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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2019

Designing Multiscale Porous Metal by Simple Dealloying with 3D Morphological Evolution Mechanism Revealed via X-ray Nano-tomography

Designing materials with multiscale, hierarchical structure is critical to drive the advancement of new technology. Specifically, porous metals with multiscale porosity from nanometer to micrometer sizes would lead to enhanced physical and chemical properties—the micron-sized pores can increase the effective diffusivity of ion transport within the porous media, and the nano-sized pores provide high specific surface area, enabling functionalities that are unique to nanoporous metals. A new ternary precursor alloy selection concept utilizing the different mixing enthalpies is demonstrated in this work for the design of multiscale, bimodal porous copper from a simple, one-step dealloying of Cu–Fe–Al ternary alloy. The nanoporosity in the bimodal porous structure is formed from dealloying of the Cu-rich phase, whereas the microporosity is controlled by dissolving the Fe-rich phase, determined by both the initial Fe particle size and sintering profile. In addition to advancing the materials design method, the multiscale pore formation during dealloying was directly visualized and quantified via an interrupted in situ synchrotron X-ray nano-tomography. The 3D morphological analysis on tortuosity showed that the presence of the microporosity can compensate the increase of the diffusion path length due to nanoporosity, which facilitates diffusion within the porous structure. Altogether the focus of the work is to introduce a new strategy to design multiscale porous metals with enhanced transport properties, and sheds light on the fundamental mechanisms on the 3D morphological evolution of the system using advanced synchrotron X-ray nano-tomography for future materials development and applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Multiscale Inelastic Internal State Variable Corrosion Model

We present a corrosion internal state variable (ISV) damage model based upon the integrated computational materials engineering (ICME) hierarchical multiscale paradigm. Structure–property experiments for magnesium alloys were used where the only inputs were the volume fractions of each element of the periodic table. This macroscale ISV corrosion model finds its basis in Horstemeyer’s mechanical damage model, which includes three separate ISVs for damage nucleation, growth, and coalescence, as well as Walton’s inclusion of corrosion, which introduces five new ISVs for pit nucleation, growth, and coalescence, along with general corrosion and intergranular corrosion. While Walton’s corrosion ISVs are phenomenological in nature, herein we develop a multiscale physical basis for the corrosion ISVs. The parameters for the macroscale corrosion ISVs were garnered from the mesoscale Butler–Volmer equations. Pure magnesium with differing amounts of aluminum were used in corrosion tests to exemplify the different pitting, general corrosion, and intergranular corrosion rates, and the macroscale ISV model was calibrated with said data, in which the only inputs to the model are the volume percentages of the elements magnesium and aluminum. Although magnesium alloys were used to motivate and calibrate the model, the model is abstract enough to possibly capture other material systems as well.

Chemistry↗
DOE OSTI2025

Mesocrystal growth through oriented sliding and attachment of nanoplates

Oriented attachment is a critical, yet poorly understood, crystal growth pathway based on the self-assembly of nanocrystals. During oriented attachment, solvent-separated particles align and coalesce through forces that enable precise rotation and translation. While prior studies emphasized intragap forces driving crystallographic alignment, the forces enabling uniform stacking and superlattice formation remain unclear. Here, we demonstrate how macroscopic gibbsite mesocrystals emerge from nanoplates guided into staggered positions by directional sliding. Electron microscopy and X-ray scattering reveal the monoclinic superlattice structure, based on nanoplate stacking with a uniform ≈50° stagger along the gibbsite [010] direction. In situ liquid-cell TEM captures preferential sliding along the gibbsite [010] direction, decelerating with increasing particle overlap. Molecular dynamics simulations reveal that this staggered arrangement corresponds to a global free-energy minimum, rather than full alignment. The simulations also confirm that sliding along the [010] direction is energetically favored and provide insight into the role of interfacial water in achieving long-range ordered assemblies. These insights highlight the energy landscape’s role in oriented attachment, with implications for material synthesis and hierarchical structures in nature.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Post-Modification of Crystalline Peptoid Nanomembranes with Active Nanoparticles for Efficient Photooxidation of a Mustard Gas Simulant

