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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Ultrafast Reactive Laser Sintering of Highly Conductive Garnet-Type LLZTO Solid Electrolytes

Rapid and scalable fabrication of garnet-type solid electrolytes remains a major challenge for the practical deployment of lithium metal batteries. Here, we report reactive laser sintering (RLS) as an ultrafast and potentially scalable strategy for fabricating garnet-type Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) solid electrolytes. RLS of LLZTO enables simultaneous reaction and densification, achieving ∼95% relative density while minimizing lithium loss and suppressing secondary phase formation. Compared to conventional furnace sintering, RLS promotes enhanced grain growth and improved densification, leading to improved ionic conductivity (0.36 ± 0.08 mS cm −1 ) while maintaining comparable activation energies for Li + transport. Structural characterization by X-ray diffraction (XRD), Raman spectroscopy, and solid-state 6 Li/ 7 Li NMR confirms the formation of cubic garnet LLZTO with homogeneous microscale elemental distribution. In addition, nanoindentation measurements demonstrate that RLS preserves the mechanical properties of the garnet framework despite ultrafast localized thermal processing. By integrating simultaneous reaction and densification with tunable microstructural control, reactive laser sintering provides a promising manufacturing pathway for high-performance garnet solid electrolytes toward next-generation solid-state batteries.

CO2 laser↗
NASA NTRS1982

Criteria for the emergence and evolution of life in the solar system

It is proposed that there are at least 25 specific conditions or requirements which have to be fulfilled in order for life to appear and evolve in a planetary system such as ours. The criteria are grouped into five major general areas: (1) Planetary criteria relate to the physical properties of the planet as it is formed and as it becomes a cosmic body with the potential to provide an abode for life. (2) Chemical criteria concern the composition, availability of effective energy sources, and chemical constraints of the environment where reactions take place for the prebiological formation of biochemical compounds. (3) Protobiological criteria relate to the prebiologically synthesized oligomeric and polymeric biomolecules, how they interact cooperatively to form protobiological structures and functions, and self-assemble to give rise to a living system. (4) Evolutionary criteria are concerned with the processes responsible for the increase in complexity of organisms by genomic multiplication, symbiotic integration, and cellular differentiation, as well as with the negentropic ability of organisms to continuously recycle all the volatile biogenic elements. (5) Stellar criteria relate to the elemental composition mass, lifetime, and other features of Main Sequence stars which may be surrounded by planetary systems similar to our own.

Oro, J.↗
NASA NTRS1993

Modelling intelligent behavior

An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

Green, H. S.↗
NASA NTRS2003

Fabricating Composite-Material Structures Containing SMA Ribbons

An improved method of designing and fabricating laminated composite-material (matrix/fiber) structures containing embedded shape-memory-alloy (SMA) actuators has been devised. Structures made by this method have repeatable, predictable properties, and fabrication processes can readily be automated. Such structures, denoted as shape-memory-alloy hybrid composite (SMAHC) structures, have been investigated for their potential to satisfy requirements to control the shapes or thermoelastic responses of themselves or of other structures into which they might be incorporated, or to control noise and vibrations. Much of the prior work on SMAHC structures has involved the use SMA wires embedded within matrices or within sleeves through parent structures. The disadvantages of using SMA wires as the embedded actuators include (1) complexity of fabrication procedures because of the relatively large numbers of actuators usually needed; (2) sensitivity to actuator/ matrix interface flaws because voids can be of significant size, relative to wires; (3) relatively high rates of breakage of actuators during curing of matrix materials because of sensitivity to stress concentrations at mechanical restraints; and (4) difficulty of achieving desirable overall volume fractions of SMA wires when trying to optimize the integration of the wires by placing them in selected layers only.

Turner, Travis L.↗
NASA NTRS1948

Metallurgical Research Relating to the Development of Metals and Alloys for Use in the High-Temperature Components of Jet-Engines, Gas Turbines and Other Aircraft Propulsion Systems

Considerable work has been done on report preparation. All items listed in the March program report will be reported during July. Fundamental studies are in progress to establish the fundamental processes by which treatments and composition control properties of commercial alloys at high temperatures. As yet work has been confined to Low-Carbon N155 alloy and progress has been reported twice previously. The work is divided into two sections: studies of solution treated and aged material and studies of rolled structures. Electron microscopic work has been started as an additional technique for the studies. Brief descriptions of experimental techniques used, results, and interpretation of the data obtained since the last report covering this field are summarized below. Since the work outlined is to a large extent still in progress, the discussion given is to be considered tentative and subject to further modification as additional data becomes available.

