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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
DOE OSTI2021

Thermal History of Matrix Forsterite Grains from Murchison Based on High-resolution Tomography

Protoplanetary disks are dust- and gas-rich structures surrounding protostars. Depending on the distance from the protostar, this dust is thermally processed to different degrees and accreted to form bodies of varying chemical compositions. The primordial accretion processes occurring in the early protoplanetary disk such as chondrule formation and metal segregation are not well understood. One way to constrain them is to study the morphology and composition of forsteritic grains from the matrix of carbonaceous chondrites. Here, we present high-resolution ptychographic X-ray nanotomography and multimodal chemical microtomography (X-ray diffraction and X-ray fluorescence) to reveal the early history of forsteritic grains extracted from the matrix of the Murchison CM2.5 chondrite. Here,. the 3D electron density maps revealed, at unprecedented resolution (64 nm), spherical inclusions containing Fe–Ni, very little silica-rich glass and void caps (i.e., volumes where the electron density is consistent with conditions close to vacuum) trapped in forsterite. The presence of the voids along with the overall composition, petrological textures, and shrinkage calculations is consistent with the grains experiencing one or more heating events with peak temperatures close to the melting point of forsterite (~2100 K), and subsequently cooled and contracted, in agreement with chondrule-forming conditions.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
DOE OSTI2023

A study of adhesive bonding in metal–metal, metal–CFRP, and CFRP–CFRP material combinations under shear deformation: Fracture morphologies and damage mechanisms

Safe design of adhesive joining in multi-materials in engineered structures requires the accumulation of numerous experimental data on the failure behavior of various adhesively-bonded material combinations under different loading conditions. The deep understanding of mechanical performance, fracturing morphologies, and main damage mechanisms is also quintessential for accelerating the development of proper physics-based and multi-scale models for assisting the design. Towards this goal, this work presents a comprehensive characterization of the failure behavior of adhesively-bonded metal–metal, metal–CFRP, and CFRP–CFRP material combinations under global shear deformation via single lap shear testing. Thanks to a synergistic combination of measurement methods by using Digital Imaging Correlation (DIC) and 3D optical profilometry, adhesive features on the adherend after failure were quantified and the main progressive damage mechanisms were identified. The characterization performed in this work provides quantitative data that contributes to a better understanding of shear failure in adhesive bonding across different bi-material combinations. Finally, the obtained results have practical implications, including the potential to enhance adhesive bonding design, identify failure causes in adhesive joints, and develop or validate computational models capable of capturing the observed behavior in various adhesively-bonded materials under global shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Selective Laser Sintering of Polyolefins

Polyolefins are some of the most produced and used polymers around the globe. They are semi-crystalline thermoplastic polymers known for their low production cost while still exhibiting good mechanical properties and chemical resistance. These polymers are often used in a variety of manufacturing processes, including 3D printing or additive manufacturing (AM). However, due to their semi-crystalline nature, there are often issues with using polyolefins in AM processes (for example, shrinkage leading to dimensional instabilities). One example of an AM process for which polyolefins are of interest is selective laser sintering (SLS), where printability depends on the powdered material’s morphology, flowability, and size. Due to the inherent properties of polyolefins, there are a variety of issues surrounding their use in the SLS process. This paper aims to review the basics of SLS as well the current state of SLS printing of a few polyolefins and what challenges that would need to be overcome to print with these polyolefins.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (Metric)

Geological solids are characterized by their mineral structure, elemental composition, and morphology. The Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC) is an instrument payload that will quantify all three. METRIC comprises a suite of instruments in a lander to perform X-ray diffraction (XRD) for mineral structure, X-ray fluorescence (XRF) for elemental composition, and X-ray Micro Computed Tomography (XCT) for 3D internal micromorphology. The instruments are accompanied by an Infrared Spectrometer (IRS) provide geologic context. A Honeybee Robotics pneumatic sampling and transfer system [e.g., 1] will be positioned under the lander to deliver sieved regolith to the X-ray instruments for analysis. This payload is envisioned for deployment to the lunar surface on a Commercial Lunar Payload Services (CLPS) lander but could be carried on a lander or rover to any solid surface in the solar system.

