Metal Hydride Composition-Derived Parameters as Machine Learning Features for Material Design and H2 Storage
Poster for Techconnect Conference
Nations, Sean↗
SEARCH · Engineering Papers
Results for “Composite Material”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Synthesis Science of Nanomembranes and Sheets: New Compositions, Geometries, and Interfaces across Energy-relevant Materials. Final Report for DOE-UNM-20186
This report summarizes the results of DOE-sponsored research on synthesis and processing science of inorganic nanomembranes and 2D materials.
36 MATERIALS SCIENCE↗
Elastic gas barrier coating compositions
An elastic gas barrier coating composition includes a barrier material dispersed in an aqueous media, a polysulfide, and a curing agent reactive with the polysulfide. When applied to a substrate and cured to form a coating, the barrier material forms a continuous phase and a polysulfide elastomer forms a discontinuous phase. An elastic gas coating is also disclosed that includes a continuous phase with a barrier material and a discontinuous phase with a polysulfide elastomer. Substrates at least partially coated with elastic gas barrier coating compositions are further disclosed.
36 MATERIALS SCIENCE↗
Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)
Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.
25 ENERGY STORAGE↗
Using Large-Scale Additive Manufacturing for Wind Turbine Blade Core Structures
This report describes the techno-economic potential of using additive manufacturing technologies to design large wind turbine blade structures. After considering all additive technologies, the authors identified large-scale, polymer-based, material extrusion as the three-dimensional (3D) printing technology best suited to meet modern wind turbine blade manufacturing costs, cycle times, and geometric scale. Due to the lower structural performance requirements and the opportunity to reduce resin uptake mass penalties within the blade, the core material within the sandwich composite sections of the wind turbine blade is established as a logical entry point into advanced wind turbine blade manufacturing.
17 WIND ENERGY↗
A method and tool to determine the colorimetric and photobiological properties of light transmitted through glass and other optical materials
Here we describe a framework and provides a computational tool to characterize the color quality and biological potential of light that is transmitted through glass and other optical materials. The IES TM-30 framework and Excel computation tool were adapted to evaluate color quality, with measures from CIE S 026, UL 24480, and the WELL Building Standard v2 added to evaluate biological potential. The user selects a pre-transmittance spectral power distribution (SPD), such as for a CIE D-Series Illuminant, Planckian radiation, electric lamp, or measurement of daylight at a building site. The tool allows a user to populate a database with spectral data for glazing or other optical materials, comprising spectral transmission and the spectral reflectance of both sides. The user creates a unit with one, two, or three panes of glazing or other optical materials, and the tool calculates the composite spectral transmittance accounting for reflections between materials. The tool computes the transmitted SPD, then determines colorimetric and photobiological outputs using the transmitted SPD as the test source. Glass, window, and skylight manufacturers can employ the tool to optimize glazing spectral transmission to achieve intentional colorimetric and photobiological performance with transmitted light. Electric lighting manufacturers, designers, and researchers can use the tool to evaluate the impact of glazing units and other optical materials on the color quality and biological potential of transmitted light.
36 MATERIALS SCIENCE↗
High-Pressure, Low Temperature Composite Nozzles for Long-Term H2 Dispensing (Final Report)
Final report on evaluation of NanoSonic high pressure hydrogen hoses under CRADA 399. NanoSonic hose liners, fiber winding, and hose end crimp fittings were analyzed for material properties, design, and burst test performance. The hose burst pressures exceeded 100 MPa under -130C sub ambient conditions. Hose crimp fittings were found to have some defects from assembly that impacted burst pressures. The use of digital image correlation provided unique insight to the crimped fitting influence on the composite material and the winding pattern during burst testing.
08 HYDROGEN↗
Quantum Dots on Silicon-on-Insulator (QD/SOI): Nanoscale Strain and Band Structure Engineering (Final Report)
As the project titles indicate, the work focused on Group IV nanomembranes (NMs), thin functional layers, and interfaces, all with one or more dimensions at the nanoscale. The primary focus was discovery driven fundamental science. This approach led to surprising new, unexpected results that ended up being patentable and creating commercial value, but that were also high-impact science. Our effort divided into several overlapping thrusts: 1) strain engineering of NMs, via both lattice strain and externally applied strain, 2) interfaces between crystalline semiconductor NMs and other 2D sheets that are stacked or grown on each other, and 3) charge transport (electronics and optoelectronics) in thin layers, sheets, and surfaces. We accomplish these goals via 1) fabrication of new or higher-quality materials using NM approaches, 2) growth of new combinations, or 3) transfer and stacking to create new composites. The range of materials included combinations of Si, Ge, graphene, and several III-V compounds. The work has foundations in both nano- and mesoscale science.