Source record↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2025

Transformation of TiN to TiNO Films via In-Situ Temperature-Dependent Oxygen Diffusion Process and Their Electrochemical Behavior

Titanium oxynitride (TiNO) thin films represent a multifaceted material system applicable in diverse fields, including energy storage, solar cells, sensors, protective coatings, and electrocatalysis. This study reports the synthesis of TiNO thin films grown at different substrate temperatures using pulsed laser deposition. A comprehensive structural investigation was conducted by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Non-Rutherford backscattering spectrometry (N-RBS), and X-ray absorption spectroscopy (XAS), which facilitated a detailed analysis that determined the phase, composition, and crystallinity of the films. Structural control was achieved via temperature-dependent oxygen in-diffusion, nitrogen out-diffusion, and the nucleation growth process related to adatom mobility. The XPS analysis indicates that the TiNO films consist of heterogeneous mixtures of TiN, TiNO, and TiO2 phases with temperature-dependent relative abundances. The correlation between the structure and electrochemical behavior of the thin films was examined. The TiNO films with relatively higher N/O ratio, meaning less oxidized, were more electrochemically active than the films with lower N/O ratio, i.e., more oxidized films. Films with higher oxidation levels demonstrated enhanced crystallinity and greater stability under electrochemical polarization. These findings demonstrate the importance of substrate temperature control in tailoring the properties of TiNO film, which is a fundamental part of designing and optimizing an efficient electrode material.

Cherono, Sheilah↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.

Rao, Nageswara↗
DOE OSTI2025

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗
NASA NTRS1988

Karhunen-Loeve expansion of Burgers' model of turbulence

The properties of the Karhunen-Loeve expansion of a strongly inhomogeneous random process are examined with emphasis on applications to turbulent flow fields. The ability of the KL expansion to represent functions that have both slow and rapid variations in a relatively small number of expansion terms is tested on a one-dimensional model based on the forced Burgers' equation. The rate of the convergence of the expansion is evaluated, and its dependence on the Reynolds number is determined. It is shown that the KL eigenfunctions possess wall boundary layers attached to outer structures that are independent of the Reynolds number (at high Reynolds numbers). It is also shown that the spectrum of eigenvalues is broad at large Reynolds numbers, requiring many terms to represent higher-order derivatives of the function.

Chambers, D. H.↗
NASA NTRS1998

Structures and Mechanical Properties of Natural and Synthetic Diamonds

A revolution in the diamond technology is in progress, as the low-pressure process becomes an industrial reality. It will soon be possible to take advantage of the demanding properties of diamond to develop a myriad of new applications, particularly for self-lubricating, wear-resistant, and superhard coatings. The production of large diamond films or sheets at low cost, a distinct possibility in the not-too-distant future, may drastically change tribology technology, particularly regarding solid lubricants and lubricating materials and systems. This paper reviews the structures and properties of natural and synthetic diamonds to gain a better understanding of the tribological properties of diamond and related materials. Atomic and crystal structure, impurities, mechanical properties, and indentation hardness of diamond are described.

Miyoshi, Kazuhisa↗
NASA NTRS1995

Development and computer implementation of design/analysis techniques for multilayered composite structures. Probabilistic fiber composite micromechanics

Probabilistic composite micromechanics methods are developed that simulate expected uncertainties in unidirectional fiber composite properties. These methods are in the form of computational procedures using Monte Carlo simulation. The variables in which uncertainties are accounted for include constituent and void volume ratios, constituent elastic properties and strengths, and fiber misalignment. A graphite/epoxy unidirectional composite (ply) is studied to demonstrate fiber composite material property variations induced by random changes expected at the material micro level. Regression results are presented to show the relative correlation between predictor and response variables in the study. These computational procedures make possible a formal description of anticipated random processes at the intraply level, and the related effects of these on composite properties.

Stock, Thomas A.↗
DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
NASA NTRS1990

Plasma-assisted physical vapor deposition surface treatments for tribological control

In any mechanical or engineering system where contacting surfaces are in relative motion, adhesion, wear, and friction affect reliability and performance. With the advancement of space age transportation systems, the tribological requirements have dramatically increased. This is due to the optimized design, precision tolerance requirements, and high reliability expected for solid lubricating films in order to withstand hostile operating conditions (vacuum, high-low temperatures, high loads, and space radiation). For these problem areas the ion-assisted deposition/modification processes (plasma-based and ion beam techniques) offer the greatest potential for the synthesis of thin films and the tailoring of adherence and chemical and structural properties for optimized tribological performance. The present practices and new approaches of applying soft solid lubricant and hard wear resistant films to engineering substrates are reviewed. The ion bombardment treatments have increased film adherence, lowered friction coefficients, and enhanced wear life of the solid lubricating films such as the dichalcogenides (MoS2) and the soft metals (Au, Ag, Pb). Currently, sputtering is the preferred method of applying MoS2 films; and ion plating, the soft metallic films. Ultralow friction coefficients (less than 0.01) were achieved with sputtered MoS2. Further, new diamond-like carbon and BN lubricating films are being developed by using the ion assisted deposition techniques.

