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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Review of Scalable Hexagonal Boron Nitride ( h -BN) Synthesis for Present and Future Applications

Hexagonal boron nitride (h-BN) is a layered inorganic synthetic crystal exhibiting high temperature stability and high thermal conductivity. As a ceramic material it has been widely used for thermal management, heat shielding, lubrication, and as a filler material for structural composites. Recent scientific advances in isolating atomically thin monolayers from layered van der Waals crystals to study their unique properties has propelled research interest in mono/few layered h-BN as a wide bandgap insulating support for nanoscale electronics, tunnel barriers, communications, neutron detectors, optics, sensing, novel separations, quantum emission from defects, among others. Realizing these futuristic applications hinges on scalable cost-effective high-quality h-BN synthesis. Here, in this work, the authors review scalable approaches of high-quality mono/multilayer h-BN synthesis, discuss the challenges and opportunities for each method, and contextualize their relevance to emerging applications. Maintaining a stoichiometric balance B:N = 1 as the atoms incorporate into the growing layered crystal and maintaining stacking order between layers during multi-layer synthesis emerge as some of the main challenges for h-BN synthesis and the development of processes to address these aspects can inform and guide the synthesis of other layered materials with more than one constituent element. Finally, the authors contextualize h-BN synthesis efforts along with quality requirements for emerging applications via a technological roadmap.

2D materials↗
DOE OSTI2024

Chemistry in Retrieved Ryugu Asteroid Samples Revealed by Non-Invasive X-ray Microanalyses: Pink-Beam Fluorescence CT and Tender-Energy Absorption Spectroscopy

The Hayabusa2 space mission recently retrieved 5.4 g of material from asteroid Ryugu, providing the first direct access to pristine material from a carbonaceous asteroid. This study employs a novel combination of non-invasive synchrotron X-ray techniques to examine microscale chemistry (elemental distributions and element-specific chemical speciation and local structure) inside Ryugu grains without physically cutting the samples. Manganese primarily occurs in carbonate: Mn-bearing dolomite with minor earlier ankerite. Iron sulfides present as large single grains and as smaller particles in the finer-grained matrix are both predominantly pyrrhotite. At the 5 μm scale, Fe sulfides do not show the mineralogical heterogeneity seen in many carbonaceous meteorites but exhibit some heterogeneous localized oxidation. Iron is present often as intergrowths of oxide and sulfide, indicating incomplete replacement. Trace selenium substitutes for S in pyrrhotite. Copper is present as Fe-poor Cu sulfide. These results demonstrate multiple episodes of fluid alteration on the parent body, including partial oxidation, and help constrain the sequence or evolution of fluids and processes that resulted in the current grain-scale mineralogical composition of Ryugu materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dataset of tensile properties for sub-sized specimens of nuclear structural materials

Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the “specimen size effect.” Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 55 columns including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carbon Fiber-Based Vitrimer Composites: A Path toward Current Research That Is High-Performing, Useful, and Sustainable

In the polymeric material industry, thermosets and related composites have played a substantial role in the production of rubber and plastics. One important subset of these is thermoset composites with carbon reinforcement. The incorporation of carbon fillers and fibers gives polymeric materials improved electrical and mechanical properties, among other benefits. However, the covalently crosslinked network of thermosets presents significant challenges for recycling and reprocessing because of its intractable nature. The introduction of vitrimer materials opens a new avenue to produce biodegradable and recyclable thermosets. Carbon-reinforced vitrimer composites are pursued for high-performance, long-lasting materials with attractive physical properties, the ability to be recycled and processed, and other features that respond uniquely to stimuli. The development of carbon-reinforced vitrimer composites over the last few years is summarized in this article. First, an overview of vitrimers and the methods used to prepare carbon fiber-reinforced vitrimer composites is provided. Because of the vitrimer nature of such composites, reprocessing, healing, and recycling are viable ways to greatly extend their service life; these approaches are thoroughly explained and summarized. The conclusion is our prediction for developing carbon-based vitrimer composites.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probabilistic Failure Criterion of SiC/SiC Composites Under Multiaxial Loading