36 MATERIALS SCIENCE↗
Evaluation of Dissolvable Additively Manufactured Molds and Supports for Electroimpacts Innovative Additive Manufacturing Process
Electroimpact inc. has developed a new additive manufacturing process involving continuous fiber reinforcement along with high-strength thermoplastics. A unique part of this process includes using additive manufacturing to print a base geometry that is then used as a tool for continuous fiber placement. Ideally the tool should be disposable and cost effective. After printing the base tool, continuous fiber is printed on top of the tool using a secondary additive head on the same system to create the final printed part. After continuous fiber printing is completed, the tool is removed and disposed of to leave only the final desired structure. Materials used for the base tool have several requirements that must be met in order to complete this process. First, the material must be rigid and strong enough to support the loads from the secondary printing operation (continuous fiber printing). In addition, the material must be able to adhere to the secondary process material as well be capable of being removed such that the only remaining structure is continuous fiber composite. Using innovative soluble materials allows for parts to be produced through additive with molds that are produced as non-reusable one-off shapes and sizes.
36 MATERIALS SCIENCE↗
Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites
With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.
36 MATERIALS SCIENCE↗
Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks
The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.
36 MATERIALS SCIENCE↗
Electrical conductivity of TaO x as function of composition and temperature
The ability to model the resistive switching devices depends on input material parameters and is particularly sensitive to values of electrical conductivity of the functional material as a function of composition and temperature. Here we report the results of conductivity measurements of TaO x with 0 AC ) changing from 0.34 eV at x=2.36 to 0.016 eV at x=1.
36 MATERIALS SCIENCE↗
Hierarchical Composites Patterned via 3D Printed Cellular Fluidics
Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.
36 MATERIALS SCIENCE↗
Specifics of Thermal Transport in Graphene Composites: Effect of Lateral Dimensions of Graphene Fillers
In this work, we report on the investigation of thermal transport in noncured silicone composites with graphene fillers of different lateral dimensions. Graphene fillers are comprised of few-layer graphene flakes with lateral sizes in the range from 400 to 1200 nm and the number of atomic planes from 1 to ~100. The distribution of the lateral dimensions and thicknesses of graphene fillers has been determined via atomic force microscopy statistics. It was found that in the examined range of the lateral dimensions, the thermal conductivity of the composites increases with increasing size of the graphene fillers. The observed difference in thermal properties can be related to the average gray phonon mean free path in graphene, which has been estimated to be around ~800 nm at room temperature. The thermal contact resistance of composites with graphene fillers of 1200 nm lateral dimensions was also smaller than that of composites with graphene fillers of 400 nm lateral dimensions. The effects of the filler loading fraction and the filler size on the thermal conductivity of the composites were rationalized within the Kanari model. The obtained results are important for the optimization of graphene fillers for applications in thermal interface materials for heat removal from high-power-density electronics.
