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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cold Sintered Ceramics and Cements for Part Encapsulation

In this project, ceramic encapsulation materials were studied for high temperature (>~°500 C) applications where typical polymer encapsulants are unstable. A new low temperature (<~°200 C) method of processing ceramics, the cold sintering process was examined. Additionally, commercially available high temperature ceramic cements were investigated. In both cases, the mechanical strengths of available materials are less than desired (i.e., desired strengths similar to Si 3 N 4 ), limiting applicability. Composite designs to increase mechanical strength are suggested. Additionally, non-uniformities in stresses and densification while embedding alumina sheets in encapsulants via cold sintering using uni-axial pressing led to fracture of sheets, and an alternative iso-static based approach is recommended for future studies.

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

Evaluating Fracture Behavior of Bioinspired Alumina-YSZ Composites through Static and Dynamic Mechanical Testing

Ceramic materials are known for their high hardness and strength but are limited by low toughness and sudden failure. Inspired by the microstructure of dental enamel, which features undulating rods that promote crack deflection and energy absorption, architected specimens were designed and manufactured. A bespoke ball-on-ring (BoR) testing apparatus measured the static biaxial rupture strength of these bioinspired materials. Additionally, impact testing with spherical steel projectiles assessed their behavior under dynamic conditions. Monolithic alumina specimens were compared to architected alumina reinforced with yttria-stabilized zirconia (YSZ) rods produced via direct ink write 3D printing. BoR tests revealed that monolithic alumina had a Weibull modulus of 10.53 and a characteristic strength of 372.3 MPa, while the composite specimens showed a Weibull modulus of 5.46 and a characteristic strength of 213.3 MPa. Although the composite failed at lower stresses and projectile velocities, it exhibited notable crack deflection and fracture resistance, with cracks being effectively interrupted by the rod inclusions. The composite specimens also resulted in fewer and larger fragments upon impact. Finite element simulations confirmed the effectiveness of the rod structures in enhancing fracture resistance.

Bioinspired materials↗
DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dopamine-Mediated Polymer Coating Facilitates Area-Selective Atomic Layer Deposition

Area-selective atomic layer deposition (ALD) has the potential to significantly improve current fabrication approaches by introducing a bottom-up process in which robust and conformal thin films are selectively deposited onto patterned substrates. This bottom-up approach requires selective areas of the substrates to be masked to inhibit deposition. Spontaneous self-assembly and organization of a mask, incorporating adhesion and other functions, are particularly attractive for this role as they do not require a separate patterning step. In this work, we make use of the pH/light tunability of catechol adhesion to develop a catechol-functionalized polymer that exhibits tunable adhesion strengths on different materials based on their specific chemistry. Tunable selective deposition was shown between metal/metal oxide substrates by controlling the local pH. Moreover, by controlling the adhesion strength through UV light, the deposition of hafnium oxide (HfO 2 ) during ALD was successfully inhibited.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars

The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Transport and mechanical behavior in PEO-LLZO composite electrolytes

Abstract Composite solid electrolytes (CEs), wherein ion-conducting polymer and ceramic/glass is mixed, are promising candidates for all-solid-state batteries due to their promise of acceptable ionic conductivity and mechanical properties compared to their individual constituents. While numerous studies have focused on improving the performance of CEs, it is still unclear what the material targets are that can result in improved macroscopic performance especially in light of the coupled needs for high transport and high mechanical strength in these materials. In this study, a two-dimensional (2D) mathematical model is developed to investigate electrochemical and mechanical characteristics of CEs. The model is compared to CEs consisting of poly-ethylene-oxide (PEO) polymer and lithium lanthanum zirconium oxide (LLZO) ceramic material with examination of the impact of varying LLZO volume fractions. The potential drop at the PEO-LLZO interface is evaluated using the junction potential theory. Using experimental data from the literature, the model estimates the ionic conductivity, effective transference number, and mechanical stiffness of the CEs. While the mechanical stiffness improves with increasing volume fraction of LLZO, the impact on conductivity and transference number depends on interfacial resistance at the interface. Finally, the study reports CE’s potential to enhance Li-ion transport and mechanical properties to inhibit lithium (Li) dendrite growth.