Spalvins, Talivaldis↗
NASA NTRS1997

ICAN Computer Code Adapted for Building Materials

The NASA Lewis Research Center has been involved in developing composite micromechanics and macromechanics theories over the last three decades. These activities have resulted in several composite mechanics theories and structural analysis codes whose applications range from material behavior design and analysis to structural component response. One of these computer codes, the Integrated Composite Analyzer (ICAN), is designed primarily to address issues related to designing polymer matrix composites and predicting their properties - including hygral, thermal, and mechanical load effects. Recently, under a cost-sharing cooperative agreement with a Fortune 500 corporation, Master Builders Inc., ICAN was adapted to analyze building materials. The high costs and technical difficulties involved with the fabrication of continuous-fiber-reinforced composites sometimes limit their use. Particulate-reinforced composites can be thought of as a viable alternative. They are as easily processed to near-net shape as monolithic materials, yet have the improved stiffness, strength, and fracture toughness that is characteristic of continuous-fiber-reinforced composites. For example, particlereinforced metal-matrix composites show great potential for a variety of automotive applications, such as disk brake rotors, connecting rods, cylinder liners, and other hightemperature applications. Building materials, such as concrete, can be thought of as one of the oldest materials in this category of multiphase, particle-reinforced materials. The adaptation of ICAN to analyze particle-reinforced composite materials involved the development of new micromechanics-based theories. A derivative of the ICAN code, ICAN/PART, was developed and delivered to Master Builders Inc. as a part of the cooperative activity.

Murthy, Pappu L. N.↗
NASA NTRS2014

In Situ Probe Science at Saturn

A fundamental goal of solar system exploration is to understand the origin of the solar sys-tem, the initial stages, conditions, and processes by which the solar system formed, how the formation pro-cess was initiated, and the nature of the interstellar seed material from which the solar system was born. Key to understanding solar system formation and subsequent dynamical and chemical evolution is the origin and evolution of the giant planets and their atmospheres. Several theories have been put forward to explain the process of solar system formation, and the origin and evolution of the giant planets and their atmospheres. Each theory offers quantifiable predictions of the abundances of noble gases He, Ne, Ar, Kr, and Xe, and abundances of key isotopic ratios 4He3He, DH, 15N14N, 18O16O, and 13C12C. Detection of certain dis-equilibrium species, diagnostic of deeper internal pro-cesses and dynamics of the atmosphere, would also help discriminate between competing theories. Measurements of the critical abundance profiles of these key constituents into the deeper well-mixed at-mosphere must be complemented by measurements of the profiles of atmospheric structure and dynamics at high vertical resolution and also require in situ explora-tion. The atmospheres of the giant planets can also serve as laboratories to better understand the atmospheric chem-istries, dynamics, processes, and climates on all planets including Earth, and offer a context and provide a ground truth for exoplanets and exoplanetary systems. Additionally, Giant planets have long been thought to play a critical role in the development of potentially habitable planetary systems. In the context of giant planet science provided by the Galileo, Juno, and Cassini missions to Jupiter and Sat-urn, a small, relatively shallow Saturn probe capable of measuring abundances and isotopic ratios of key at-mospheric constituents, and atmospheric structure in-cluding pressures, temperatures, dynamics, and cloud locations and properties not accessible by remote sens-ing can serve to test competing theories of solar system and giant planet origin, chemical, and dynamical evolution.

Interstellar↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗
NASA NTRS2013

Understanding the Relationships Between Lightning, Cloud Microphysics, and Airborne Radar-derived Storm Structure During Hurricane Karl (2010)

This study explores relationships between lightning, cloud microphysics, and tropical cyclone (TC) storm structure in Hurricane Karl (16 September 2010) using data collected by the NASA DC-8 and Global Hawk (GH) aircraft during NASA's Genesis and Rapid Intensification Processes (GRIP) experiment. The research capitalizes on the unique opportunity provided by GRIP to synthesize multiple datasets from two aircraft and analyze the microphysical and kinematic properties of an electrified TC. Five coordinated flight legs through Karl by the DC-8 and GH are investigated, focusing on the inner-core region (within 50km of the storm center) where the lightning was concentrated and the aircraft were well coordinated. GRIP datasets are used to compare properties of electrified and nonelectrified inner-core regions that are related to the noninductive charging mechanism, which is widely accepted to explain the observed electric fields within thunderstorms. Three common characteristics of Karl's electrified regions are identified: 1) strong updrafts of 10-20ms21, 2) deep mixed-phase layers indicated by reflectivities.30 dBZ extending several kilometers above the freezing level, and 3) microphysical environments consisting of graupel, very small ice particles, and the inferred presence of supercooled water. These characteristics describe an environment favorable for in situ noninductive charging and, hence, TC electrification. The electrified regions in Karl's inner core are attributable to a microphysical environment that was conducive to electrification because of occasional, strong convective updrafts in the eyewall.

Cloud mircophysics↗