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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Intermetallic nanoparticles are promising catalysts in hydrogenation and fuel cell technologies. Much is known about the ability of intermetallic nanoparticles to selectively reduce nitro vs alkene, alcohol, or halide functional groups; less is known about their selectivity toward aniline vs azo or azoxy condensation products that result from the reduction of a nitro group alone. Because azo(xy)arenes bear promise as dyes, chemical stabilizers, and building blocks to functional materials but can be difficult to isolate, developing high surface area nanoparticle catalysts that display azo(xy) selectivity is desirable. To address this question, we studied a family of nanocrystalline group 10 metal (Pd, Pt)- and group 14 metal (Ge, Sn, Pb)-containing intermetallics-Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , Pd 3 Pb, and PtSn-in the catalytic reduction of nitroarenes. In contrast to monometallic Au, Pt, and Pd nanoparticles and "random" PdxSn1 – x nanoalloys, which are selective for aniline, nanoparticles of atomically precise intermetallic Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , and PtSn prefer an indirect condensation pathway and have a high selectivity for the azo(xy) products. The only exception is Pd 3 Pb, the most active among the intermetallic nanoparticles studied here, which is instead selective for aniline. Employing a novel application of molecular dynamics-based on machine learned potentials within a DeePMD framework-to heterogeneous catalysis, we are able to identify key reaction species on the different types of catalysts employed, furthering our understanding of the unique selectivity of these materials. By demonstrating how intermetallic nanoparticles can be as active yet more selective than other more traditional catalysts, this work provides new physical insights and opens new opportunities in the use of these materials in other important chemical transformations and applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2023

Probing Nanocrystal Optical Properties in the Limit of Single Nanocrystals

The Bawendi lab has been at the forefront of the synthesis and characterization of the fundamental photo-physics of quantum confined systems for the past three decades. During this DoE project, our progress has been enabled by both our expertise in single nanocrystal (NC) photoluminescence (PL) spectroscopy as well as our extensive knowledge of the synthetic optimization of various NC semiconductors - all of this forming an efficient feedback loop between characterization and synthesis. Our research in this project has focused on the development and application of novel single emitter optical tools for the characterization of colloidal quantum dots (CQDs) which are semiconducting NCs with size-tunable quantum confined electronic structure. CQDs have had a significant commercial impact as display technologies. They are also commercially available as fluorescent biological markers. CQDs are being investigated as a potential material for photovoltaic or optical sensing applications and as emerging optical quantum materials. We have focused on the fundamental understanding of the optical properties of NCs through the development of novel spectroscopic methods at the single emitter level, including understanding how synthetic and environmental heterogeneities influence NC properties. A major advance in our research has been the development of novel photon resolved correlation spectroscopies that allow us to extract the physics of photo-excited single NCs with unprecedented time and spectral resolution from the photon stream of individual NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

From acetone fractionation to lignin-based phenolic and polyurethane resins

In this paper, acetone fractionation was used to increase the homogeneity of softwood kraft lignin for product development. The acetone soluble and insoluble fractions were analyzed in terms of elemental composition, chemical structure, and thermal properties. The phenolic hydroxyl content of the acetone soluble fraction increased by 24%. The acetone soluble fraction of kraft lignin was used to fully substitute phenol in phenolic resin formulation. The resulting formulated lignin-formaldehyde adhesive showed higher adhesion strength (4.3 MPa) compared to a lab formulated phenol-formaldehyde resin (3.4 MPa) with 100% wood failure after lap shear test. On the other hand, the acetone insoluble lignin fraction, which had 23% higher aliphatic hydroxyl content compared to the starting lignin, was used to entirely replace petroleum-based polyol in polyurethane resin formulation. Then a bio-derived solvent (dihydrolevoglucosenone, Cyrene) was used to dissolve the acetone insoluble lignin fraction to synthesize lignin-based polyurethane resin. Tailoring the structure of kraft lignin through solvent fractionation resulted in two fractions, which were used to replace 100% of two major petrochemicals (phenol and polyol) in the formulations of phenolic and PU resins, that would otherwise be challenging due to the heterogeneity of softwood kraft lignin.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct Laser Writing of Multimetal Bifunctional Catalysts for Overall Water Splitting