Owing to its excellent mechanical properties and stability under high temperature and neutron irradiation conditions, SiC/SiC composites have emerged as a promising material for light water reactors (LWRs) in the development of accident-tolerant fuel (ATF) systems. Structural integrity and retention of hermeticity are two crucial requirements for SiC/SiC claddings during normal operations, and both of them are closely related to the proportional limit stress (PLS) of the material. Understanding the behavior of SiC/SiC composites under multiaxial stress states and developing a probabilistic approach for evaluating the structural vulnerability are of paramount importance for reliability-based analysis and design of SiC/SiC composite claddings. So far, there has been very limited effort towards experimental and analytical investigations of probabilistic failure of SiC/SiC claddings. This critical knowledge gap motivates this research. A probabilistic failure criterion for SiC/SiC composites under multi-axial loading is developed, and this criterion is incorporated into reliability analysis of the structural integrity of SiC/SiC fuel cladding. The research consists of two parts: 1) experimental investigation of multiaxial failure behavior of SiC/SiC composites, and 2) theoretical modeling of time-dependent probabilistic failure of SiC/SiC cladding. In the experimental investigation, the PLS is determined through the examination of stress-strain response, the acoustic emission measurement, as well as the X-ray computed tomography. The theoretical framework is derived by combin- ing the finite weakest-link statistical model and the subcritical damage growth model. This theoretical model captures the time-dependent failure mechanism of the material, which has a major consequence for predicting the lifetime distribution of the cladding. Meanwhile, the model also predicts that the failure statistics of the cladding depends strongly on the cladding length. The results of the multiaxial experiments reveal the level of statistical variation of the PLS of SiC/SiC materials under different stress states. The theoretical model provides a robust analytical tool for extrapolation of small-scale laboratory test results to the behavior of full-scale claddings. These findings establish a scientific foundation for the development of reliability-based design of SiC/SiC fuel claddings, which will play an essential role in improving the structural safety and integrity of LWRs.

42 ENGINEERING↗
DOE OSTI2022

Rapid Access to Diverse Multicomponent Hierarchical Nanostructures from Mixed–Graft Block Copolymers

Multicomponent nanostructured materials assembled from molecular building blocks received wide attention due to their precisely integrated multifunctionalities. However, discovery of these materials with desirable composition and morphology was limited by their low synthetic scalability and narrow structural tuning window with given building blocks. Here, we report a scalable and diversity-oriented synthetic approach to hierarchically structured nanomaterials based on a few readily accessible building blocks. Mixed-graft block copolymers containing sequence-defined side chains were prepared through ring-opening metathesis copolymerization of three or four types of macromonomers. Intramolecularly defined interfaces promoted the formation of ordered hierarchical structures with lattice sizes tunable across multiple length scales. Finally, the same set of macromonomers were arranged and combined in different ways, providing access to diverse morphologies in the resultant structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design and Analysis of an Advanced Three-Point Bend Test Approach for Miniature Irradiated Disk Specimens

Candidate tungsten armor materials in a magnetic confinement fusion device must be able to withstand thermal variation that leads to internal stresses caused by the impinging heat load. In addition, the thermomechanical properties of these materials are degraded by irradiation-induced defect accumulation. Fission reactor–based irradiation data are used to predict the fusion neutron damage and property change. This study examines the motivation and design of a custom-designed three-point bend test for neutron-irradiated disk specimens that are 3 mm in diameter to be able to define the flexural strength of advanced tungsten materials, alloys, and composites—and to the extent that embrittlement occurs after neutron irradiation. The theory provided shows a calculation for the flexural deflection and shear deflection due to the small-geometry constraints. A finite element deformation analysis is performed to evaluate the mechanical stress field of disk bend specimens. The stress values above 80% of the maximum stress are concentrated in 2.4 mm of the 3.0-mm length of the centerline across the tungsten disk diameter. A bend test fixture has been designed and fabricated to enable testing of these specimens with precisely engineered tolerance and minimal machine compliance. This fixture will be able to be placed inside a universal testing frame at elevated temperatures for the mechanical property evaluation of future neutron-irradiated disk specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Anisotropic spall failure of additively manufactured 316L stainless steel

