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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x , and two conducting metallization systems, Cu-SiO 2 , and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Driven Tailoring Optimization of Thermoset Polymers Using Ultrasonics and Machine Learning

Thermoset polymers are highly demanded for their structural robustness, thermal stability, and chemical resistance. Tailoring the properties of these polymers for high-performance applications is often preferred to designing brand-new polymers. However, the traditional destructive techniques used to characterize their properties as a function of manufacturing parameters are expensive and time-consuming. A novel non-destructive, data-driven method leveraging ultrasonics and machine learning techniques to tailor the properties of thermosets as a function of the manufacturing parameters is demonstrated. Thermoset epoxy samples with varying curing temperatures (15–40 °C) and curing agent amounts (±40%) were manufactured and tested. Their curing kinetics were monitored by determining the sound speed in the material in real time, while the longitudinal modulus of the samples was determined post-cure. Machine learning models were developed using a k-nearest neighbors algorithm. These models were implemented to predict the curing and final elastic properties using the manufacturing parameters, i.e., stoichiometry and curing temperature, and vice versa. Understanding and modeling how these parameters affect the cure kinetics and final properties will allow for efficient and reliable optimization of thermoset tailoring and manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Magnetic interaction reversal in watermelon nanostructured Cr-doped Fe nanoclusters

Cr-doped core-shell Fe/Fe-oxide nanoclusters (NCs) were synthesized at varied atomic percentages of Cr from 0 at. % to 8 at. %. The low concentrations of Cr (<10 at. %) were selected in order to inhibit the complete conversion of the Fe-oxide shell to Cr{sub 2}O{sub 3} and the Fe core to FeCr alloy. The magnetic interaction in Fe/Fe-oxide NCs (∼25 nm) can be controlled by antiferromagnetic Cr-dopant. We report the origin of σ-FeCr phase at very low Cr concentration (2 at. %) unlike in previous studies, and the interaction reversal from dipolar to exchange interaction in watermelon-like Cr-doped core-shell NCs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Modular Design of Advanced Catalytic Materials Using Hybrid Organic-Inorganic Raspberry Particles

Catalysis is one of the most sophisticated areas of materials research that encompasses a diverse set of materials and phenomena occurring on multiple length and time scales. Designing catalysts that can be broadly applied toward global energy and environmental challenges requires the development of universal frameworks for complex catalytic systems through rational and independent (or quasi-independent) optimization of multiple structural and compositional features. Toward addressing this goal, a modular platform is presented in which sacrificial organic colloids bearing catalytic nanoparticles on their surfaces self-assemble with matrix precursors, simultaneously structuring the resulting porous networks and fine-tuning the locations of catalyst particles. This strategy allows combinatorial variations of the material building blocks and their organization, in turn providing numerous degrees of freedom for optimizing the material's functional properties, from the nanoscale to the macroscale. The platform enables systematic studies and rational design of efficient and robust systems for a wide range of catalytic and photocatalytic reactions, as well as their integration into industrial and other real-life environments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Perovskite-Type Solid Solution Nano-Electrocatalysts Enable Simultaneously Enhanced Activity and Stability for Oxygen Evolution

A trade-off between catalytic activity and structural stability generally exists in oxygen evolution electrocatalysis, especially in acidic environment. This dilemma limits the development of higher-performance electrocatalysts that are required by next-generation electrochemical technologies. In this work it is demonstrated that the inverse catalytic activity–structural stability relation can be broken by alloying catalytically inert strontium zirconate with the other catalytically active perovskite, strontium iridate. This strategy results in an alloyed perovskite electrocatalyst with simultaneously improved iridium mass activity and structural stability, by about five times, for the oxygen evolution reaction under acidic conditions. The experimental and theoretical results suggest that the alloying strategy generates multiple positive effects, mainly including the reduction of catalyst size, the decrease of catalyst covalency, and the weakening of surface oxygen-binding ability. The synergistic optimization of bulk and surface properties, as a result, enhances the intrinsic activity and availability of surface iridium sites, whilst significantly inhibiting the surface cation corrosion during electrocatalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Insights into Structural Evolution of Lithium Peroxides with Reduced Charge Overpotential in Li–O 2 System

