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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

Effects of Galactic Irradiation on Thermal and Electronic Transport in Tungsten

The impact of irradiation on the thermal and electronic properties of materials is a persistent puzzle, particularly defect formation at the atomic and nanoscales. This work examines the nanoscale effects of low-energy irradiation on tungsten (W), focusing on defect-induced modifications to thermal and electronic transport. Using the site-projected thermal conductivity (SPTC) method [A. Gautam et al. PSS-RRL, 2400306, 2024], bulk and twin-grain boundary W with vacancy defects based on the Norgett–Robinson–Torrens displacements per atom model is analyzed. SPTC provides a detailed prediction of postcascade spatial thermal conductivity distribution. Electronic conductivity activity is estimated using the “N 2 method” [K. Nepal et al. Carbon, 119711, 2025] to explore the consequences of vacancies and grain boundaries, highlighting the defect-dependent nature of charge transport behavior. These findings offer high-resolution insights into irradiation-driven transport phenomena, with implications for space-exposed materials and nanoscale thermal/electronic management.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Advancing the Performance of Lithium-Rich Oxides in Concert with Inherent Complexities: Domain-Selective Substitutions

Historically, modifications to Li- and Mn-rich (LMR) cathodes have been studied in relation to their efficacy in solving challenges such as oxygen loss and voltage fade, which are inherent to the activation process of these electrodes. However, even in the presence of these phenomena, well-optimized LMR cathodes show considerable promise as earth-abundant options, particularly if other barriers to implementation can be overcome or mitigated. As the complex mechanisms of LMR electrodes are known to stem from the local, chemical inhomogeneities that define the nanocomposite domain nature of these oxides, strategies aimed at manipulating the performance of activated electrodes, irrespective of voltage fade, through domain-selective modifications, could prove instructive. In this work, we use a novel synthesis process aimed at influencing the site occupancy of substituted Sn 4+ , as an example 4+ cation, into a Co-free Li 1.13 Mn 0.57(1–x) Sn 0.57x Ni 0.3 O 2 LMR oxide. We show that Sn 4+ can be selectively substituted into Li-rich environments. The consequences are revealed to be both chemical and morphological, and the domain-selective doping strategy provides a knob for directed control of the low state-of-charge impedance behavior. In conclusion, these results reveal new clues and insights with respect to further advancing the practical relevance of LMR cathode particles and electrodes.

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

Preprocessing Affords 3D Crystalline Poly(3-hexylthiophene) Structure

The aggregation and crystallization of poly(3-hexylthiophene- 2,5-diyl) (P3HT), a representative active layer material used for organic field-effect transistor (OFET) applications, are influenced by the solution pretreatment and deposition process. This study explores vibration-assisted convective deposition for the fabrication of OFETs in comparison to spin coating, blade coating, and convective deposition without vibration. The ultraviolet–visible spectroscopic analysis demonstrates that convective deposition, especially assisted with vibration, leads to a greater degree of intrachain interactions, longer conjugation length, and enhanced polymer backbone planarization. When the P3HT solution is preprocessed via sonication and aging, the P3HT films exhibit J-like aggregation, and (h11) peaks can be observed through grazing-incidence wide-angle X-ray scattering, suggesting an ordered 3D crystalline structure. OFETs based on such films exhibit high mobilities (up to 0.14 cm 2 V –1 s –1 ). The results point to the sensitivity of P3HT charge transport behavior to the intramolecular interactions and backbone planarity and further deepen our understanding of the relationship between processing, aggregates, molecular ordering, and resultant device properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nanoscale structural correlations in a model cuprate superconductor

Understanding the extent and role of inhomogeneity is a pivotal challenge in the physics of cuprate superconductors. While it is known that structural and electronic inhomogeneity is prevalent in the cuprates, it has proven difficult to disentangle compound-specific features from universally relevant effects. Here, in this study, we combine advanced neutron and x-ray diffuse scattering with numerical modeling to obtain insight into bulk structural correlations in HgBa 2 ⁢ CuO 4+δ . This cuprate exhibits a high optimal transition temperature of nearly 100 K, pristine charge-transport behavior, and a simple average crystal structure without long-range structural instabilities, and is therefore uniquely suited for investigations of intrinsic inhomogeneity. We uncover diffuse reciprocal-space patterns that correspond to prominent nanoscale correlations of atomic displacements perpendicular to the CuO 2 planes. The real-space nature of the correlations is revealed through three-dimensional pair distribution function analysis and complementary numerical refinement. We find that relative displacements of ionic and CuO 2 layers play a crucial role, and that the structural inhomogeneity is not directly caused by the presence of conventional point defects. The observed correlations are therefore intrinsic to HgBa 2 ⁢ CuO 4+δ , and thus likely important for the physics of cuprates more broadly. It is possible that the structural correlations are closely related to the unusual superconducting fluctuations and Mott-localization in these complex oxides. As advances in scattering techniques yield increasingly comprehensive data, the experimental and analysis tools developed here for large volumes of diffuse scattering data can be expected to aid future investigations of a wide range of materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Solid-state polymer-particle hybrid electrolytes: Structure and electrochemical properties

