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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Freestanding BaTiO 3 ‐Au Vertically Aligned Nanocomposite toward Flexible Multi‐Sensing Platform

Abstract Flexible and wearable sensors show enormous potential for personalized healthcare devices by real‐time monitoring of an individual's health. Typically, a single functional material is selected for one sensor to sense a particular physical signal while multiple materials will be selected for multi‐mode sensing. Vertically aligned nanocomposites (VANs) have recently demonstrated various material combinations and novel coupled multifunctionalities that are hard to achieve in any single‐phase material alone, including multiphase multiferroics, magneto‐optic coupling, and strong magnetic and optical anisotropy. Integrating these novel VANs into wearable sensors shows enormous potential in multi‐mode sensing owing to their multifunctional nature. In this work, the transfer of VANs onto polydimethylsiloxane as a novel flexible chemical and pressure sensor is demonstrated. For this demonstration, the classical BaTiO 3 ‐Au VAN with combined plasmonic and piezoelectric properties is used to demonstrate a multi‐sensing mechanism. A thin water‐soluble buffer of Sr 3 Al 2 O 6 serves as a buffer layer for the epitaxial growth and transfer process. The electrical output based on the piezoelectric responses and identifying 4‐mercaptobenzoic acid by surface‐enhanced Raman spectroscopy reveal great potential for free‐standing VANs in a wearable multifunctional sensing platform.

Tsai, Benson Kunhung [School of Materials Engineer↗
NASA NTRS1986

Creep fatigue life prediction for engine hot section materials (isotropic): Fourth year progress review

As gas turbine technology continues to advance, the need for advanced life prediction methods for hot section components is becoming more and more evident. The complex local strain and temperature histories at critical locations must be accurately interpreted to account for the effects of various damage mechanisms (such as fatigue, creep, and oxidation) and their possible interactions. As part of the overall NASA HOST effort, this program is designed to investigate these fundamental damage processes, identify modeling strategies, and develop practical models which can be used to guide the early design and development of new engines and to increase the durability of existing engines.

Nelson, Richard S.↗
DOE OSTI2019

High performance thermoelectric module through isotype bulk heterojunction engineering of skutterudite materials

We demonstrate filled CoSb 3 skutterudite materials with excellent thermoelectric (TE) performance that results in one of the highest reported single stage module efficiency. Here, the improvement in TE material performance was obtained by creating isotype n/n “bulk heterojunction” structure through assembly of novel skutterudite nanocrystals with different Yb-doping content. Combination of significant increase in carrier transport through heterojunction structure and reduction in long-range acoustic phonon transmission by two-phase mixture resulted in enhanced power factor and reduced lattice thermal conductivity. As a result, the figure-of-merit (zT) of heterojunction TE material is improved by more than 35% compared with pristine single homogeneous material. Using these improved TE materials, a high module conversion efficiency of ~9.15% was obtained when operating between 650 °C and 50 °C. This is one of the highest conversion efficiency among the practically measured single stage modules.

42 ENGINEERING↗
DOE OSTI2021

Phonon-engineered extreme thermal conductivity materials

Materials with ultrahigh or low thermal conductivity are desirable for many technological applications, such as thermal management of electronic and photonic devices, heat exchangers, energy converters and thermal insulation. Recent advances in simulation tools (first principles, the atomistic Green’s function and molecular dynamics) and experimental techniques (pump–probe techniques and microfabricated platforms) have led to new insights on phonon transport and scattering in materials and the discovery of new thermal materials, and are enabling the engineering of phonons towards desired thermal properties. Here, we review recent discoveries of both inorganic and organic materials with ultrahigh and low thermal conductivity, highlighting heat-conduction physics, strategies used to change thermal conductivity, and future directions to achieve extreme thermal conductivities in solid-state materials.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Engine technology

Materials used in a presentation on development of engine technology for electric flight systems are presented. Component and system technology issues, NASA's role, and flight test requirements are outlined.