Peptoids (or poly-N-substituted glycines) hold immense potential for assembling into hierarchically structured functional materials via controlled molecular interactions. To create self-assembled materials with tailored functionalities, peptoid sequences are often conjugated with reactive or recognition motifs to enable applications including specific binding, biomimetic catalysis, and fluorescence imaging. However, the direct integration of bulky functional motifs into peptoid sequences can disrupt assembly processes and structural outcomes. Herein, we present a post-modification strategy for functionalizing pre-formed 2D crystalline assemblies. Through introducing clickable active sites, such as azide, alkyne, or thiol groups into a peptoid sequence, site-specific conjugation is achieved post-assembly via efficient “click”-type reactions. This strategy enables the ordered alignment of functional groups and gold nanoparticles (Au NPs) on the surface of 2D peptoid nanomaterials with controlled density, while preserving their high crystallinity and structural integrity. Furthermore, we demonstrated that nanomembranes functionalized with both Au NPs and porphyrins enhance the efficiency and selectivity of the photooxidation of 2-chloroethyl ethyl sulfide, a simulant of sulfur mustard. This innovative strategy lays the groundwork for advancing peptoid-based functional materials across diverse applications, from catalysis to biomedicine.

Chemistry↗
DOE OSTI2021

Self-assembly of aramid amphiphiles into ultra-stable nanoribbons and aligned nanoribbon threads

Small molecule self-assembly is an established route for producing high surface area nanostructures with readily customizable chemistries and precise molecular organization. However, these structures are fragile, exhibiting molecular exchange, migration, and rearrangement - among other dynamic instabilities - and ultimately disassociate upon drying. Here we show a small molecule platform, the aramid amphiphile (AA), that overcomes these dynamic instabilities by incorporating a Kevlar-inspired domain into the molecular structure. Strong, anisotropic interactions between AAs suppress molecular exchange and elicit spontaneous self-assembly in water to form nanoribbons with lengths of up to 20 microns. The nanoribbons have a Young’s modulus of 1.7 GPa and tensile strength of 1.9 GPa. Here, we exploit this stability to extend small molecule self-assembly to hierarchically ordered macroscopic materials outside of solvated environments. Through an aqueous shear alignment process, we organize AA nanoribbons into arbitrarily long flexible threads that support 200 times their weight when dried. Tensile tests of the threads provide a benchmark for Young’s moduli (between ~400 and 600 MPa) and extensibilities (between ~0.6 and 1.1%) that depend on the counterion chemistry in solution. This bottom-up approach to macroscopic materials could benefit solid-state applications, historically inaccessible by self-assembled nanomaterials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Spatially varying colloidal phase behavior on multi-dimensional energy landscapes

A method is reported to determine equilibrium concentration profiles and local phase behavior for colloids on multi-dimensional energy landscapes. A general expression is derived based on local particle concentration and osmotic pressure differences that are balanced by forces on colloids due to energy landscape gradients. This analysis is applied to colloidal particles in high frequency AC electric fields within octupolar electrodes, where the energy landscape can be shaped in two dimensions. These results are also directly applicable to any particles having induced dipoles in spatially non-uniform electromagnetic fields. Predictions based on modelling colloids with an effective hard disk equation of state indicate inhomogeneous solid and fluid states coexisting on different shaped energy landscapes including multiple minima. Model predictions show excellent agreement with time-averaged Brownian Dynamic simulations at equilibrium. Findings demonstrate a general approach to understand colloidal phase behavior on energy landscapes due to external fields, which could enable control of colloidal microstructure on morphing energy landscapes and the inverse design of fields to assemble hierarchically structured colloidal materials.