Water electrolysis is of interest as a sustainable way to produce clean hydrogen and oxygen fuel and help mitigate the rising problems of climate change while meeting global energy demands. High-efficiency, stable, and earth-abundant bifunctional catalysts are needed to enable more effective electrochemical cells for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Here, we investigate the synthesis, composition, performance, and mechanism of multimetal catalysts serving dual functionality in both OER and HER of water electrolysis. Through a laser synthesis method, we synthesized heterogeneous catalysts of nanocrystalline multimetallic alloy pockets embedded within an amorphous oxide matrix. We evaluated the performance and composition of a range of mixed transition-metal oxide materials for both OER and HER, ultimately synthesizing a Cr 0.01 Fe 0.27 Co 0.34 Ni 0.38 O x /C y catalyst that has a stable, high-rate, and competitive overall water splitting performance of 1.76 V at 100 mA cm –2 in an alkaline medium. Using density functional theory to gain insight as the active site and mechanism, we propose that the inclusion of a minor amount of Cr increases the degeneracy of energetic states that lowers the cost of forming the O 2 p–d bond and H 1 s–d bond due to the hybridization of s, p, and d orbitals from Cr. Using a two-electrode water electrolysis cell with a constant potential of 1.636 V to mimic the setup for fuel production, we found the catalyst to be stable at 14–15 mA cm –2 for 40 h. Furthermore, this laser synthesis method allowing for facile and rapid synthesis of complex multimetal systems demonstrates how doping a Fe, Co, and Ni heterogeneous amorphous/nanocrystalline structure with small amounts of Cr is important for bifunctional catalytic behavior, particularly for increasing HER functionality in advancing our understanding for future electrocatalytic design.

08 HYDROGEN↗
DOE OSTI2020

A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys

Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Textured Electrodes: Manipulating Built-In Crystallographic Heterogeneity of Metal Electrodes via Severe Plastic Deformation

Control of crystallography of metal electrodeposit films has recently emerged as a key to achieving long operating lifetimes in next-generation batteries. It is reported that the large crystallographic heterogeneity, e.g., broad orientational distribution, that appears characteristic of commercial metal foils, results in rough morphology upon plating/stripping. On this basis, an accumulative roll bonding (ARB) methodology—a severe plastic deformation process—is developed. Zn metal is used as a first example to interrogate the concept. It is demonstrated that the ARB process is highly effective in achieving uniform crystallographic control on macroscopic materials. After the ARB process, the Zn grains exhibit a strong (002) texture (i.e., [002] Zn //ND). The texture transitions from a classical bipolar pattern to a nonclassical unipolar pattern under large nominal strain eliminate the orientational heterogeneity of the foil. The strongly (002)-textured Zn remarkably improves the plating/stripping performance by nearly two orders of magnitude under practical conditions. The performance improvements are readily scaled to achieve pouch-type full batteries that deliver exceptional reversibility. The ARB process can, in principle, be applied to any metal chemistry to achieve similar crystallographic uniformity, provided the appropriate temperature and accumulated strains are employed. Here this concept is evaluated using commercial Li and Na foils, which, unlike Zn (HCP), are BCC crystals. The simple process for creating strong textures in both hexagonal and cubic metals and illustrating the critical role such built-in crystallography plays underscores opportunities for developing highly reversible thin metal anodes (e.g., hexagonal Zn, Mg, and cubic Li, Na, Ca, Al).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2022

Editorial: Lignocellulose valorization: Fractionation, conversion and applications

Lignocellulosic biomass is the most abundant form of renewable feedstock on the Earth. Chemicals, energies, and materials derived from lignocellulosic biomass are renewable and sustainable, and have the potential to replace fossil feedstocks. Biorefinery, a process to fractionate lignocellulose into the three major components, is considered the most pivotal step for bioenergy, biomaterials and biochemicals. However, due to the complex hierarchy and chemical structures, only limited lignocellulose is valorized into value-added products, and most of them are burnt or just discarded. Therefore, there is a need to investigate the heterogeneous structure of lignocellulose, improve its efficient fractionation into cellulose, hemicelluloses and lignin, and develop the technology that can convert the lignocellulose into high-value chemical products and high-performance functional materials in a sustainable and promising configuration. In short, the ultimate goal of biomass utilization is that the production of biofuels and biomaterials in industrial applications must be cost-, and performance-competitive with petroleum-derived equivalents.