E B Rampe↗
NASA NTRS2024

Impact of Simulated Microgravity on Mesenchymal Stem Cell Proliferation

Background Long term exposure to microgravity has adverse effects on human body such as bone loss. Due to the vital role of mesenchymal stem cell (MSC) in bone regeneration, studying MSC under microgravity conditions has been of interest during the past decade. However, high cost and limited access to space flight can dampen enthusiasm of researchers. Microgravity simulators provide a way to investigate the effects of microgravity in human physiology. Methods In this study, we compared MSC cultures under simulated microgravity using random positioning machine (RPM) and Gravite 3D simulators. Two culture durations, 1 week and 2 weeks, were selected for MSC cultures without media change. To evaluate impact of simulated microgravity on cell proliferation, cell counting was performed using trypan blue exclusion assay. Additionally, RNA samples and conditioned media were collected for further analysis. Results After 1 week and 2 weeks, no cell morphology change was observed from RPM and Gravite cultures compared to control. Notably, distinct lower cell confluence at the edge of the flasks on the RPM was observed, which could be caused by fluid sheer forces. MSCs cultured on both microgravity simulators showed decreased cell proliferation after 1-week culture. After 2-week culture, cells grown on the RPM showed no significant difference in cell proliferation compared with control while cells cultured on the Gravite bioreactor showed significantly enhanced MSC proliferation. Conclusion Type of microgravity simulator and duration of culture significantly affect MSC proliferation. Further investigation into how simulated microgravity influences MSC gene expression and cytokine secretion related to bone loss is currently on going in our lab.

Cuiping Zhang↗
DOE OSTI2019

Origin of High Activity and Durability of Twisty Nanowire Alloy Catalysts under Oxygen Reduction and Fuel Cell Operating Conditions

The ability to control the surface composition and morphology of alloy catalysts is critical for achieving high activity and durability of catalysts for oxygen reduction reaction (ORR) and fuel cells. Here, this report describes an efficient surfactant-free synthesis route for producing a twisty nanowire (TNW) shaped platinum–iron (PtFe) alloy catalyst (denoted as PtFe TNWs) with controllable bimetallic compositions. PtFe TNWs with an optimal initial composition of ~24% Pt are shown to exhibit the highest mass activity (3.4 A/mg Pt , ~20 times higher than that of commercial Pt catalyst) and the highest durability (<2% loss of activity after 40000 cycles and <30% loss after 120000 cycles) among all PtFe-based nanocatalysts under ORR or fuel cell operating conditions reported so far. Using ex situ and in situ synchrotron X-ray diffraction coupled with atomic pair distribution function (PDF) analysis and 3D modeling, the PtFe TNWs are shown to exhibit mixed face-centered cubic (fcc)-body-centered cubic (bcc) alloy structure and a significant lattice strain. A striking finding is that the activity strongly depends on the composition of the as-synthesized catalysts and this dependence remains unchanged despite the evolution of the composition of the different catalysts and their lattice constants under ORR or fuel cell operating conditions. Notably, dealloying under fuel cell operating condition starts at phase-segregated domain sites leading to a final fcc alloy structure with subtle differences in surface morphology. Due to a subsequent realloying and the morphology of TNWs, the surface lattice strain observed with the as-synthesized catalysts is largely preserved. This strain and the particular facets exhibited by the TNWs are believed to be responsible for the observed activity and durability enhancements. These findings provide new insights into the correlation between the structure, activity, and durability of nanoalloy catalysts and are expected to energize the ongoing effort to develop highly active and durable low-Pt-content nanowire catalysts by controlling their alloy structure and morphology.