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

Leveraging Additive Manufacturing to Fabricate High Temperature Alloys With Co-Designed Mechanical Properties and Environmental Resistance

A paradigm shift in the traditional sequential design approaches is critically essential to create application-specific hierarchical and multifunctional materials with superior long-term performance for next-generation energy technologies involving extreme environments. In the current work, we aim to leverage the flexibility and geometric/compositional complexity offered by additive manufacturing to demonstrate this new approach by codesigning a compositionally graded Ni-based alloy for molten salts\sCO 2 heat exchangers to enable mitigation of environmental degradation of surfaces exposed to molten halide salts, while simultaneously suppressing the consequent deterioration in mechanical stability. Further, thermokinetic modeling describing the underlying physics of thermally- and environmentally induced spatiotemporal compositional and microstructural evolution will be employed to predict the parameter space of material deposition processes and precisely identify the required composition gradient. Preliminary corrosion and mechanical testing of the dual material demonstrated the potential of the material to replace existing solid solution strengthened materials for this application.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems

This document contains the proceedings of the Training Workshop on Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems held at NASA Langley Research Center, Hampton, Virginia, March 5 - 6, 2002. The workshop was jointly sponsored by Old Dominion University's Center for Advanced Engineering Environments and NASA. Workshop attendees were from NASA, other government agencies, industry, and universities. The objectives of the workshop were to give overviews of the diverse activities in hierarchical approach to material modeling from continuum to atomistics; applications of multiscale modeling to advanced and improved material synthesis; defects, dislocations, and material deformation; fracture and friction; thin-film growth; characterization at nano and micro scales; and, verification and validation of numerical simulations, and to identify their potential for future aerospace systems.

Noor, Ahmed K.↗
NASA NTRS2010

Developing a Logistics Data Process for Support Equipment for NASA Ground Operations

The United States NASA Space Shuttle has long been considered an extremely capable yet relatively expensive rocket. A great part of the roughly US $500 million per launch expense was the support footprint: refurbishment and maintenance of the space shuttle system, together with the long list of resources required to support it, including personnel, tools, facilities, transport and support equipment. NASA determined to make its next rocket system with a smaller logistics footprint, and thereby more cost-effective and quicker turnaround. The logical solution was to adopt a standard Logistics Support Analysis (LSA) process based on GEIA-STD-0007 http://www.logisticsengineers.org/may09pres/GEIASTD0007DEXShortIntro.pdf which is the successor of MIL-STD-1388-2B widely used by U.S., NATO, and other world military services and industries. This approach is unprecedented at NASA: it is the first time a major program of programs, Project Constellation, is factoring logistics and supportability into design at many levels. This paper will focus on one of those levels NASA ground support equipment for the next generation of NASA rockets and on building a Logistics Support Analysis Record (LSAR) for developing and documenting a support solution and inventory of resources for. This LSAR is actually a standards-based database, containing analyses of the time and tools, personnel, facilities and support equipment required to assemble and integrate the stages and umbilicals of a rocket. This paper will cover building this database from scratch: including creating and importing a hierarchical bill of materials (BOM) from legacy data; identifying line-replaceable units (LRUs) of a given piece of equipment; analyzing reliability and maintainability of said LRUs; and therefore making an assessment back to design whether the support solution for a piece of equipment is too much work, i.e., too resource-intensive. If one must replace or inspect an LRU too much, perhaps a modification of the design of the equipment can make such operational effort unnecessary. Finally, this paper addresses processes of tying resources to a timeline of tasks performed in ground operations: this enables various overarching analyses, e.g., a summarization of all resources used for a given piece of equipment. Quality Control of data will also be discussed: importing and exporting data from product teams, including spreadsheets-todatabase or data exchange between databases.

Chakrabarti, Suman↗
NASA NTRS2019

TPSAS-NF1676L-32791-DND

Accelerate adoption of 3D printing in aerospace by investigating issues involving part quality, material compatibility, and hierarchical structures.

John M Gardner↗
NASA NTRSDate not supplied

A Partitioned -Task Parallel Implementation of the NASA Multiscale Analysis Tool for High Performance Computing

The NASA Multiscale Analysis Tool (NASMAT) is a “plug and play” software package that allows users to conduct massively multiscale modeling of hierarchical and nonlinear materials. This work extends the scalability and improves the High Performance Computing friendliness of NASMAT by adopting a Partitioned Task-Parallel approach. Interoperability of NASMAT with external software is enhanced through preCICE, a open source library for multiphysics coupling in a partitioned manner. Enhancement through preCICE allows for easy integration of NASMAT to other macro solvers and dissociates the parallelization strategy adopted within NASMAT from the macro solver. The task-parallel framework based on Master-Worker approach is implemented as the parallelization scheme. The scheme accounts for hierarchy of multiple scales (task-dependence) and heterogeneous nature (dynamic load balancing) of computations. The applicability and scalability of the framework will be evaluated by analyzing large scale engineering problems through massively multiscale methods.