36 MATERIALS SCIENCE↗
Source apportionment of soot particles and aqueous-phase processing of black carbon coatings in an urban environment
Abstract. The impacts of soot particles on climate and human health depend on the concentration of black carbon (BC) as well as the thickness and composition of the coating material, i.e., organic and inorganic compounds internally mixed with BC. In this study, the size-resolved chemical composition of BC-containing aerosol was measured using a high-resolution soot particle aerosol mass spectrometer (SP-AMS) during wintertime in Fresno, California, a location influenced by abundant combustion emissions and frequent fog events. Concurrently, particle optical properties were measured to investigate the BC absorption enhancement. Positive matrix factorization (PMF) analysis was performed on the SP-AMS mass spectral measurements to explore the sources of soot particles and the atmospheric processes affecting the properties of BC coatings. The analysis revealed that residential woodburning and traffic are the dominant sources of soot particles. Alongside primary soot particles originating from biomass burning (BBOABC) and vehicles (HOABC) two distinct types of processed BC-containing aerosol were identified: fog-related oxidized organic aerosol (FOOABC) and winter-background OOABC (WOOABC). Both types of OOABC showed evidence of having undergone aqueous processing, albeit with differences. The concentration of FOOABC was substantially elevated during fog events, indicating the formation of aqueous secondary organic aerosol (aqSOA) within fog droplets. On the other hand, WOOABC was present at a relatively consistent concentration throughout the winter and was likely related to the formation of secondary organic aerosol (SOA) in both the gas phase and aerosol liquid water. By comparing the chemical properties and temporal variations in FOOABC and WOOABC, we gain insights into the key aging processes of BC aerosol. It was found that aqueous-phase reactions facilitated by fog droplets had a significant impact on the thickness and chemical composition of BC coatings, thereby affecting the light absorption and hygroscopic properties of soot particles. These findings underscore the important role of chemical reactions occurring within clouds and fogs and influencing the climate forcing of BC aerosol in the atmosphere.
54 ENVIRONMENTAL SCIENCES↗
Performance of hybrid Innegra-carbon fiber composites
Abstract In this work material synergy with high stiffness carbon fiber with ductile high strength polypropylene fiber (Innegra S), (referred to as Innegra, herein) have been evaluated in a range of laminate designs. Both woven and discontinuous carbon fiber have been considered. The discontinuous fibers are based on three-dimensional deposition (3DEP) (referred to as 3DEP, herein) carbon fiber preform process. Eleven (11) variants of Innegra-carbon fiber hybrid laminates were investigated for tensile, flexure, compression, in-plane shear and low velocity impact response. The effect of position of Innegra within the laminate was studied and found to influence strength and stiffness properties. In terms of overall trends, Innegra provides upward of 18% improvement in strain (ductility) to the composite and eliminates brittle fracture of carbon fiber. The moduli trends follow the proportionality of Innegra fiber to carbon fiber plies. However, the strength is controlled by the interface between Innegra and carbon fiber. The primary failure mode in tension and compression is via onset of debonding between Innegra and carbon fiber. The 3DEP carbon fiber constituents provided highest values of in-plane shear indicating that three-dimensional (3D) network of carbon fiber provides higher shear resistance. The Innegra intensive variants exhibited superior energy absorption under low velocity impact, at energy levels 15–60 J. This work provides insight for designers to incorporate Innegra and carbon fiber hybrids in composite structures.
36 MATERIALS SCIENCE↗
Improving deep learning model performance under parametric constraints for materials informatics applications
Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.
36 MATERIALS SCIENCE↗
Characterization and repair of core gap manufacturing defects for wind turbine blades
Various wind turbine blade components, such as shear webs and skins, commonly use fiber-reinforced composite sandwich structures with a core material like balsa or foam. During manufacturing, core gap defects may result from the misalignment of adjacent foam or balsa core sheets in the blade mold. It is important to understand the influence that core gaps have on the structural integrity of wind turbine blades and how to mitigate their influence. This research characterized the effects of core gap defects at the manufacturing and mechanical levels for both epoxy and next-generation thermoplastic composites. Common repair methods were assessed and compared. Multiple defect sizes were compared using temperature data gathered with thermocouples embedded during manufacturing to core gap defect characteristics obtained using image-mapping techniques, optical microscopy, and mechanical characterization by long beam flexure. Results showed that peak exothermic temperatures during curing were closely related to core gap size. The long beam flexure tests determined that transverse core gaps under pure bending loads can have a substantial effect on the ultimate facesheet strength of both epoxy and thermoplastic composite sandwich structures (up to 25% strength reduction), although the size of the defect itself had less of an influence on the magnitude of the strength reduction. The supporting image-mapping techniques indicated that the distortion of the composite facesheets by the core gaps contributed to the premature failures. The repair methods used in this study did very little to improve the ultimate strength of the sandwich panels that previously had core gap defects. The repair of the thermoplastic panel resulted in a further loss in ultimate facesheet strength. This research demonstrated that there is a vital need for the development of a compatible thermoplastic polymer repair resin system and appropriate resin specific repair procedures for the next generation of recyclable thermoplastic wind blades.
17 WIND ENERGY↗