The dynamic high strain rate tensile (spall) failure of additively manufactured (AM) materials is an area of great interest but much less understood than material failure under other loading regimes. The research presented here examines the effects of microstructure anisotropy in 316L stainless steel fabricated by laser powder bed fusion (LPBF) on its spall failure and properties. In this work, plate-on-plate impact experiments were performed using an 80-mm gas gun to investigate the spall behavior of AM fabricated sample discs with impact along the perpendicular and parallel orientations relative to build directions. Experiments were also performed on wrought 316L stainless steel as baseline material with uniform equiaxed grain microstructure for comparison with the AM material of same composition. The spall experiments involved free-surface velocity profiles measured using Photon Doppler Velocimetry (PDV) to capture the spall pull-back signals, combined with post-mortem analysis of the microstructure of soft-recovered impacted samples. In the case of AM samples, spall failure initiated preferentially at melt pool boundaries in both impact directions. The spall plane location and spall strength, however, were both heavily influenced by the orientation of the impact direction relative to the build direction in the AM samples. The spall behavior is also observed to be more complex in the samples impacted perpendicular to the build (through-thickness) direction, due in large part to the orientation of the microstructural features such as melt pool boundaries, laser scan tracks, and general grain morphology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Visualization of the strain-induced topological phase transition in a quasi-one-dimensional superconductor TaSe 3

Control of the phase transition from topological to normal insulators can allow for an on/off switching of spin current. While topological phase transitions have been realized by elemental substitution in semiconducting alloys, such an approach requires preparation of materials with various compositions. Thus it is quite far from a feasible device application, which demands a reversible operation. In this work, we use angle-resolved photoemission spectroscopy and spin- and angle-resolved photoemission spectroscopy to visualize the strain-driven band-structure evolution of the quasi-one-dimensional superconductor TaSe 3 . We demonstrate that it undergoes reversible strain-induced topological phase transitions from a strong topological insulator phase with spin-polarized, quasi-one-dimensional topological surface states, to topologically trivial semimetal and band insulating phases. The quasi-one-dimensional superconductor TaSe 3 provides a suitable platform for engineering the topological spintronics, for example as an on/off switch for a spin current that is robust against impurity scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermo-Catalytic Decomposition Comparisons: Carbon Catalyst Structure, Hydrocarbon Feed and Regeneration

Thermo-catalytic decomposition (TCD) activity and stability depend upon the initial carbon catalyst structure. However, further transitions in the carbon structure depend on the carbon material (structure and composition) originating from the TCD process. In this article, reaction data are presented that illustrates the time-dependent TCD activity as TCD-formed carbon contributes and then dominates conversion. A variety of initial carbon catalysts are compared, including sugar char, a conductive carbon black (AkzoNobel Ketjenblack), a rubber-grade carbon black (Cabot R250), and its graphitized analogue as formed and partially oxidized. Regeneration of carbon catalysts by partial oxidation is evaluated using nascent carbon black as a model, coupled with subsequent comparative TCD performance relative to the nascent, non-oxidized carbon black. Activation energies for TCD with nascent and oxidized carbons are evaluated by a leading-edge analysis method applied to TCD. Given the correlation between nanostructure and active sites, two additional carbons, engine soots, are evaluated for regeneration and dependence upon nanostructure. Active sites are quantified by oxygen chemisorption, followed by X-ray photoelectron spectroscopy (XPS). The structure of carbon catalysts is assessed pre- and post-TCD by high-resolution transmission electron microscopy (HRTEM). Last, energy dispersive X-ray analysis mapping (EDS) is carried out for its potential to visualize oxygen chemisorption.

03 NATURAL GAS↗
DOE OSTI2020

Dynamically controlled random lasing with colloidal titanium carbide MXene

Control of lasing properties through tailorable and dynamically tunable materials and reconfigurable compositions can augment the performance of random lasers for a wide range of applications. Here, a colloid of randomly dispersed weakly scattering single-layer titanium carbide (Ti 3 C 2 T x ) MXene flakes embedded within rhodamine 101 gain medium is experimentally shown to provide feedback for random lasing. Additionally, in contrast to previously reported random laser systems where the optical properties of scatterers are static, the relative permittivity of Ti 3 C 2 T x MXene flakes can be varied under optical pumping due to the saturable absorption properties. Numerical simulations indicate that the observed nonlinear response of Ti 3 C 2 T x MXene flakes enables dynamically tunable random lasing. Thus, pumping the Ti 3 C 2 T x MXene flakes with a second optical source decreases the gain threshold required to obtain random lasing. Also, using numerical simulations, it is shown that the control over the intensity of the second pump enables tuning the field distribution of the random lasing modes. Considering the diversity of the MXenes family, the proposed MXene colloidal metamaterial design opens up a new avenue to advanced control of lasing properties for photonic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tough and circular glass fiber composites via a tailored dynamic boronic ester interface