25 ENERGY STORAGE↗
DOE OSTI2021

National and regional waste stream in the United States: conformance and disparity

Accurate estimation of municipal solid waste (MSW) composition is critical for efficient waste management. In the United States, site-specific and material flow approaches determine the MSW composition at regional and national levels. The material flow-based national estimates are determined by the US EPA; the US EPA’s estimates are known to differ substantially from the aggregated tonnage of MSW managed by waste handling facilities in the United States. However, the material class-specific discrepancies of the US EPA’s material flow approach resulting in these differences are unknown. To find the basis of these discrepancies, we analyze the discarded MSW stream of 27 US states, which roughly accounts for 73 percent of the US population. Our analysis indicates that the material flow-based national estimates are accurate for the food, plastic, and glass material classes. In contrast, we find that the US EPA’s material flow-based predictions underestimate paper waste disposal by at least 15 million tons annually. These differences likely stem from incorrect assumptions of residence time. These results highlight the material class-specific strengths and drawbacks of the US EPA’s material flow-based MSW estimates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Adhesion in thermomechanically processed seaweed-lignocellulosic composite materials

The increasing concerns associated with petroleum-derived polymers motivate the development of sustainable, renewably sourced alternatives. In ubiquitous applications such as structural materials for infrastructure, the built environment as well as packaging, where natural materials such as wood are used, we rely on nonrenewable and nondegradable polymers to serve as adhesives. In wood panels, such as medium density fiberboards (MDFs), formaldehyde-based resins are predominantly used to bond wood fibers and to provide strength to the materials. To further mitigate the environmental impact of construction materials, more sustainable adhesives need to be investigated. Here, in this article, we introduce Ulva seaweed as an adhesive to enable cohesion and strength in hot-pressed wood panels. Upon hot-pressing, powdered Ulva flows in between the wood particles, generating a matrix, which provides strong binding. We show that the flexural strength of Ulva-bonded wood biocomposites increases with increasing Ulva concentrations. At an Ulva concentration of 40 wt%, our composites reach an average elastic modulus of 6.1 GPa, and flexural strength of 38.2 MPa (compared to 4.7 GPa and 22.6 MPa, respectively, for pure wood compressed at the same pressing conditions). To highlight the bonding mechanisms, we performed infrared and x-ray photoelectron spectroscopy and identified indications of fatty acid mobility during hot-pressing. In addition, we demonstrate that the presence of Ulva improves other properties of the composites such as water resistance and fame retardancy. Ulva is also shown to behave as an excellent adhesive agent between two prepressed beams. Finally, we perform an in-depth analysis of the environmental impact of wood-Ulva biocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A continuum damage approach to spallation and the role of microinertia

Spall failure is of interest due to its prevalence in high strain rate problems in which the spallation is driven by the interaction of release waves. In this article, a porosity-based damage model that includes microinertial effects is used to examine spall failure. The model is successfully calibrated to plate impact-driven experiments and then used to evaluate experimental conditions producing more extreme strain rate conditions, such as those in laser-driven experiments. The incorporation of microinertia allows us to better understand the increase in apparent macroscopic spall strength seen at high strain rates. Correspondingly, we conclude that the incorporation of microinertial effects improves the model’s predictive capabilities. Microinertial effects result in more severe local tensile stresses that affect the damage evolution, and microinertia can play a significant role in the approach to the material’s ideal strength at extreme loading rates. Additionally, a preliminary parametric study is also carried out to investigate the role of microstructural aspects such as nucleation volume fraction and initial pore radius. One counter-intuitive result from the microinertial effects is that, for a given nucleation site volume fraction, having larger initial pore nucleation sites can lead to an improved macroscopic spall strength.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2021

X-ray diffraction and in situ pressurization of dentine apatite reveals nanocrystal modulus stiffening upon carbonate removal