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

Promotion of CaCO 3 Nucleation by Carboxyl- and Amine-Terminated Peptoid Nanotubes

The use of surfaces to promote heterogeneous nucleation of CaCO 3 has been extensively studied to better understand natural processes of biomineralization, to develop bioinspired approaches for synthesizing composite materials with diverse functionalities, and as a route toward accelerating CO 2 mineralization. Peptoids have emerged as a material of particular interest, because they offer tunable side chain chemistry while maintaining a constant long-range self-assembled architecture. Here, in this work, we investigate the ability of films of self-assembled -NH 2 and −COOH terminated peptoid nanotubes to promote the nucleation of CaCO 3 . Using a combination of atomic force, scanning electron, and optical microscopy, we find that interwoven films of nanotubes formed from peptoids with -NH 2 side chains promote the preferential formation of vaterite with the (001) plane parallel to the film and exhibit a lower interfacial energy than that reported for inorganic surfaces. In contrast, the film of −COOH-functionalized peptoid nanotubes is a much stronger promoter, giving a particle density 5 orders of magnitude larger than for the -NH 2 terminated peptoid nanotubes and enabling complete coverage of individual peptoid tubes with CaCO 3 nanoparticles that results in a composite material with a 50-fold increase in Young’s modulus. Thus, interwoven films of peptoid nanotubes provide a useful platform for investigating the impact of specific chemical groups on nucleation and offer potential application to accelerated mineralization of CO 2 for removal from the environment or modulation of mineralogy and properties of carbonate-based building materials.

carbon nanotubes↗
NASA NTRS1987

Experimental constraints on heating and cooling rates of refractory inclusions in the early solar system

The refractory inclusions in carbonaceous chondrites were the subject of considerable interest since their discovery. These inclusions contain minerals that are predicted to be some of the earliest condensates from the solar nebula, and contain a plethora of isotopic anomalies of unknown origin. Of particular interest are those coarse-grained inclusions that contain refractory metal particles (Fe, Ni, Pt, Ru, Os Ir). Experimental studies of these inclusions in terrestrial laboratories are, however, complicated because the dense particles tend to settle out of a molten or partially molten silicate material. Heating experiments in the Space Station technology and microgravity in order to observe the effects of metal nuggets (which may act as heterogeneous nucleation sites) on nucleation rates in silicate systems and to measure simultaneously the relative volatilization rate of siderophile and lithophile species. Neither experiment is possible in the terrestrial environment.

Boynton, W. V.↗
NASA NTRS1991

The surface and through crack problems in layered orthotropic plates

An analytical method is developed for a relatively accurate calculation of Stress Intensity Factors in a laminated orthotropic plate containing a through or part-through crack. The laminated plate is assumed to be under bending or membrane loading and the mode 1 problem is considered. First three transverse shear deformation plate theories (Mindlin's displacement based first-order theory, Reissner's stress-based first-order theory, and a simple-higher order theory due to Reddy) are reviewed and examined for homogeneous, laminated and heterogeneous orthotropic plates. Based on a general linear laminated plate theory, a method by which the stress intensity factors can be obtained in orthotropic laminated and heterogeneous plates with a through crack is developed. Examples are given for both symmetrically and unsymmetrically laminated plates and the effects of various material properties on the stress intensity factors are studied. In order to implement the line-spring model which is used later to study the surface crack problem, the corresponding plane elasticity problem of a two-bonded orthotropic plated containing a crack perpendicular to the interface is also considered. Three different crack profiles: an internal crack, an edge crack, and a crack terminating at the interface are considered. The effect of the different material combinations, geometries, and material orthotropy on the stress intensity factors and on the power of stress singularity for a crack terminating at the interface is fully examined. The Line Spring model of Rice and Levy is used for the part-through crack problem. The surface crack is assumed to lie in one of the two-layered laminated orthotropic plates due to the limitation of the available plane strain results. All problems considered are of the mixed boundary value type and are reduced to Cauchy type of singular integral equations which are then solved numerically.

