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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Development of Industrial Prototype Spectropolarimeter for Medical Use Based on Liquid Crystal Polarization Diffraction Grating (Final Report)

This project will develop and design a prototype for an advanced spectro-polarimeter, which measures the polarization of light at various wavelengths. This spectro-polarimeter will combine the functionality of both spectrometer and polarimeter in one compact, lightweight and inexpensive device. Unlike devices on market today, this device allows for simultaneous, real-time measurements of fast biological processes. This advance in spectro-polarimeters can be applied in the chemical, medical and biological fields to research new drugs, control dynamics of drugs in blood, and improve medical diagnosis by enabling the study of tissue structure, cellular and sub-cellular structures of proteins and DNA. This project will also help develop other applications for such devices as imaging, identification of targets embedded in dense scattering media, and biochemical sensing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Neural network accelerator for quantum control

Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outside of the simulation. Existing hardware solutions structured as lookup tables are imprecise and costly. By designing a machine learning model to approximate the results of traditional tools, a more efficient method can be produced. Such a model can then be synthesized into a hardware accelerator for use in quantum systems. In this study, we demonstrate a machine learning algorithm for predicting optimal pulse parameters. This algorithm is lightweight enough to fit on a low-resource FPGA and perform inference with a latency of 175 ns and pipeline interval of 5 ns with > 0.99 gate fidelity. In the long term, such an accelerator could be used near quantum computing hardware where traditional computers cannot operate, enabling quantum control at a reasonable cost at low latencies without incurring large data bandwidths outside of the cryogenic environment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Cost estimates of production scale semitransparent organic photovoltaic modules for building integrated photovoltaics

Building integrated photovoltaics (BIPVs) are attached to commercial and residential structures to enable solar energy harvesting. While conventional Si photovoltaics (PVs) are dominant in the current market, second and third generation thin film solar cells based on amorphous Si, CdTe, CIGS, perovskites or organic photovoltaics (OPVs) are often considered as an alternative for BIPV applications since they may offer reduced costs compared to Si PVs. Indeed, recent advances in performance suggest that lightweight, flexible and visibly transparent OPVs can potentially be integrated into windows or other applications to which Si PVs are less well suited. Here, we estimate the cost of high efficiency, semitransparent OPVs (ST-OPVs) based on solution processing in a roll-to-roll (R2R) manufacturing line. Assuming modules with 10% power conversion efficiency (PCE), a 70% geometric fill factor (GFF), and 95% inverter efficiency, we anticipate a %1.6 per Wp module manufacturing cost that includes the cost of the microinverter to condition the OPV dc output to be compatible with the ac line voltage of the building. The materials and inverter cost comprise ~90% of the total module cost. Hence, with simplified material synthesis and a lower inverter cost, including marginally improved PCE and GFF, we expect the cost can be as low as $0.47 per Wp. Here, while the module costs ~60% of the average (uninstalled) double-pane window, we expect the payback period can be as short as 2 to 6 years, suggesting that OPVs can be an economic and attractive candidate for BIPV applications.

14 SOLAR ENERGY↗
DOE OSTI2020

Carbon Fiber and Syntactic Foam Hybrid Materials via Core–Shell Material Extrusion Additive Manufacturing

Abstract Biological materials often employ hybrid architectures, such as the core–shell (C–S) motif present in porcupine quills and plant stems, to achieve unique specific properties and performance. Drawing inspiration from these natural materials, a new method to fabricate lightweight and stiff C–S architected filaments is reported. Specifically, a C–S printhead conducive to printing highly loaded fiber‐filled inks, as well as a new low‐density syntactic foam ink, are utilized to 3D‐print C–S architectures consisting of a syntactic epoxy foam core surrounded by a stiff carbon fiber‐reinforced epoxy composite shell. Effective printing of test specimens and structures with controlled geometry, composition, and architecture is demonstrated. The new foam ink exhibits density as low as 0.68 g cm −3 and C–S structures exhibit up to 25% higher specific flexural stiffness ( E 1/3 /ρ) than either constituent alone. Finally, a new mechanical model is presented to predict this performance improvement while accounting for potential eccentricity of the core.