Reducing charge-discharge overpotential is of great significance to enhance efficiency and cyclability of Li–O 2 batteries. Here we successfully achieve dramatically reduced charge overpotential (0.4 V) via a rational design of cathode architecture, which features highly uniform Pt and Pt 3 Co nanocrystals embedding within nitrogen-doped cobalt@graphene heterostructures. Because of the improvement on the catalytic efficiency for optimizing the electrical and dynamic properties, the cathode design enables promising electrochemical performance. More importantly, the results reveal different overpotential prompted by different structural evolution of bulk Li 2 O 2 , which depends on the Pt modification approach (surface-coating and bulk-doping). This dependence is found to be attributed to the influence of Pt nanocomponents and their dispersion on the formation and decomposition mechanism of Li 2 O 2 . Density functional theory calculations provide mechanistic insights into the promotional effect of Pt and Pt 3 Co on the reduction of the charge overpotential. Lastly, an inner relationship between overpotential and electrochemical kinetics is proposed.

25 ENERGY STORAGE↗
DOE OSTI2015

Revealing the preferred interlayer orientations and stackings of two-dimensional bilayer gallium selenide crystals

Characterizing and controlling the interlayer orientations and stacking order of bilayer two-dimensional (2D) crystals and van der Waals (vdW) heterostructure is crucial to optimize their electrical and optoelectronic properties. The four polymorphs of layered gallium selenide (GaSe) that result from different layer stacking provide an ideal platform to study the stacking configurations in bilayer 2D crystals. Here, through a controllable vapor-phase deposition method we selectively grow bilayer GaSe crystals and investigate their two preferred 0° or 60° interlayer rotations. The commensurate stacking configurations (AA' and AB-stacking) in as-grown 2D bilayer GaSe crystals are clearly observed at the atomic scale and the Ga-terminated edge structure are identified for the first time by using atomic-resolution scanning transmission electron microscopy (STEM). Theoretical analysis of the interlayer coupling energetics vs. interlayer rotation angle reveals that the experimentally-observed orientations are energetically preferred among the bilayer GaSe crystal polytypes. Here, the combined experimental and theoretical characterization of the GaSe bilayers afforded by these growth studies provide a pathway to reveal the atomistic relationships in interlayer orientations responsible for the electronic and optical properties of bilayer 2D crystals and vdW heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Highly Ordered Carbon Coating Prepared with Polyvinylidene Chloride Precursor for High‐Performance Silicon Anodes in Lithium‐Ion Batteries

Abstract A highly ordered carbon structure based on polyvinylidene chloride precursor has been developed to host silicon nanoparticles. The stoichiometric ratio of sacrificial H and Cl elements facilitated full utilization of the carbon content of the precursor that produced robust carbon coatings on silicon nanoparticles with excellent mechanic properties and electrochemical stabilities. The optimal sintering temperature and carbon content have been investigated. When evaluated as the anode of a lithium‐ion battery (LIB), the Si : C 1 : 2 composite sintered at 800 °C (SiC12‐800) provided excellent capacity retention of 85 % at 0.1 C after 50 cycles with an enhanced CE, which reached 99 % only after 10 cycles. The SiC12‐800 electrode maintained a specific capacity of 709.2 mAh/g after 300 cycles at 0.3 C, and delivered a high rate performance of 737.9 mAh/g and 485.6 mAh/g at 5 C and 10 C, respectively. The results indicate that polymer precursors with stoichiometric ratio of sacrificial elements have high potential for generating highly robust carbon coatings for silicon anodes in high energy density LIBs.

