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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Melting and solidification dynamics during laser melting of reaction-based metal matrix composites uncovered by in-situ synchrotron X-ray diffraction

Laser additive manufacturing (AM) of reaction-based metal matrix composites (MMCs) involves highly complex and non-equilibrium material transformation behavior, including melting, dissolution, precipitation, and solidification. Yet, the dynamics and interplay of these phase transformation processes remain poorly understood, posing substantial challenges in identifying the microstructure formation mechanism, and predicting and controlling the microstructure in the printed parts. Here, in this study, we performed the in-situ X-ray diffraction experiment to characterize the phase evolution dynamics of the 316L + 10 vol.%TiC system during laser melting, which provides direct and quantitative insights of the complex phase reaction and evolution dynamics under rapid heating and cooling conditions relevant to additive manufacturing of reaction-based MMCs. Further in-depth thermodynamic and kinetic calculations revealed that most of the phase evolution behavior observed in the in-situ X-ray diffraction experiment cannot be solely explained by widely used equilibrium thermodynamic models, and diffusion-controlled nonequilibrium dissolution and precipitation kinetics must be considered to elucidate the complex phase evolution behavior, including incomplete TiC dissolution, and three-step TiC precipitation. The three distinct types of precipitates generate unique hierarchical TiC micro- and nanostructures, which enhances the yield strength from 513 MPa to 877 MPa by 71 %, tensile strength from 628 MPa to 1054 MPa by 68 %, and Young's modulus from 193 GPa to 221 GPa by 14 %. The findings of our research provide the knowledge foundation for the design of unique microstructures and advanced MMC materials through laser AM.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Calcium-silicate-based porous particles, composition, method of making and use thereof

A method for synthesizing calcium-silicate-based porous particles (CSPPs) is described. Control over CSPP morphology and pore size is achieved through a refined solution-based synthesis, allowing loading of a variety of sealants. These particles, upon external stimuli, release the loaded sealant into the surrounding material. Methods of loading the CSPPs with loading sealant are described. The CSPPs may be used in pure form or mixed with another material to deliver self-healing, sealing and multi-functional properties to a physical structure. The composition of the CSPPs is described, along with methods of use of the CSPPs.

Shahsavari, Rouzbeh↗
DOE OSTI2020

Mesoporous nickel-iron-manganese-alloy based metal/metal oxide composite thick film catalysts

Selective alloy corrosion is used to synthesize a robust and ultrafine mesoporous NiFeMn-based metal/metal oxide oxygen evolving catalyst with ligament and pore sizes in the range of 10 nm and a BET surface area of 43 m2/g. As an oxygen evolving catalyst, the mesoporous catalyst exhibits high stability (>264 hours) at a high current density (500 mA/cm2) with a low overpotential (360 mV) using a moderate electrolyte concentration (1 M KOH). The catalyst is made from non-precious metals and its fabrication is straight forward and directly applicable to large-scale synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Self-assembly of wood-based shape memory composites triggered by solar-thermal energy

Transporting and assembling large, complex structures poses significant challenges due to their size, geometry, and cost. Additionally, the installation sites are often inaccessible or hazardous for humans, necessitating self-assembling capabilities in these structures. To mitigate these challenges, we propose using 3D printing materials with shape memory effect (SME) for both transport and construction. This approach involves developing 3D modular components into flat sheets for easier transportation, and then self-assembling into 3D structures on-site using solar energy. To gain a deeper understanding of the factors influencing material memory performance, we have chosen a composite PLA/WF, which is polylactic acid (PLA) with 20 wt% wood flour (WF) for this purpose, leveraging its high tensile modulus at 0.966 GPa, low cost, and sustainability. Printed shapes with this material can maintain a recovery ratio over 90% after 3 cycles. While traditional composites fillers (e.g. glass or carbon fiber) are added to enhance mechanical and thermal properties, the addition of bio-based fillers like WF accomplish similar goals without compromising sustainability. We conducted multiple experiments to demonstrate how environmental conditions (i.e. temperature) maximize the material’s SME. Although still at an early stage, this study provides initial insights into bridging the gap between the small-scale nature of shape memory polymers (SMPs) and their potential for large-scale additive manufacturing, addressing a critical need for efficient and sustainable construction. In the long term, we hope our study contributes to the design vision of utilizing SMPs for transportation, assembly, and deployment of complex structures, providing a new pathway for sustainable construction and transportation of large-scale structures to hard-to-access locations such as disaster-affected areas and remote deserts, etc.