Solid-state electrolytes (SSEs) are challenged by complex interfacial chemistry and poor ion transport through the interfaces they form with battery electrodes. Here, we investigate a class of SSE composed of micrometer-sized lithium oxide (Li 2 O) particles dispersed in a polymerizable 1,3-dioxolane (DOL) liquid. Ring-opening polymerization (ROP) of the DOL by Lewis acid salts inside a battery cell produces polymer-inorganic hybrid electrolytes with gradient properties on both the particle and battery cell length scales. These electrolytes sustain stable charge-discharge behavior in Li||NCM811 and anode-free Cu||NCM811 electrochemical cells. On the particle length scale, Li 2 O retards ROP, facilitating efficient ion transport in a fluid-like region near the particle surface. On battery cell length scales, gravity-assisted settling creates physical and electrochemical gradients in the hybrid electrolytes. By means of electrochemical and spectroscopic analyses, we find that Li 2 O particles participate in a reversible redox reaction that increases the effective CE in anode-free cells to values approaching 100%, enhancing battery cycle life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

ReachNow EV Driving Data From Seattle, WA, Portland, OR, and New York, NY

ReachNow provided Idaho National Laboratory (INL) with a dataset describing approximately 49,000 trips taken by customers and employees in approximately 100 BMW i3 EVs operating in ReachNow's free-floating car-sharing fleets in Seattle, WA, Portland, OR, and New York, NY between May 2016 and February 2017. Data fields include vehicle rental period start and end timestamps, the location where vehicles were parked at the start and end of rental periods, and distance driven during rental periods. A field categorizing the user during each rental period is also included. This field makes it possible to identify when vehicles were rented by customers and when vehicles were driven by fleet management team employees to reposition, charge, or service the vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Evi-Pro Lite API

This application programing interface provides output from NLR's EVI-Pro model and is used to power the EVI-Pro Lite tool at https://afdc.energy.gov/evi-pro-lite. These endpoints provide daily (24-hour) fleet-level charging load profiles for a variety of customizable scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Rural EVSE Planning and Analysis

The dataset includes detailed anonymized public charging station usage from several rural stations on the ChargePoint and Shell Recharge Solutions (formerly Greenlots) networks situated in and around Athens, Ohio, a rural Appalachian community. Both Level 2 and DC fast charging stations are represented. Historical data in the set date back to 2019; additional data will be uploaded semiannually until the project's completion in 2023. Each charging session recorded includes information on date and time, location, charging station level, session duration, energy delivered, and fuel savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

EV Watts Public Database

With the rapid increase in vehicle electrification, there is a need for up-to-date, publicly available national data to understand end user charging and driving patterns, as well as vehicle and infrastructure performance, to inform research planning. Energetics worked with various partners to collect and analyze plug-in electric vehicle (PEV) and electric vehicle supply equipment (EVSE) data from 2019 to 2022. All sensitive attributes have been removed from this publicly available dataset. Researchers from one of the partner national labs under non-disclosure agreement (NDA) can request access to additional attributes by reaching out to evwattsdata@energetics.com.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

DOE EV Data Collection - Maintenance Data

Maintenance data includes information on maintenance performed on the electric vehicles, including preventive maintenance, service calls, and availability of the vehicles. The parameters collected, and their definitions, will vary due to the differences in maintenance tracking systems that exist between fleets. Parameter definitions are detailed in the data dictionary, and specific vehicle information is available in the vehicle attributes table. Vehicle ID can be used as a key between maintenance data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

DOE EV Data Collection - Charging Data

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The **Charge Data Daily/Session Dictionaries** contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The **Charger Attributes** table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The **Charger Attributes Data Dictionary** contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The **Charging Data** tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

DOE EV Data Collection - Facility Data

Facility data includes information on electricity consumption by larger-scale infrastructure, including buildings, solar arrays, and energy storage systems. Parameter definitions can be found in the data dictionary. If a connection between specific vehicle information and facility data exists, it will be available in the vehicle attributes table. Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Charging Hub Scenario Sheet Screenshot

The Excel-based CHECT tool estimates the LCOC ($/kWh) by charger type (Level 1, Level 2, and DC fast charging) for a given charging hub scenario. CHECT requires users to input certain charging hub scenario parameters, including the number of chargers, daily utilization, and charger replacement frequency by charger type. Additional inputs such as the charging schedule, local utility rates, capital/operational costs, and financial inputs can be customized by the user, or the tool can generate results using appropriate default values from literature for the charging hub scenario and service location(s). Using the above inputs, CHECT performs a robust techno-economic analysis to generate the LCOC by charger type, broken down by cost category (e.g., capital, operational, utility, taxes) for various combinations of charging hub types (multiunit dwelling or public) and ownership models (residential, utility, or private company). It also outputs the annual discounted cash flows and determines the most sensitive input variables. In addition, the tool allows users to easily compare the LCOC across various ownership models or across different states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

CalderaCast Web Interface

CalderaCast may be accessed as a web-based tool at the first link in the references section of this dataset. All of the necessary datasets to run the tool are built into the simulation software running behind the web interface. These input datasets are referenced by the additional links in the references section below. Many of those datasets are taken into machine-learning algorithms by the Caldera team and heavily processed to create internal datasets, which are then relied upon by the simulation to produce individual results. These internal datasets are not accessible and are not necessary for use of the CalderaCast tool.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