25 ENERGY STORAGE↗
DOE OSTI2023

Influence of Potassium Metal‐Support Interactions on Dendrite Growth

Abstract Combined synchrotron X‐ray nanotomography imaging, cryogenic electron microscopy (cryo‐EM) and modeling elucidate how potassium (K) metal‐support energetics influence electrodeposit microstructure. Three model supports are employed: O‐functionalized carbon cloth (potassiophilic, fully‐wetted), non‐functionalized cloth and Cu foil (potassiophobic, nonwetted). Nanotomography and focused ion beam (cryo‐FIB) cross‐sections yield complementary three‐dimensional (3D) maps of cycled electrodeposits. Electrodeposit on potassiophobic support is a triphasic sponge, with fibrous dendrites covered by solid electrolyte interphase (SEI) and interspersed with nanopores (sub‐10 nm to 100 nm scale). Lage cracks and voids are also a key feature. On potassiophilic support, the deposit is dense and pore‐free, with uniform surface and SEI morphology. Mesoscale modeling captures the critical role of substrate‐metal interaction on K metal film nucleation and growth, as well as the associated stress state.

Liu, Pengcheng↗
DOE OSTI2023

Influence of Potassium Metal-Support Interactions on Dendrite Growth

We report combined synchrotron X-ray nanotomography imaging, cryogenic electron microscopy (cryo-EM) and modeling elucidate how potassium (K) metal-support energetics influence electrodeposit microstructure. Three model supports are employed: O-functionalized carbon cloth (potassiophilic, fully-wetted), non-functionalized cloth and Cu foil (potassiophobic, nonwetted). Nanotomography and focused ion beam (cryo-FIB) cross-sections yield complementary three-dimensional (3D) maps of cycled electrodeposits. Electrodeposit on potassiophobic support is a triphasic sponge, with fibrous dendrites covered by solid electrolyte interphase (SEI) and interspersed with nanopores (sub-10 nm to 100 nm scale). Lage cracks and voids are also a key feature. On potassiophilic support, the deposit is dense and pore-free, with uniform surface and SEI morphology. Mesoscale modeling captures the critical role of substrate-metal interaction on K metal film nucleation and growth, as well as the associated stress state.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Determinants of synapse diversity revealed by super-resolution quantal transmission and active zone imaging

Neural circuit function depends on the pattern of synaptic connections between neurons and the strength of those connections. Synaptic strength is determined by both postsynaptic sensitivity to neurotransmitter and the presynaptic probability of action potential evoked transmitter release (P r ). Whereas morphology and neurotransmitter receptor number indicate postsynaptic sensitivity, presynaptic indicators and the mechanism that sets P r remain to be defined. To address this, we developed QuaSOR, a super-resolution method for determining P r from quantal synaptic transmission imaging at hundreds of glutamatergic synapses at a time. We mapped the P r onto super-resolution 3D molecular reconstructions of the presynaptic active zones (AZs) of the same synapses at the Drosophila larval neuromuscular junction (NMJ). We find that P r varies greatly between synapses made by a single axon, quantify the contribution of key AZ proteins to P r diversity and find that one of these, Complexin, suppresses spontaneous and evoked transmission differentially, thereby generating a spatial and quantitative mismatch between release modes. Transmission is thus regulated by the balance and nanoscale distribution of release-enhancing and suppressing presynaptic proteins to generate high signal-to-noise evoked transmission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An intensity-based post-processing tool for 3D instance segmentation of organelles in soft X-ray tomograms

Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β -cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β -cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β -cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

The extended atmosphere and circumstellar environment of the cool evolved star VX Sagittarii as seen by MATISSE

