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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

X-ray Computed Tomography as a Metrology Technique for the Analysis of Additively Manufactured Material

X-ray computed tomography (X-ray CT) is an analytical technique used in materials science to non-destructively characterize features in a variety materials like polymer, metals, composites, and explosives. It also has the capability of imaging additively manufacture, machine and assembled parts. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of the material characteristics. Additionally, X-ray CT can obtain accurate measurements of dimensional and topographic variations due to different stimuli and assess the accuracy of material production. This study focuses on parts manufactured via metal additive manufacturing (AM). Although AM produces parts faster and easier, the printing process can produce defects (pores and surface roughness) that undermine the part’s mechanical properties and performance. The analysis of 3D printed objects has an asset in that the material has an STL file from which the item was printed, which is not available in many manufactured materials (i.e., foams) due to stochastic structures. For this study, the print accuracy of four additively manufactured cylinders will be assessed via X-ray CTto approximate the surface roughness and visualize any major morphological changes to assess the dimensional accuracy of complex additively manufactured parts. It was concluded that using X-ray CT to measure surface roughness was affective because reasonable surface roughness values were measured. Additionally, itwas determined that small-scale features can be produced via additive manufacturing with strong dimensional accuracy so long as the features are highly complex with sharp grooves.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Science and Technology Review (May 2021)

Lawrence Livermore has been on the forefront of cancer research for over 60 years. Early interest in cancer statistics stemmed from the nature of work, particularly how radiation affects humans. The Department of Energy funded research to investigate the effects of radiation on workers with long term exposure. This research quickly morphed into a wider breadth of cancer research topics, including the use of advanced computational models to investigate mutations in genes. Livermore is regarded as a leader in cancer research, from the Human Genome Center to its participation in the National Cancer Institute’s “Moonshot” project. The highly interdisciplinary Laboratory unites research in one more example: bringing together cancer biology, 3D printing, high-performance computing, big data, and materials science to address this pressing medical challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Preferred crystallographic orientation of cellulose in plant primary cell walls

Abstract Cellulose, the most abundant biopolymer on earth, is a versatile, energy rich material found in the cell walls of plants, bacteria, algae, and tunicates. It is well established that cellulose is crystalline, although the orientational order of cellulose crystallites normal to the plane of the cell wall has not been characterized. A preferred orientational alignment of cellulose crystals could be an important determinant of the mechanical properties of the cell wall and of cellulose-cellulose and cellulose-matrix interactions. Here, the crystalline structures of cellulose in primary cell walls of onion ( Allium cepa ), the model eudicot Arabidopsis ( Arabidopsis thaliana ), and moss ( Physcomitrella patens ) were examined through grazing incidence wide angle X-ray scattering (GIWAXS). We find that GIWAXS can decouple diffraction from cellulose and epicuticular wax crystals in cell walls. Pole figures constructed from a combination of GIWAXS and X-ray rocking scans reveal that cellulose crystals have a preferred crystallographic orientation with the (200) and (110)/( $$1\bar 10$$ 1 1 ¯ 0 ) planes preferentially stacked parallel to the cell wall. This orientational ordering of cellulose crystals, termed texturing in materials science, represents a previously unreported measure of cellulose organization and contradicts the predominant hypothesis of twisting of microfibrils in plant primary cell walls.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Physical Sciences Vistas (Issue 2 2021) [Newsletter]

I am pleased to introduce the second 2021 issue of Physical Sciences Vistas, with a focus on “excellence in mission-focused science, technology, and engineering.” Excellence in mission-focused ST&E within the Physical Sciences Directorate (ALDPS) spans a broad spectrum of accomplishments in nuclear physics and materials science that sustains and enhances LANL’s science base.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defect Characterization Using Positron Annihilation Spectroscopy on Laser-Ablated Surfaces