Zhou, Shuo↗
DOE OSTI2026

Kinetic control of phase evolution and defect-mediated recombination in AACVD-grown copper antimony sulphide thin films

Copper antimony sulphide (CAS) is a multinary chalcogenide semiconductor in which small deviations from stoichiometry can drive phase competition and strong defect-mediated modulation of optoelectronic properties. However, the systematic roles of copper precursor fraction and deposition time in governing phase evolution, off-stoichiometry, and recombination dynamics in aerosol-assisted chemical vapour deposition (AACVD)-grown undoped CAS thin films remain insufficiently understood. In this work, undoped CAS thin films were deposited by AACVD using Cu(dedtc)2 and Sb(dedtc)3 single-source precursors at 550 °C and a carrier gas flow rate of 150 sccm, while the Cu(dedtc)2 mole fraction (x = 0.15 – 0.55) and deposition time (1 – 2 hours) were systematically varied to probe how growth kinetics influence phase composition, microstructure, and defect-mediated optical properties without post-deposition annealing or extrinsic doping. Increasing copper precursor content drives phase evolution toward tetrahedrite-dominant CAS films at intermediate Cu(dedtc)2 mole fractions, with the film deposited at x = 0.35 exhibiting the strongest tetrahedrite character within the parameter space examined. The films are also copper-rich, antimony-poor, and sulphur-deficient, consistent with off-stoichiometric growth and intrinsic defect formation, plausibly including copper interstitials, Cu-on-Sb antisites, and sulphur vacancies. These growth-dependent compositional deviations are accompanied by tunable indirect optical bandgaps of approximately 1.60 – 2.18 eV and weak visible photoluminescence governed by defect-mediated recombination. Time-resolved photoluminescence reveals bi-exponential decay behaviour with lifetimes of approximately 0.1 – 3.6 ns, with emission dominated by slower donor–acceptor pair recombination and a smaller contribution from faster trap-assisted pathways. Collectively, these results establish an explicit kinetic process–structure–defect–property relationship for AACVD-grown CAS thin films and provide a growth–structure–property framework relevant to future optimization of CAS-based optoelectronic and energy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Reduction of Fire Hazard in Materials for Irrigators and Water Collectors in Cooling Towers

A way of reducing the fire hazard of PVC film used to make cooling-tower irrigators and water collectors is examined. A new generation of fire retardant, nanostructured magnesium hydroxide, is used to impart fire retardant properties. The fabrication technology is optimized with a roller-calendering manufacturing technique, and the permissible ranges of fire hazard indicators for materials in irrigators and water collectors are determined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Fabrication and Testing of Soy-Based Polyurethane Foam for Insulation and Structural Applications

This study is focused on the fabrication and evaluation of polyurethane (PU) foam fabricated using soy-based polyol that can potentially replace conventional petroleum-based PU foam for thermal applications. Rigid soy-based PU foam samples investigated in this study were produced by copolymerization of diisocyanate with soy-based polyol in presence of Xiameter X0193 surfactant, dibutyltin dilaurate (DBTL) as a catalyst, and water as a blowing agent. Different formulations with varying amounts of blowing agent and catalyst were investigated to optimize the mechanical and thermal properties of soy-based PU foam when compared to commercial petroleum-based foam as a control sample. The prepared samples were tested for density, compressive strength, thermal conductivity, tensile strength, dimensional stability, and thermal stability using the ASTM standards. The cellular structure of the fabricated foam samples was investigated using scanning electron microscopy. It was observed that an increase in density leads to a decrease in insulation. Overall, soy-based foams exhibited similar thermal conductivity as compared to petroleum-based foams.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Process Evaluation of AISI 4340 Steel Manufactured by Laser Powder Bed Fusion

Laser powder bed fusion (L-PBF) involves the consolidation of metal powder, layer by layer, through laser melting and solidification. In this study, process parameters are optimized for AISI 4340 steel to produce dense and homogeneous structures. The optimized process parameters produce mechanical properties at the center of the build plate that are comparable to wrought in the vertical and horizontal orientations after heat treatment and machining. Four subsequent builds are filled with specimens to evaluate the mechanical behavior as a function of location and orientation. Variations in the mechanical properties are likely due to recoater blade interactions with the powder and uneven gas flow. The results obtained in this study are analyzed to assess the reliability and reproducibility of the process. A different build evaluates the performance of near-net-shaped tensile specimens angled 35°-90° from the build plate surface (horizontal). Ductility measurements and surface roughness vary significantly as a function of the build angle. In the stress-relieved and as-built conditions, the mechanical behavior of vertically oriented specimens exhibits somewhat lower and more variable ductility than horizontally oriented specimens. Therefore, several process variables affect the mechanical properties of parts produced by the L-PBF process.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Properties of Equivalent Source Distributions Useful for Inverse Thermal Analysis