Bone-like materials comprise carbonated-hydroxyapatite nanocrystals (c-Ap) embedding a fibrillar collagen matrix. The mineral particles stiffen the nanocomposite by tight attachment to the protein fibrils creating a high strength and toughness material. The nanometer dimensions of c-Ap crystals make it very challenging to measure their mechanical properties. Mineral in bony tissues such as dentine contains 2~6 wt.% carbonate with possibly different elastic properties as compared with crystalline hydroxyapatite. Here we determine strain in biogenic apatite nanocrystals by directly measuring atomic deformation in pig dentine before and after removing carbonate. Transmission electron microscopy revealed the platy 3D morphology while atom probe tomography revealed carbon inside the calcium rich domains. High-energy X-ray diffraction in combination with in situ hydrostatic pressurization quantified reversible c-Ap deformations. Additionally, crystal strains differed between annealed and ashed (decarbonated) samples, following 1 or 10 h heating at 250 °C or 550 °C respectively. Measured bulk moduli (K) and a-/c-lattice deformation ratios (η) were used to generate synthetic K syn and ηsyn identifying the most likely elastic constants C 33 and C 13 for c-Ap. These were then used to calculate the nanoparticle elastic moduli. For ashed samples, we find an average E 11 =107 GPa and E 33 =128 GPa corresponding to ~5% and ~17% stiffening of the a-/c-axes of the nanocrystals as compared with the biogenic nanocrystals in annealed samples. Ashed samples exhibit ~10% lower Poisson's ratios as compared with the 0.25~0.36 range of carbonated apatite. Carbonate in c-Ap may therefore serve for tuning local deformability within bony tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Filament Formation and Melt Spinning of Coal-Based Mesophase Pitch for Carbon Fiber Production

High-performance carbon fibers excel as high specific strength and modulus materials, and are utilized in composites applications ranging across aerospace, automotive, energy, infrastructure, and sports equipment sectors. Compared to PAN-based carbon fiber, mesophase pitch-based carbon fiber has lower tensile strength but provides a higher modulus, higher thermal conductivity, and a lower cost potential. However, challenges in stable, continuous melt spinning processing are a serious limiting factor. Unlike typical melt spinning of long, linear chain polymers, mesophase pitch is a comprised of relatively shorter polycyclic aromatic hydrocarbons which form a liquid crystal. In its ‘green’ or ‘as-spun’ state, the fibers are very fragile. Moreover, its temperature of processing is quite high, often approaching 400ºC. Complex flow dynamics combined with short length and time scale heat transfer render its melt spinning a formidable processing challenge. Improved understanding of the root causes for nascent filament breakage and spinning instabilities are needed. This work aims to determine fundamental phenomena that govern mesophase pitch filament formation by comparison of required draw force and uninterrupted spinning minutes with applied draw down ratio. The effect of perturbations on spinning stability are analyzed using capillary rheometry, microscopy of elongating filaments, and filtration.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Development of a silicon carbide ceramic based counter-flow heat exchanger by binder jetting and liquid silicon infiltration for concentrating solar power

A silicon carbide ceramic counter-flow heat exchanger with integrated headers was printed by binder jetting additive manufacturing process. Multiple phenolic binder infiltration cycles (3 or 5) followed by pyrolysis were conducted to increase the net carbon content of the printed SiC specimens. Subsequently, to attain full densification, silicon melt infiltration was used. The microstructure and mechanical properties were comprehensively characterized on the densified material. The chemical compositions and visual distribution of the various regions in the specimens were determined via scanning electron microscopy, while X-ray diffraction and synchrotron µ-computed tomography were used to provide a quantitative assessment of the volume fractions of the identified phase regions. Microhardness measurements showed dependence on the local microstructure. The fracture strength of the material was correlated with the specimen density and agreed with the reported values in the literature. High-temperature exposure at 750 °C for up to 200h did not degrade the strength for the specimens with three phenolic-binder infiltrations; however, the strengths degraded for ones with five phenolic-binder infiltrations. The associated fracture toughnesses of the specimens were ~3.4 MPam 1/2 at room temperature and 750°C, and the thermal conductivities varied from >150 W/mK at room temperature to ~45 W/mK at 750°C. Hence, this study validated the use of the binder-jetting printed SiC ceramic materials for high-temperature heat exchanges. Lastly, we also present in this work the first successful fabrication of a binder-jetting printed one-piece dense SiC ceramic heat exchanger body with unblocked channels that can be used for the flow of heat transfer fluids.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ultimate Strength of Metals