Pack, Robert C.↗
DOE OSTI2013

Field Testing of Nano-PCM-Enhanced Building Envelope Components in a Warm-Humid Climate

The U.S. Department of Energy Building Technologies Program s goal of developing high-performance, energy-efficient buildings will require more cost-effective, durable, energy-efficient building envelopes. Forty-eight percent of the residential enduse energy consumption is spent on space heating and air conditioning. Reducing envelope-generated heating and cooling loads through application of phase-change material (PCM) enhanced envelope components can facilitate maximizing the energy efficiency of buildings. Field testing of prototype envelope components is an important step in estimating their energy benefits. An innovative PCM (nano-PCM) was developed with PCM encapsulated with expanded graphite (interconnected) nanosheets, which is highly conducive for enhanced thermal storage and energy distribution, and is shape-stable for convenient incorporation into lightweight building components. During 2012, two test walls with cellulose cavity insulation and prototype PCM-enhanced interior wallboards were installed in a natural exposure test (NET) facility in Charleston, SC. The first test wall was divided into four sections separated by wood studs and thin layers of foam insulation. Two sections contained nano-PCMenhanced wallboards: one was a three-layer structure in which nano-PCM was sandwiched between two gypsum boards, and the other one had PCM dispersed homogeneously throughout graphite nanosheet-enhanced gypsum board. The second test wall also contained two sections with interior PCM wallboards; one contained nano-PCM dispersed homogeneously in gypsum and the other was gypsum board containing a commercial microencapsulated PCM (MEPCM) for comparison. Each test wall contained a section covered with gypsum board on the interior side that served as control or a baseline for evaluation of the PCM wallboards. The walls were instrumented with arrays of thermocouples and heat flux transducers. This paper presents the measured performance and analysis to evaluate the energy-saving potential of the nano-PCM-enhanced building components.

Biswas, Kaushik↗
DOE OSTI2013

Field Testing of Nano-PCM Enhanced Building Envelope Components

The U.S. Department of Energy's (DOE) Building Technologies Program s goal of developing high-performance, energy efficient buildings will require more cost-effective, durable, energy efficient building envelopes. Forty-eight percent of the residential end-use energy consumption is spent on space heating and air conditioning. Reducing envelope-generated heating and cooling loads through application of phase change material (PCM)-enhanced envelope components can facilitate maximizing the energy efficiency of buildings. Field-testing of prototype envelope components is an important step in estimating their energy benefits. An innovative phase change material (nano-PCM) was developed with PCM encapsulated with expanded graphite (interconnected) nanosheets, which is highly conducive for enhanced thermal storage and energy distribution, and is shape-stable for convenient incorporation into lightweight building components. During 2012, two test walls with cellulose cavity insulation and prototype PCM-enhanced interior wallboards were installed in a natural exposure test (NET) facility at Charleston, SC. The first test wall was divided into four sections, which were separated by wood studs and thin layers of foam insulation. Two sections contained nano-PCM-enhanced wallboards: one was a three-layer structure, in which nano-PCM was sandwiched between two gypsum boards, and the other one had PCM dispersed homogeneously throughout graphite nanosheets-enhanced gypsum board. The second test wall also contained two sections with interior PCM wallboards; one contained nano-PCM dispersed homogeneously in gypsum and the other was gypsum board containing a commercial microencapsulated PCM (MEPCM) for comparison. Each test wall contained a section covered with gypsum board on the interior side, which served as control or a baseline for evaluation of the PCM wallboards. The walls were instrumented with arrays of thermocouples and heat flux transducers. Further, numerical modeling of the walls containing the nano-PCM wallboards were performed to determine their actual impact on wall-generated heating and cooling loads. The models were first validated using field data, and then used to perform annual simulations using Typical Meteorological Year (TMY) weather data. This article presents the measured performance and numerical analysis to evaluate the energy-saving potential of the nano-PCM-enhanced building components.

nano-PCM↗
DOE OSTI2018

Mechanically Robust, Ultraelastic Hierarchical Foam with Tunable Properties via 3D Printing

We present a mechanically robust, ultraelastic foam with controlled multiscale architectures and tunable mechanical/conductive performance is fabricated via 3D printing. Hierarchical porosity, including both macro- and microscaled pores, are produced by the combination of direct ink writing (DIW), acid etching, and phase inversion. The thixotropic inks in DIW are formulated by a simple one-pot process to disperse duo nanoparticles (nanoclay and silica nanoparticles) in a polyurethane suspension. The resulting lightweight foam exhibits tailorable mechanical strength, unprecedented elasticity (standing over 1000 compression cycles), and remarkable robustness (rapidly and fully recover after a load more than 20 000 times of its own weight). Surface coating of carbon nanotubes yields a conductive elastic foam that can be used as piezoresistivity sensor with high sensitivity. For the first time, this strategy achieves 3D printing of elastic foam with controlled multilevel 3D structures and mechanical/conductive properties. In conclusion, the facile ink preparation method can be utilized to fabricate foams of various materials with desirable performance via 3D printing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)

Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.