Hoffman, A. C.↗
NASA NTRS1981

Hostile environmental conditions facing candidate alloys for the automotive Stirling engine

The materials research program in support of the Automotive Stirling Engine Project focuses on the hot heater head of the engine including the heater head tubes, cylinders, and regenerator housings, which are considered to be the most critical components from a materials viewpoint. The specific areas of investigation in the program involve hydrogen permeability testing, doping of the hydrogen working fluid to reduce permeability rates, oxidation/corrosion studies, creep-rupture evaluation, and assessing effects of hydrogen environment on mechanical properties. Emphasis is placed on the materials challenges that result from the use of hydrogen as the working fluid. Previously announced in STAR as N81-26236

Stephens, J. R.↗
DOE OSTI2020

A Scalable Method for Thickness and Lateral Engineering of 2D Materials

The physical properties of two-dimensional (2D) materials depend strongly on the number of layers. Hence, methods for controlling their thickness with atomic layer precision are highly desirable, yet still too rare, and demonstrated for only a limited number of 2D materials. Here we present a simple and scalable method for the continuous layer-by-layer thinning that works for a large class of 2D materials, notably layered germanium pnictides and chalcogenides. It is based on a simple oxidation/etching process, which selectively occurs on the topmost layers. Through a combination of atomic force microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy and X-ray diffraction experiments we demonstrate the thinning method on germanium arsenide (GeAs), germanium sulfide (GeS) and germanium disulfide (GeS 2 ). We use first-principles simulation to provide insights into the oxidation mechanism. Our strategy, which could be applied to other classes of 2D materials upon proper choice of the oxidation/etching reagent, could pave the way for the realization of 2D material-based devices, such as electronic or optoelectronic ones, where a precise control over the number of layers (hence over the material’s physical properties) is needed. We also show that when used in combination with lithography, our method can be used to make precise patterns in the 2D materials.

2D materials↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Single Material Band Gap Engineering in GaAs Nanowires

The structural and optical properties of GaAs nanowire with mixed zinc-blende/wurtzite structure are presented. High resolution transmission electron microscopy indicates the presence of a variety of shorter and longer segments of zinc-blende or wurtzite crystal phases. Sharp photoluminescence lines are observed with emission energies tuned from 1.515 eV down to 1.43 eV. The downward shift of the emission peaks can be understood by carrier confinement at the wurtzite/zinc-blende heterojunction, in quantum wells and in random short period superlattices existent in these nanowires, assuming the theoretical staggered band-offset between wurtzite and zinc-blende GaAs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Data management and processing workflow for the Materials Physics and Engineering group beamlines at the Advanced Photon Source

The ability to store, organize, process and distribute experimental data effectively, efficiently and securely is particularly important for large user facilities like the Advanced Photon Source. In this article, the deployment of the APS Data Management System (DM) at the 1-ID and 6-BM beamlines of the APS is described. These two beamlines support a wide range of experimental techniques and generate data at relatively high rates, making them ideal candidates to illustrate the deployment and customization of the DM system and its tools. Using several usage examples at these beamlines, various capabilities of the DM system are described.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

IST for Extended Deterrence

Department of Energy (DOE) scientists have long enjoyed technical collaboration with Japanese colleagues ₋ valuing their technical and scientific prowess in materials science and engineering, large-scale, scientific computing, and many other areas. In particular, NNSA’s laboratories are attractive partners for this effort because they have a heritage of performing high quality, basic research; they work with an awareness of the dual use nature of emerging science and technology; and, importantly, they work with deep national security sensibilities through engagement with NNSA and other national security mission responsibilities. Other DOE laboratories with significant national security bona fides are also significant collaborators.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Design of Novel Hot Gas Path Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This report covers the activities associated with the development and evaluation of two high-γ’ superalloys that were designed by external partners on this project, namely Carpenter Technologies Corporation and the University of California-Santa Barbra. One alloy was GammaPrint-700, a cobalt-base superalloy, and the other a nickel-base (Ni-base) superalloy GammaPrint-1100. Both were found to be printable through laser powder bed fusion (LPBF) additive manufacturing, with optimal process parameter sets being identified for each alloy. Further, high temperature mechanical testing was conducted on each alloy that showed both materials performed better than the comparative baseline (LPBF Hastelloy X), with the Ni-base superalloy being down-selected for scaling and printing of the tip shoe components.

36 MATERIALS SCIENCE↗