Erdogan, Fazil↗
DOE OSTI2025

Multiphysics Meshfree Degradation Modeling of Energy Storage Materials with Kernel Enrichment

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. Reference: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

25 ENERGY STORAGE↗
DOE OSTI2026

Kernel Enriched Meshfree Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Laboratory of the Rockies (NLR), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. References: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling: Part II. On In-Plane Heterogeneities and the Mechanisms that Regulate Them

Li-ion batteries performance and degradation are typically modeled at the macroscopic scale, that is neglecting in-plane heterogeneities that can arise from non-uniform electrode microstructures. Herein, a microstructure scale electrochemical model is used to quantify the impact of microstructure heterogeneity on cell performance during fast charging. The model predicts the electrolyte and solid concentration in-plane standard deviation can reach, respectively, ≈200 mol·m −3 and 6–7 kmol·m −3 locally. Further, the intercalation current density in-plane relative standard deviation can reach extremely high values, around 100% in the cathode and well above 100% in the anode graphite. These denote highly non-uniform lithiation rates and material utilization within each slice of the microstructure along the cell thickness. Non-uniform curvatures, at the particle scale (surface roughness) and between particles (size distribution), were found to initiate these in-plane heterogeneities, while an OCP-induced mechanism subsequently regulates them. The present model provides new insights into small length scale heterogeneity impact on battery performance not available with standard macro-scale/P2D modeling.

25 ENERGY STORAGE↗
DOE OSTI2024

In situ electric-field control of ferromagnetic resonance in the low-loss organic-based ferrimagnet V[TCNE] x ∼2

We demonstrate indirect electric-field control of ferromagnetic resonance (FMR) in devices that integrate the low-loss, molecule-based, room-temperature ferrimagnet vanadium tetracyanoethylene (V[TCNE]x∼2) mechanically coupled to PMN-PT piezoelectric transducers. Upon straining the V[TCNE]x films, the FMR frequency is tuned by more than 6 times the resonant linewidth with no change in Gilbert damping for samples with α = 6.5 × 10−5. We show this tuning effect is due to a strain-dependent magnetic anisotropy in the films and find the magnetoelastic coefficient |λs| ∼ (1–4.4) ppm, backed by theoretical predictions from density-functional theory calculations and magnetoelastic theory. Noting the rapidly expanding application space for strain-tuned FMR, we define a new metric for magnetostrictive materials, magnetostrictive agility, given by the ratio of the magnetoelastic coefficient to the FMR linewidth. This agility allows for a direct comparison between magnetostrictive materials in terms of their comparative efficacy for magnetoelectric applications requiring ultra-low loss magnetic resonance modulated by strain. With this metric, we show V[TCNE]x is competitive with other magnetostrictive materials, including YIG and Terfenol-D. This combination of ultra-narrow linewidth and magnetostriction, in a system that can be directly integrated into functional devices without requiring heterogeneous integration in a thin film geometry, promises unprecedented functionality for electric-field tuned microwave devices ranging from low-power, compact filters and circulators to emerging applications in quantum information science and technology.

Materials Science↗
DOE OSTI2021

Aging and Lifetimes Milestone 7777, G.C. #4: Assess the ability of MRI to identify chemical and mechanical heterogeneity in polymers at various length scales

Polysiloxane foams utilized for stockpile applications experience changes over time induced by exposure to thermal, radiative, and mechanical stresses. The interplay of these stresses can lead to chemical and/or physical changes in the polymer, such as compression set and material degradation over time. Understanding the mechanisms of material aging and identifying material degradation prior to failure are critical in maintaining the integrity of the nuclear stockpile. Our prior work using solid-state NMR spectroscopy has shown that we can observe signatures of thermal, radiative, and mechanical aging of silicone elastomers using differences in NMR parameters such as relaxation (T1 and T2). However, most of our measurements have been on bulk samples and have only provided an average aging behavior of each sample. Here, we have used magnetic resonance imaging (MRI) to provide relaxation-weighted images that are a metric of structural heterogeneity (cross-link density) in polymers across multiple length scales. In addition, the larger sample size that can be accommodated in animal MRI scanners has the potential to make the technique adaptable to detecting heterogeneities whole parts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