Gedara, Buddhika S. A. [Physical and Computational↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2021

Interactions of Aqueous Surface Treatments with Magnesium Alloy

Magnesium, the lightest structural metal, has recently been identified as an important substrate for weight reduction in automotive, aerospace, personal electronics, and many other industries. Magnesium’s unique chemistry, however, makes it challenging to protect from corrosion and incorporate with other materials. Cleaners are the first step in any pretreatment process, and understanding their role in surface preparation for corrosion prevention is crucial to making magnesium substrate a viable solution for lightweighting. In this work, we explore the relationship between cleaner composition, pH, etch rate, and panel morphology post-cleaning to understand how various cleaners interact with magnesium alloy. Our findings broaden the pool of possible bath chemistries that provide safe and effective surface cleaning of magnesium substrate.

Harris, Rachel↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Infrastructure for Multiphysics Software Integration in High Performance Computing-Aided Science and Engineering

The project described in this report constructed and exercised an innovative multiphysics coupling toolkit called the Illinois Rocstar MultiPhysics Application Coupling Toolkit (IMPACT). IMPACT is an open source, flexible, natively parallel infrastructure for coupling multiple uniphysics simulation codes into multiphysics computational systems. IMPACT works with codes written in several high-performance-computing (HPC) programming languages, and is designed from the beginning for HPC multiphysics code development. It is designed to be minimally invasive to the individual physics codes being integrated, and has few requirements on those physics codes for integration. The goal of IMPACT is to provide the support needed to enable coupling existing tools together in unique and innovative ways to produce powerful new multiphysics technologies without extensive modification and rewrite of the physics packages being integrated. There are three major outcomes from this project: 1) construction, testing, application, and open-source release of the IMPACT infrastructure, 2) production of example open-source multiphysics tools using IMPACT, and 3) identification and engagement of interested organizations in the tools and applications resulting from the project. This last outcome represents the incipient development of a user community and application echosystem being built using IMPACT. Multiphysics coupling standardization can only come from organizations working together to define needs and processes that span the space of necessary multiphysics outcomes, which Illinois Rocstar plans to continue driving toward. The IMPACT system, including source code, documentation, and test problems are all now available through the public gitHUB.org system to anyone interested in multiphysics code coupling. Many of the basic documents explaining use and architecture of IMPACT are also attached as appendices to this document. Online HTML documentation is available through the gitHUB site. There are over 100 unit tests provided that run through the Illinois Rocstar Application Development (IRAD) lightweight testing infrastructure that is also supplied along with IMPACT. The package as a whole provides an excellent base for developing high-quality multiphysics applications using modern software development practices. To facilitate understanding how to utilize IMPACT effectively, two multiphysics systems have been developed and are available open-source through gitHUB. The simpler of the two systems, named ElmerFoamFSI in the repository, is a multiphysics, fluid-structure-interaction (FSI) coupling of the solid mechanics package Elmer with a fluid dynamics module from OpenFOAM. This coupling illustrates how to combine software packages that are unrelated by either author or architecture and combine them into a robust, parallel multiphysics system. A more complex multiphysics tool is the Illinois Rocstar Rocstar Multiphysics code that was rebuilt during the project around IMPACT. Rocstar Multiphysics was already an HPC multiphysics tool, but now that it has been rearchitected around IMPACT, it can be readily expanded to capture new and different physics in the future. In fact, during this project, the Elmer and OpenFOAM tools were also coupled into Rocstar Multiphysics and demonstrated. The full Rocstar Multiphysics codebase is also available on gitHUB, and licensed for any organization to use as they wish. Finally, the new IMPACT product is already being used in several multiphysics code coupling projects for the Air Force, NASA and the Missile Defense Agency, and initial work on expansion of the IMPACT-enabled Rocstar Multiphysics has begun in support of a commercial company. These initiatives promise to expand the interest and reach of IMPACT and Rocstar Multiphysics, ultimately leading to the envisioned standardization and consortium of users that was one of the goals of this project.