Glass fiber reinforced polymer (GFRP) composites are valued for their strength and cost-effectiveness. However, traditional GFRPs often face challenges for end-of-life recycling due to their non-depolymerizable thermoset matrices, and long-term performance due to inadequate interfacial adhesion, which can lead to fiber–matrix delamination. Here, in this work, we have designed dynamic fiber–matrix interfaces to allow tough and closed-loop recyclable GFRPs by utilizing a vitrimer, derived from upcycled polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (SEBS) with boronic ester (S-Bpin) and amine-based diol crosslinker. The boronic ester groups in S-Bpin form dynamic covalent bonds with the naturally present hydroxyl groups on the unsized GF surface, which eliminates the need for fiber sizing and enables facile closed-loop recyclability of both the fibers and the vitrimer matrix. The resulting strong fiber–matrix interface, depicted by the Raman mapping, leads to a 552% increase in-plane shear toughness (6.2 ± 0.3 MJ m -3 ) and 27% ultimate tensile strength (361 ± 89.2 MPa) compared to those of the conventional epoxy-based matrix (0.95 ± 0.05 MJ m -3 and 264 ± 59.7 MPa, respectively). The network rearrangement through dynamic boronic ester exchange enables fast thermoformability and repairability of micro-cracks at elevated temperatures. Additionally, both the matrix and composite demonstrate strong adhesion to various surfaces including steel and glasses exhibiting ≥6 MPa lap shear strength, which expands their suitability for diverse industrial applications. The readily created dynamic interface between boronic ester functionalized vitrimer and neat GFs presents a promising strategy for developing closed-loop recyclable, multifunctional structural materials, offering a sustainable alternative to non-recyclable thermoset GFRPs and contributes to a circular economy in composite materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Zirconium-diboride silicon-carbide composites: A review

Zirconium diboride (ZrB 2 ) and silicon carbide (SiC) composites have long been of interest since it was observed that ZrB 2 improved the thermal shock resistance of SiC. However, processing of these materials can be difficult due to high and different sintering temperatures and differences in the thermodynamic stability of each material. ZrB 2 –SiC composites have been processed in a variety of ways including hot-pressing, spark-plasma sintering, reactive melt infiltration, pack cementation, chemical vapor deposition, chemical vapor infiltration, stereolithography, direct ink writing, selective laser sintering, electron beam melting, and binder jet additive manufacturing. Each manufacturing method has its own pros and cons. This review serves to summarize more than 60 years of research and provide a coherent resource for the variety of methods and advancements in development of ZrB 2 –SiC composites.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2024

Determining sulfur speciation in oxidatively crosslinked degradable polymers using sulfur K-edge X-ray absorption spectroscopy

A new family of water-degradable elastic polymers prepared by oxidative crosslinking of the parent polythionolactones shows promise in a broad range of applications, but the compositions of these materials elude conventional analytical methods. Here, in this work, we use sulfur K-edge X-ray absorption spectroscopy to quantify the amounts of thioether, disulfide, sulfone, sulfate ester, and thionoester in each polymer and to rule out the presence of several other functional groups, including sulfonate, thiosulfonate, sulfate, and sulfoxide. We rationalize this speciation as a function of linker flexibility in the context of sulfinyl cycloaddition reactions and propose a mechanism of aggregation for the oxidized polymers. Our results correlate with swelling ratios but not with porosity nor crosslink density measurements, demonstrating the importance of pairing mechanical and chemical techniques when characterizing heterogeneous organic polymers. Finally, we take advantage of the proximity of the gold M 4,5 -edges to the sulfur K-edge to analyze the binding and reactivity of Au(III) with the crosslinked polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Finite Difference Analysis of the Effect of Graphene Additions on the Electrical Conductivity of Polycrystalline Copper

A finite-difference method was used to explore the effect of graphene on the bulk electrical conductivity of copper-graphene composites. In this capacity, grain orientation information from pure copper and copper-graphene composites were used to generate synthetic 3D microstructures. The electrical conductivity of these microstructures were calculated using the finite difference method assuming different average grain sizes. From these calculations, we demonstrate that when high-conductivity grain boundaries are present within the microstructure arising from the presence of graphene, an increase in the bulk electrical conductivity is observed. On the other hand, the difference in textures between copper and copper-graphene composites may not account for a significant difference in bulk electrical conductivity. In comparison, the copper grain size has a considerably larger effect on electrical conductivity as previously anticipated. This is one of the first demonstrations of a physical basis for enhanced conductivity composites and presents pathways for further investigations on the effects of composite microstructural features, material interfaces and graphene content on electrical performance.

Frazier, William E.↗