In recent years, short, pulsed laser ablation has been gaining popularity for machining small-scale test geometries from bulk samples and for efficient serial sectioning. These laser-based techniques are being added to the toolbox in material science, which makes it necessary to understand the changes in the material that occur from the laser–material interaction. Positron annihilation spectroscopy is a unique, nondestructive technique to investigate small defects in materials difficult to investigate by other tools. In this work, Doppler broadening and positron lifetime annihilation spectroscopy are utilized to help quantify the damage in materials treated with short, pulsed lasers. Using a femtosecond laser on single crystal silicon, this manuscript shows that clusters of vacancy-like defects and small voids increase systematically with laser power. Lastly, the damage induced by the laser can also reach to micrometer depths.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermal processing to modulate surface chemistry and bulk charge distribution in nickel-rich layered lithium positive electrodes

The broader application of nickel-rich layered oxides as positive electrode materials for lithium-ion batteries has been hindered by their high manufacturing cost and inferior cycling stability. Thermal processing, which is integral to electrode materials manufacturing and fundamental in materials science, has not been fully utilized to design advanced positive electrode materials. Herein, we demonstrate the capability of using quenching heat treatment to regulate Li distribution and modulate electronic structure near particle surface. The resulting materials exhibit less parasitic reactions with the electrolyte and an improved charge distribution homogeneity in secondary particles, leading to more stable cycling performance at high voltages (4.5 V vs Li/Li + ). Our synchrotron X-ray analyses reveal the underlying interplay between surface structure and bulk charge distribution in positive electrode materials particles. While strategies used to stabilize positive electrode materials through compositional control, surface modification, and electrolyte engineering have become mature, thermal processing can be advantageous to further improve positive electrode materials manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computing inelastic neutron scattering spectra from molecular dynamics trajectories

Abstract Inelastic neutron scattering (INS) provides a weighted density of phonon modes. Currently, INS spectra can only be interpreted for perfectly crystalline materials because of high computational cost for electronic simulations. INS has the potential to provide detailed morphological information if sufficiently large volumes and appropriate structural variety are simulated. Here, we propose a method that allows direct comparison between INS data with molecular dynamics simulations, a simulation method that is frequently used to simulate semicrystalline/amorphous materials. We illustrate the technique by analyzing spectra of a well-studied conjugated polymer, poly(3-hexylthiophene-2,5-diyl) (P3HT) and conclude that our technique provides improved volume and structural variety, but that the classical force field requires improvement before the morphology can be accurately interpreted.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Manufacturing of Complex Silicon–Carbon Structures: Exploring Si x C y Materials

This paper reports on the manufacturing of complex three-dimensional Si/C structures via a chemical vapor deposition method. The structure and properties of the grown materials were characterized using various techniques including scanning electron microscopy, aberration-corrected transmission electron microscopy, confocal Raman spectroscopy, and X-ray photoelectron spectroscopy. The spectroscopy results revealed that the grown materials were composed of micro/nanostructures with various compositions and dimensions. These included two-dimensional silicon carbide (SiC), cubic silicon, and various SiC polytypes. The coexistence of these phases at the nano-level and their interfaces can benefit several Si/C-based applications ranging from ceramics and structural applications to power electronics, aerospace, and high-temperature applications. With an average density of 7 mg/cm 3 , the grown materials can be considered ultralightweight, as they are three orders of magnitude lighter than bulk Si/C materials. This study aims to impact how ceramic materials are manufactured, which may lead to the design of new carbide materials or Si/C-based lightweight structures with additional functionalities and desired properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Applications of Autonomous Data Collection and Active Learning

Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From Data to Discovery: AI's Transformative Role in Thin Film Research