We present a theoretical model that predicts the peak strength of polycrystalline metals based on the activation energy (or stress) required to cause deformation via amorphization. Building on extensive earlier work, this model is based purely on materials properties, requires no adjustable parameters, and is shown to accurately predict the strength of four exemplar metals (fcc, bcc, and hcp, and an alloy). This framework reveals new routes for design of more complex high-strength materials systems, such as compositionally complex alloys, multiphase systems, nonmetals, and composite structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Scale Invariance of Hot Spot Formation in TATB High Explosives

Shock-induced detonation of insensitive high explosives based on 1,3,5-triamino-2,4,6-trinitrobenzene starts with formation of hot spots at microstructural defects but has eluded atomistic modeling treatment at micron length scales. To this end, we performed multimicron scale all-atom molecular dynamics (MD) simulations of hot spots that form during the collapse of cylindrical pores with diameters between 10 and 300 nm. Our MD simulations show that hot spots formed at pores larger than 20 nm exhibit temperature fields with scale-invariant features for sizes up to at least 300 nm. Through a continuum-based grain-scale modeling framework, we span and extend beyond the size scales currently accessible to MD and find that hot spot scale invariance is a general feature that arises when the mechanical strength is insensitive to strain rate. Finally, our results demonstrate the applicability of all-atom MD to simulate the complicated dynamical evolution of micron-sized systems and bolster confidence in insights from MD simulations of materials that exhibit strength with negligible rate dependence over the relevant intervals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of crystallographic orientation on intragranular void growth in polycrystalline FCC materials

In this study, we study the effect of crystallographic orientation and applied triaxiality on the growth of intragranular voids. Two 3D full-field micromechanics methods are used, the dilatational visco-plastic fast-Fourier transform (DVP-FFT) and the crystal plasticity Finite Elements (CP-FE), both of which incorporate a combination of crystalline plasticity and dilatational plasticity. We demonstrate with several select cases that predictions of void growth from both formulations agree qualitatively. With the more computationally efficient DVP-FFT, additional effects of polycrystalline microstructure and the influence of nearest neighborhood are investigated. Crystals bearing a single intracrystalline void are studied in three types of 3D microstructural environments: isolated single crystals, individual equal-sized grains within a regular polycrystal, and individual variable sized grains within a polycrystal with grains and voids randomly located. We show that loading type plays a significant role. In strain-rate controlled conditions, voids in the hardest [111]-crystals grow the fastest in time, whereas in stress-controlled conditions, voids in the softest [100]-crystal grow the fastest in time. The analysis reveals that on average void growth is slower for the same starting orientation in the polycrystal than in the single crystal. We find that at the highest triaxiality tested that the correlation between crystal orientation and void growth rate in the polycrystal strengthens, drawing closer to that seen in the isolated single crystals. These results and model can help guide the microstructural design of polycrystalline materials with high strength and damage-tolerance in high-rate deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Toughening Brittle Bio‐P3HB with Synthetic P3HB of Engineered Stereomicrostructures

Abstract Poly(3‐hydroxybutyrate) (P3HB), a biologically produced, biodegradable natural polyester, exhibits excellent thermal and barrier properties but suffers from mechanical brittleness, largely limiting its applications. Here we report a mono‐material product design strategy to toughen stereoperfect, brittle bio or synthetic P3HB by blending it with stereomicrostructurally engineered P3HB. Through tacticity ([ mm ] from 0 to 100 %) and molecular weight ( M n to 788 kDa) tuning, high‐performance synthetic P3HB materials with tensile strength to ≈30 MPa, fracture strain to ≈800 %, and toughness to 126 MJ m −3 (>110× tougher than bio‐P3HB) have been produced. Physical blending of the brittle P3HB with such P3HB in 10 to 90 wt % dramatically enhances its ductility from ≈5 % to 95–450 % and optical clarity from 19 % to 85 % visible light transmittance while maintaining desirably high elastic modulus (>1 GPa), tensile strength (>35 MPa), and melting temperature (160–170 °C). This P3HB‐toughening‐P3HB methodology departs from the traditional approach of incorporating chemically distinct components to toughen P3HB, which hinders chemical or mechanical recycling, highlighting the potential of the mono‐material product design solely based on biodegradable P3HB to deliver P3HB materials with diverse performance properties.

Zhang, Zhen↗