NASMAT↗
DOE OSTI2022

System and method for using ultramicroporous carbon for the selective removal of nitrate with capacitive deionization

The present disclosure relates to a flow through electrode, capacitive deionization (FTE-CDI) system which is able to adsorb nitrates from water being treated using the system. The system makes use of a pair of electrodes arranged generally parallel to one another, with a water permeable dielectric sandwiched between the electrodes. The electrodes receive a direct current voltage from an electrical circuit. At least one of the electrodes is formed from a carbon material having a hierarchical pore size distribution which includes a first plurality of pores having a width of no more than about 1 nm, and a second plurality of micro-sized pores. The micron-sized pores enable a flow of water to be pushed through the electrodes while the first plurality of pores form adsorption sites for nitrate molecules carried in the water flowing through the electrodes.

Campbell, Patrick↗
DOE OSTI2026

Structural and Mechanical Analysis of Individual Mineralized Collagen Fibrils Using In Situ Transmission Electron Microscopy

Bone serves as an example of nature’s architectured material with its characteristic blend of strength and toughness, all at a lightweight design. Given the hierarchical nature of these materials, it is essential to understand the governing mechanisms and organization of their constituents across length scales for bioinspired structural design. Despite recent advances in transmission electron microscopy (TEM) that have allowed us to witness the hierarchical arrangement of bone at micro-down to the nanoscale, we are still missing the details about the structural organization and mechanical properties of the main building blocks of bone─mineralized collagen fibrils (MCFs). Here, we present a method to extract individual MCFs from nature’s model material, mineralized turkey leg tendon, using a dropcasting procedure. By isolating the MCFs onto TEM supporting grids, we visualized the arrangement of organic and mineral phases within individual MCFs at the nanoscale. Using a four-dimensional scanning transmission electron microscopy (4D-STEM) approach, the orientation of individual mineral crystals within the MCFs was examined. Furthermore, we conducted in situ tensile experiments, revealing exceptional tensile strains of at least 8%, demonstrating the intricate relationship between structural organization and the mechanical behavior of MCFs. These insights into the ultrastructure of mineralized tissue building blocks, as well as the proposed sample-extraction method compatible with in situ mechanical testing, provide a strong basis for research into nature-inspired material design.

4D-STEM↗
DOE OSTI2022

Designing sequence-defined peptoids for fibrillar self-assembly and silicification

In the biological environment, mineral crystals exquisitely controlled by biomacromolecules often show intricate hierarchical structures and superior mechanical properties. Among these biominerals, spicules, hybrid silica/protein superstructures serving as skeletal elements in demosponges, represent an excellent example for motivating the synthesis of silica materials. Herein, by designing sequence-defined peptoids containing side chains with a strong binding to silica, we demonstrated that self-assembly of these peptoids into fiber structures enables the mimicking of both biocatalytic and templating functions of silicatein filaments for the formation of silica fibers at near-neutral pH and ambient temperature. We further showed that the presence of amino groups is significant for the nucleation of silica on self-assembled peptoid nanofibers. Molecular dynamics simulation further confirmed that having silica-binding of amino side chains is critical for self-assembled peptoid fibers in triggering silica formation. Here, we demonstrated that tuning inter-peptoid interactions by varying carboxyl and amino side chains significantly influences the assembly kinetics and final morphologies of peptoid assemblies as scaffolds for directing silica mineralization to form silica spheres, fibers, and sheets. The formation of silica shell on peptoid fibers increased the mechanical property of peptoid hydrogel materials by nearly 1000-fold, highlighting the great potential of using silicification to enhance the mechanical property of hydrogel materials for applications including tissue engineering. Since peptoids are highly robust and programmable, we expect that self-assembly of peptoids containing solid-binding side chains into hierarchical materials opens new opportunities in the design and synthesis of highly tunable scaffolds that direct the formation of composite nanomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2021

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

Abstract Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (<400) and large structural data (>130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

Chen, Chi (ORCID:0000000180087043)↗