The advancement of thin film technologies is pivotal for progress in numerous fields, including energy, electronics, and quantum computing. However, the traditional trial-and-error approach to materials discovery is inherently slow and inefficient. This presentation will showcase how artificial intelligence (AI) is transforming thin film research by enabling a data-driven paradigm shift. We will highlight our past successes in applying AI to understand radiation damage in thin film oxides, demonstrating how graph analytics can unravel complex material behavior. Additionally, we will provide insights into our current work at the National Renewable Energy Laboratory, where we are leading the charge in autonomous materials science. Backed by a $14M investment in our characterization facility, we are developing AI-guided workflows that seamlessly integrate experimentation and AI-guided decision-making. By harnessing the power of AI, we aim to accelerate the discovery and design of high-performance thin films, propelling innovation across a multitude of industries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SMATool: Strength of materials analysis toolkit

The study of the strength of materials is a cornerstone in material science and engineering, playing a critical role in shaping the progress and application of materials in diverse industrial sectors. The strength of a material is meticulously examined to understand the behavior of the material under different stress conditions and environments, thereby guiding material selection and structural design. Herein, we introduce the SMATool, a computational toolkit for the efficient calculation and analysis of material strength at both zero and finite temperatures for 3D, 2D, 1D, and tubular 2D-based nanostructures and nanotubes, as well as 1D nanoribbons. The toolkit is capable of calculating tensile, shear, ultimate, yield, and indentation (Vickers' hardness) strengths in various dimensions, as well as the energy storage capacity. We conducted several calculations both at zero and finite temperatures to validate the accuracy and reliability of the developed software. Here, the results show that the SMATool package provides accurate predictions that align with existing data on material strength. SMATool integrates seamlessly with widely used electronic structure codes like VASP and Quantum Espresso, providing a user-friendly interface catering to academic researchers and industry professionals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cobalt-free, high-nickel layered oxide cathodes for lithium-ion batteries: Progress, challenges, and perspectives

High-nickel layered oxides are enabling extraordinary growth of electric vehicles market due to its high energy density. Nonetheless, leading battery manufacturers are trying to cut down the manufacturing costs further by eliminating the dependency on cobalt in cathode materials. In this perspective, we explore several aspects that need to be considered to develop cobalt-free high-Ni layered oxides by reviewing the fundamental properties of LiNiO2. Furthermore, critical key factors for exploring alternative dopants and substituents are discussed for designing stable and inexpensive Co-free high-Ni layered oxides from a viewpoint of materials science and electrochemistry. Lastly, a perspective on the future research direction on Co-free high-Ni layered oxides are highlighted with respect to practical considerations.

25 ENERGY STORAGE↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Self–Healing and Stretchable Molecular Ferroelectrics with High Expandability

The interplay between crystal ordering and stretchability is frequently encountered in contemporary materials science, particularly in the case of ferroelectrics. The inherent dilemma arises when these materials need to withstand repetitive mechanical deformations or stretching without sacrificing their crystal integrity, all while retaining their remarkable ferroelectric properties and even exhibiting self-healing capabilities. This complexity further presents a significant challenge in the design and engineering of mechanically rigid molecular ferroelectric crystals, particularly for applications where both precise crystalline structure and mechanical adaptability are crucial. In this study, the humidity-controlled expansion and contraction, dissolution, and recrystallization of a self-assembled molecular ferroelectric-in-hydrogel framework are reported. Self-healing ferroelectric-in-hydrogel networks exhibit a recyclable humidity-tailored ionic conductivity from 2.86 × 10 –6 to 1.36 × 10 –5 S cm –1 , facilitating the stretchable piezoelectric sensing. Additionally, the dynamic bond reforming interactions are observed, leading to the tailoring of Young's modulus from 452 to 170 MPa, maintaining ferroelectricity under a strain of 20% with a piezoelectric coefficient of 15.7 pC N –1 . Upon lattice contraction, the molecular contacts undergo reforming, leading to the restoration of stretchable ferroelectrics/piezoelectrics and paving the way for stretchable bioelectronics for full-body motion monitoring. The capabilities highlighted here open avenues for stretchable and self-healing ferroelectric-in-hydrogel bioelectronic technologies.

36 MATERIALS SCIENCE↗