A general approach for inverse thermal analysis of heat deposition processes is parametric modeling of such processes using effective or equivalent source distributions (ESDs). ESDs can be adjusted conveniently for calculation of temperature fields, which are constrained according to boundary and temperature constraint conditions. This study applies properties of ESDs useful for parametric modeling of heat deposition processes. Inverse thermal analysis of a friction stir weld is presented, which is prototypical of processes tending to be complex with respect to energy deposition morphology. This analysis demonstrates properties of ESDs providing relatively optimal parametric modeling of energy deposition processes.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Correlation of rocket propulsion fuel properties with chemical composition using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry followed by partial least squares regression analysis

There is an increased need to more fully assess and control the composition of kerosene based rocket propulsion fuels, namely RP-1 and RP-2. In particular, it is crucial to be able to make better quantitative connections between the following three attributes: (a) fuel performance, (b) fuel properties (flash point, density, kinematic viscosity, net heat of combustion, hydrogen content, etc) and (c) the chemical composition of a given fuel (i.e., specific chemical compounds and compound classes present as a result of feedstock blending and processing). Indeed, recent efforts in predicting fuel performance through modeling put greater emphasis on detailed and accurate fuel properties and fuel compositional information. In this regard, advanced distillation curve (ADC) metrology provides improved data relative to classical boiling point and volatility curve techniques. Using ADC metrology, data obtained from RP-1 and RP-2 fuels provides compositional variation information that is directly relevant to predictive modeling of fuel performance. Often, in such studies, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is typically employed to provide chemical composition information. Building on approaches using GC-MS, but to glean substantially more chemical composition information from these complex fuels, we have recently studied the use of comprehensive two dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC - TOFMS) to provide chemical composition data that is significantly richer than that provided by GC-MS methods. In this report, by applying multivariate data analysis techniques, referred to as chemometrics, we are able to readily model (correlate) the chemical compositional information from RP-1 and RP-2 fuels provided using GC × GC - TOFMS, to the fuel property information such as that provided by the ADC method and other specification properties. We anticipate that this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an optimized approach to fuel formulation and specification for advanced engine cycles.

fuel↗
DOE OSTI2019

Recent advances in machine learning towards multiscale soft materials design

The multiscale design of soft materials requires an ensemble of computational techniques spanning quantum-chemistry to molecular dynamics to continuum modeling. The recent emergence of machine-learning (ML) and modern optimization algorithms has accelerated material property prediction, as well as stimulated the development of hybrid ML/molecular modeling methodologies capable of providing physical insights unobtainable from purely physics-based modeling and intuition. Such hybrid techniques also have important ramifications for the ML-enhanced interpretation of results from simulations and experiments alike. Leveraging ML techniques for the design of chemical or morphological structures based on a target property or functionality represents an exciting goal for the general area of soft materials, including polymers, liquid crystals, colloids, or biomolecules, to name a few representative classes of systems. Furthermore, we provide a perspective on recent work using ML techniques of relevance for the multiscale design of soft materials and outline potential future directions of interest to the soft materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Structure-guided design of potent and permeable inhibitors of MERS coronavirus 3CL protease that utilize a piperidine moiety as a novel design element

There are currently no approved vaccines or small molecule therapeutics available for the prophylaxis or treatment of Middle East Respiratory Syndrome coronavirus (MERS-CoV) infections. MERS-CoV 3CL protease is essential for viral replication; consequently, it is an attractive target that provides a potentially effective means of developing small molecule therapeutics for combatting MERS-CoV. We describe herein the structure-guided design and evaluation of a novel class of inhibitors of MERS-CoV 3CL protease that embody a piperidine moiety as a design element that is well-suited to exploiting favorable subsite binding interactions to attain optimal pharmacological activity and PK properties. Additionally, the mechanism of action of the compounds and the structural determinants associated with binding were illuminated using X-ray crystallography.

3CL protease↗