42 ENGINEERING↗
DOE OSTI2017

Optimized Generator Designs for the DTU 10-MW Offshore Wind Turbine using GeneratorSE

Compared to land-based applications, offshore wind imposes challenges for the development of next generation wind turbine generator technology. Direct-drive generators are believed to offer high availability, efficiency, and reduced operation and maintenance requirements; however, previous research suggests difficulties in scaling to several megawatts or more in size. The resulting designs are excessively large and/or massive, which are major impediments to transportation logistics, especially for offshore applications. At the same time, geared wind turbines continue to sustain offshore market growth through relatively cheaper and lightweight generators. However, reliability issues associated with mechanical components in a geared system create significant operation and maintenance costs, and these costs make up a large portion of overall system costs offshore. Thus, direct-drive turbines are likely to outnumber their gear-driven counterparts for this market, and there is a need to review the costs or opportunities of building machines with different types of generators and examining their competitiveness at the sizes necessary for the next generation of offshore wind turbines. In this paper, we use GeneratorSE, the National Renewable Energy Laboratory's newly developed systems engineering generator sizing tool to estimate mass, efficiency, and the costs of different generator technologies satisfying the electromagnetic, structural, and basic thermal design requirements for application in a very large-scale offshore wind turbine such as the Technical University of Denmark's (DTU) 10-MW reference wind turbine. For the DTU reference wind turbine, we use the previously mentioned criteria to optimize a direct-drive, radial flux, permanent-magnet synchronous generator; a direct-drive electrically excited synchronous generator; a medium-speed permanent-magnet generator; and a high-speed, doubly-fed induction generator. Preliminary analysis of leveled costs of energy indicate that for large turbines, the cost of permanent magnets and reliability issues associated with brushes in electrically excited machines are the biggest deterrents for building direct-drive systems. The advantage of medium-speed permanent-magnet machines over doubly-fed induction generators is evident, yet, variability in magnet prices and solutions to address reliability issues associated with gearing and brushes can change this outlook. This suggests the need to potentially pursue fundamentally new innovations in generator designs that help avoid high capital costs but still have significant reliability related to performance.

17 WIND ENERGY↗
DOE OSTI2016

Membrane-type resonator as an effective miniaturized tuned vibration mass damper

Damping of low frequency vibration by lightweight and compact devices has been a serious challenge in various areas of engineering science. Here we report the experimental realization of a type of miniature low frequency vibration dampers based on decorated membrane resonators. At frequency around 150 Hz, two dampers, each with outer dimensions of 28 mm in diameter and 5 mm in height, and a total mass of 1.78 g which is less than 0.6% of the host structure (a nearly free-standing aluminum beam), can reduce its vibrational amplitude by a factor of 1400, or limit its maximum resonance quality factor to 18. Furthermore, the conceptual design of the dampers lays the foundation and demonstrates the potential of further miniaturization of low frequency dampers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

High-K dielectric sulfur-selenium alloys

Upcoming advancements in flexible technology require mechanically compliant dielectric materials. Current dielectrics have either high dielectric constant, K (e.g., metal oxides) or good flexibility (e.g., polymers). Here, we achieve a golden mean of these properties and obtain a lightweight, viscoelastic, high-K dielectric material by combining two nonpolar, brittle constituents, namely, sulfur (S) and selenium (Se). This S-Se alloy retains polymer-like mechanical flexibility along with a dielectric strength (40 kV/mm) and a high dielectric constant (K = 74 at 1 MHz) similar to those of established metal oxides. Our theoretical model suggests that the principal reason is the strong dipole moment generated due to the unique structural orientation between S and Se atoms. The S-Se alloys can bridge the chasm between mechanically soft and high-K dielectric materials toward several flexible device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

2023 Annual Merit Review Vehicle Technologies Office Results Report

The 2023 U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy's (EERE) Vehicle Technologies Office (VTO) Annual Merit Review (AMR) was held June 12-15, 2023, as a hybrid event with in-person and virtual attendance. The review encompassed work done by VTO: 257 individual activities were reviewed by 266 reviewers. Exactly 991 individual review responses were received for the VTO technical reviews. The objective of the meeting was to review the accomplishments and plans for VTO over the previous 12 months, and provide an opportunity for industry, government, and academia to give inputs to DOE with a structured and formal methodology. The meeting also provided attendees with a virtual forum for interaction and technology information transfer.

33 ADVANCED PROPULSION SYSTEMS↗