4D printing↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix I

As stated in Section 9.1.2.2, a final inventory of parent radionuclides is projected for every DU at the time of facility operational closure in 2065. These final closure inventories are upper-bound estimates wherein each DU is assumed to reach its activity capacity. Composition vectors based on existing DU inventories are employed to estimate the final compositions of all existing and future DUs. Specifically, (1) the existing DU compositions are assumed to remain constant; (2) future DUs are assigned averaged existing compositions based on appropriately averaged DUs. This information is detailed in Appendix H, Section H.7. In addition, the variability in composition among existing DUs is used to generate log-normal distributions for uncertainty quantification, which is detailed in Section I.1.1.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

GenomeFace v1.0

GenomeFace is meta-genome binning software. Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

Lettich, Richard [Lawrence Berkeley National Labor↗
DOE OSTI2020

Homopiperazine-based catalysts for neutralization of organophosphorus-based compounds

Novel compositions of matter based on homopiperazine precursor materials and forming a homopiperazine-based ligand are disclosed, along with suitable techniques and materials for the synthesis and utilization thereof. In particular various synthetic schemes and techniques for applying the disclosed compositions of matter as a decontaminating agent. The decontaminating agents include homopiperazine-based ligand-metal complexes that are particularly effective at neutralizing toxicity of nerve agents, pesticides, and other toxic organophosphorus-based compounds. In preferred approaches, the homopiperazine-based ligand-metal complexes act as catalysts to facilitate substitution of a leaving group of the organophosphorus-based compound with a functional group that does not permit the organophosphorus-based compound to inactivate acetylcholinesterase upon introduction of the organophosphorus-based compound to a living organism such as insects and mammals. Advantageously, the catalytic homopiperazine-based ligand-metal complexes are formed using inexpensive, readily-available precursor materials, and may be utilized to neutralize toxins without relying on damaging caustic reactants or environmentally unfriendly organic solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advanced polymer impregnation and pyrolysis (PIP) of SiOC-based ceramic matrix composites (CMCs)

Silicon carbide (SiC) ceramic matrix composites (CMCs) are valued for their high-temperature properties, making them ideal for harsh environments. However, conventional polymer impregnation and pyrolysis (C-PIP) often result in porous composites and require numerous cycles for densification. This study introduces the advanced PIP (A-PIP) method using a crosslinked polycarbosiloxane (PCS) precursor to enhance densification efficiency. Five SiC CMCs were fabricated using C-PIP and A-PIP, with variations in fiber reinforcement and fiber coatings. A-PIP achieved up to 19 % higher density and 78 % lower porosity in 79–86 % less processing time compared to C-PIP. Moreover, SiC-SiOC composites with boron nitride-coated fibers showed significant improvements in tensile strength (11 MPa to 134 MPa) and strain at maximum strength (0.01–0.11 %), underscoring the role of weak fiber-matrix interfaces. In conclusion, these results demonstrate A-PIP's potential to produce dense, low-porosity SiC CMCs more efficiently, significantly reducing manufacturing time and costs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Carbon nanofibers based carbon–carbon composite fibers

Textile grade polyacrylonitrile (PAN) was used as a precursor material for carbon fiber preparation. E-beam irradiated polyacrylonitrile grafted carbon nanofibers were dispersed in polyacrylonitrile solution (dissolved in dimethyl formamide). Carbon nanofibers (CNF) infused polyacrylonitrile solution was wet spun on a lab-scale wet-spinning setup to form 50 to 70 µm diameter fibers with 3.2 wt.% CNF-PAN, 6.4 wt.% CNF-PAN, and neat PAN. Precursor fibers were characterized for thermal, mechanical and morphological properties using various techniques. Drawing the precursor fibers further enhanced polymer chain orientation and coalesced the voids, enhancing tensile strength and modulus by more than 150% compared to those of the undrawn fibers. Precursor composite fibers on carbonization showed enhanced strength, compared to that of pristine PAN fibers, by four times and stiffness by 14 times. The carbon–carbon composite fibers were further characterized with SEM/FIB, XRD and tensile strength. The property improvements were dependent on the uniform distribution of carbon nanofibers, and surface modification of carbon nanofibers further enabled their dispersion in the composite fibers. Furthermore, 3.2 wt.% CNFs in PAN fibers showed maximum improvement in properties compared to 6.4 wt.% CNF in PAN fibers, indicating that the property enhancements go through a maximum and then drop off due to challenge in getting uniform distribution of nanofibers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Wind Turbine Materials Recycling Prize Phase 2 (Commercialization of Wind Turbine Blade Waste (WTBW)-Based, Lightweight, Cementitious Composite Materials): Cooperative Research and Development Final Report, CRADA Number CRD-24-31305

The National Laboratory of the Rockies (NLR) and AltiSora, LLC., will develop new lightweight cementitious composite material technologies that will utilize wind turbine blade waste as a raw material to; (1) allow a high value addition (2) at low cost, enabling (3) a significant waste consumption volume; while (4) consuming the entire wind turbine blade, without (5) creating any waste or emissions and to also (6) offer specific benefits to communities involved.

17 WIND ENERGY↗