is preserved after the measurement. In the latter case, the measurement of two semi-infinite intervals in one CFT or of one semi-infinite interval in each CFT can destroy the Einstein-Rosen bridge and disconnect the bulk dual spacetime. In particular, we find that a transition between a connected and disconnected phase occurs depending on the relative size of the measured and unmeasured subregions and on the specific Cardy state the measured subregions are projected on. We identify this phase transition as an entangled/disentangled phase transition of the dual CFT system by computing the post-measurement holographic entanglement entropy between the two CFTs. We also find that bulk information encoded in one CFT in the absence of measurement can sometimes be reconstructed from the other CFT when a measurement is performed, or can be erased by the measurement. Finally, we show that a purely CFT calculation of the Renyi entropy using the replica trick yields results compatible with those obtained in our bulk analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