Context. VX Sgr is a cool, evolved, and luminous red star whose stellar parameters are difficult to determine, which affects its classification. Aims. We aim to spatially resolve the photospheric extent as well as the circumstellar environment. Methods. We used interferometric observations obtained with the MATISSE instrument in the L (3–4 μm), M (4.5–5 μm), and N (8–13 μm) bands. We reconstructed monochromatic images using the MIRA software. We used 3D radiation-hydrodynamics simulations carried out with CO5BOLD and a uniform disc model to estimate the apparent diameter and interpret the stellar surface structures. Moreover, we employed the radiative transfer codes OPTIM3D and RADMC3D to compute the spectral energy distribution for the L, M, and N bands, respectively. Results. MATISSE observations unveil, for the first time, the morphology of VX Sgr across the L, M, and N bands. The reconstructed images show a complex morphology with brighter areas whose characteristics depend on the wavelength probed. We measured the angular diameter as a function of the wavelength and showed that the photospheric extent in the L and M bands depends on the opacity through the atmosphere. In addition to this, we also concluded that the observed photospheric inhomogeneities can be interpreted as convection-related surface structures. The comparison in the N band yielded a qualitative agreement between the N-band spectrum and simple dust radiative transfer simulations. However, it is not possible to firmly conclude on the interpretation of the current data because of the difficulty in constraing the model parameters using the limited accuracy of our absolute flux calibration. Conclusions. MATISSE observations and the derived reconstructed images unveil the appearance of VX Sgr’s stellar surface and circumstellar environment across a very large spectral domain for the first time.

stars: atmospheres↗
NASA NTRSDate not supplied

Multi-Scale Thermo-Mechanical Modeling of Porous 3D Woven TPS Materials

This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.

Multi-Scale↗
NASA NTRSDate not supplied

Multi-Scale Thermo-Mechanical Modeling of Porous 3D Woven TPS Materials

This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA [1,2] software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.

Multi-Scale↗
DOE OSTI2023

High-Throughput Automated Exploration of Phase Growth Behaviors in Quasi-2D Formamidinium Metal Halide Perovskites

Quasi-2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi-2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin-film fabrication process. In this work, a high-throughput automated synthesis-characterization-analysis workflow is implemented to accelerate material exploration in formamidinium (FA)-based quasi-2D MHP compositional space, revealing the early-stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis-characterization platform, it is observed that the prominent n = 2 2D phase restricts the growth kinetics of 3D-like phases—α-FAPbI 3 MHPs with spacer-coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI 2 crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer-solvent interaction determining the quasi-2D MHP morphologies and microstructures are demonstrated. The high-throughput study provides comprehensive insights into the fundamental principles in quasi-2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications.

2D perovskites↗
DOE OSTI2023

Design and additive manufacturing of optimized electrodes for energy storage applications

Supercapacitors exhibit fast charging/discharging ability and have attracted considerable attention within the automotive, aerospace, and telecommunication industries. Porous carbons, prized for their high electrical conductivity and high surface area, have been attractive candidates for supercapacitor electrodes. Moving to thick electrodes is one strategy to further increase energy density due to a higher volume fraction of active material. However, thick electrodes suffer from sluggish charged species transport, which is why thin electrodes are currently favored. In this work, we investigate the use of computational optimization and additive manufacturing to design and fabricate thick porous electrodes with improved performance. Electrode performance was maximized by designing their morphologies via topology optimization and printing by projection micro stereolithography (PμSL) using commercial resin (PR48). The PR48 resin was then pyrolyzed (PR48-P) to create the final conductive electrode. The optimized PR48-P electrodes exhibited 99% improvement in capacitance compared to control electrodes printed with cubic lattice morphologies. To further improve performance, we formulated a resin combining graphene oxide (GO) and trimethylolpropane triacrylate (TMPTA). Electrodes printed with 3 wt% GO in TMPTA exhibited improved capacitance retention after pyrolysis compared to the PR48-P electrodes. Finally, this work demonstrates the benefits of using topology optimization to design electrodes and material development to improve functional properties of 3D printable electrodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks

Abstract Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.

25 ENERGY STORAGE↗