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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Detecting Fermi-level shifts by Auger electron spectroscopy in Si and GaAs

In this paper, changes in surface Fermi-level of Si and GaAs, caused by doping and cleaning, are investigated by Auger electron spectroscopy. Based on the Auger voltage contrast, we compared the Auger transition peak energy but with higher accuracy by using a more accurate detector and an improved peak position determination method. For silicon, a peak shift as large as 0.46 eV was detected when comparing a cleaned p-type and n-type wafer, which corresponds rather well with the theoretical difference in Fermi-level. If no cleaning was applied, the peak position did not differ significantly for both wafer types, indicating Fermi-level pinning in the band gap. For GaAs, peak shifts were detected after cleaning with HF and (NH4)2S-solutions in an inert atmosphere (N2-gas). Although the (NH4)2S-cleaning in N2 is very efficient in removing the oxygen from the surface, the observed Ga- and As-peak shifts are smaller than those obtained after the HF-cleaning. It is shown that the magnitude of the shift is related to the surface composition. After Si-deposition on the (NH4)2S-cleaned surface, the Fermi-level shifts back to a similar position as observed for an as-received wafer, indicating that this combination is not successful in unpinning the Fermi-level of GaAs. This work has been funded by J.D.'s PhD fellowship of the Fund of Scientific Research-Flanders (FWO-V) (Dossier No. 11U4516N). P.H. acknowledges support from Becas Chile-CONICYT. This research was also supported by the FWO Odysseus Program, the Belgian Hercules Stichting with the Project No. Her/08/25 and AKUL/13/19 and the KU Leuven project GOA "Fundamental challenges in Semiconductor Research". The authors would also like to thank Bastiaan Opperdoes and Ludwig Henderix for technical support. The work was supported by the U.S. Department of Energy (USDOE), Office of Science, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, and performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). Battelle operates PNNL for the USDOE under contract DE-AC05-76RL01830.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2025

DOE ART Graphite R&D Program

Graphite overview: Material science, supply chain facts, nuclear material science, licensing thoughts

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Engineered Living Materials For Sustainability

Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Lastly, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Chapter 8: Materials for Exploration Systems

Materials science and processing research in space can be thought of as a field of study that began with the sounding rocket experiments in the 1950s. Material science studies of the lunar surface materials returned during the Apollo missions enabled the study of lunar resource utilization. The study of materials science and processing in space continued with over 30 years of microgravity materials processing research which continues today in the International Space Station. These studies are the technical foundation that could enable lower cost human exploration through the use of in-situ propellant production, the production of energy from space resources, and the eventual establishment of a substantial portion of humanity living self sufficiently off Earth.

Curreri, Peter A.↗
DOE OSTI2018

On the nexus between atom probe microscopy and density functional theory simulations

The capability of precisely determining the atomic identity and position, coupled with an understanding of the inter-atomic interactions is crucial to enabling microstructure-property relationships in modern materials science. Two approaches to the investigation of materials at the atomic-scale are atom probe microscopy (APM) and density functional theory (DFT). On the one hand, APM offers an ultimate in 3D elemental analysis by determining the chemical identity and atomic position in a material. On the other, first principles DFT simulations allow the prediction of atomic structure and, critically, the electronic structure which is the gateway to calculating the real-world properties of materials. Both approaches are undertaken at the atomic-scale, and so are intrinsically complementary. This paper provides a review of the opportunities and challenges in combining these approaches to do materials science. We first summarise the status of these approaches and set out a framework for the current main workflows by which APM experiments inform DFT simulations and vice-versa. We point out certain gaps between the approaches and ways that these may be bridged via a series of case-studies organised into the aforementioned workflow framework.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automated generation and ensemble-learned matching of X-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. Here, we will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Vapor Growth of ZnSe-based Compound Semiconductors in Low Gravity environment on International Space Station

Six low-gravity experiments of crystal growth of ZnSe-based compound semiconductors by physical vapor transport (PVT) are scheduled to be processed inside Low Gradient Furnace (LGF) on Materials Science Research Rack (MSRR) in Material Science Laboratory (MSL) on International Space Station (ISS) starting from February 27th, 2023. The flight samples consist of three unseeded growths of ZnSe, Cr-doped ZnSe and ZnSeTe as well as growth of ZnSe on top of three single crystal ZnSe seeds with orientation of (100), (110) and (111). The main scientific objective of the flight experiments is to establish the effects of gravity-driven fluid flows on Earth to (i) the non-uniform incorporation of impurities and defects and (ii) the deviation from stoichiometry observed in the grown crystals caused by growth interface fluctuations and evolution as the results of buoyancy-driven convection and irregular fluid-flows. The investigation will also evaluate the effects of gravity on the PVT process by examining (i) the growth kinetics on various seed orientations, (ii) the dopant segregation and distribution in the Cr doped ZnSe, and (iii) the compositional segregation and distribution in the ternary ZnSeTe crystal grown by PVT. Additionally, from X-ray diffraction/topography and by measuring the density and distribution of various crystalline defects it will assess the self-induced strain effects developed during processing at elevated temperatures caused by the weight of the crystals. During the ground-based preparations, the following tasks have been accomplished: • Establish the thermodynamic properties of the ZnSe binary and related ternary systems by measuring the partial pressures of the vapor phase coexisting with the condensed phases and perform phase diagram calculations for comparison. • Evaluate the effects of gravity-driven convection in the growth process by performing experiments under various vapor transport orientations relative to gravity vector, i.e., horizontal, vertically stabilized and destabilized configurations. • Establish a quantitative correlation between growth parameters and the characteristics and properties of the grown crystals by characterizing the crystals using a variety of techniques. • Evaluate the fundamentals of the current theories on vapor transport and crystal growth kinetics by performing in-situ and real-time monitoring techniques during growth. • Develop a one-dimensional diffusion limited theory of mass transport and two- and three-dimensional numerical simulations of both mass transport and heat transfer and thus establish the microgravity requirements for the flight experiments and to predict the characteristics of the grown crystals such as the solid-vapor interface shapes… etc. and establish a fundamental understanding of the crystal growth process from the comparison with experimental results.

ZnSe↗
DOE OSTI2013

Exascale Co-Design Center for Materials in Extreme Environments (ExMatEx) Annual Report - Year 2

All activities of the Exascale Co-design Center for Materials in Extreme Environments (Ex- MatEx) are focused on the two ultimate goals of the project: (1) demonstrating and delivering a prototype scale-bridging materials science application based upon adaptive physics refinement, and (2) identifying the requirements for the exascale ecosystem that are necessary to perform computational materials science simulations (both single- and multi-scale). During the first year of ExMatEx, our focus was on establishing how we do computational materials science, by developing an initial suite of flexible proxy applications. These “proxy apps” are the primary vehicle for the co-design process, involving assessments and tradeoff evaluations both within the ExMatEx team, and with the entire exascale ecosystem. These interactions have formed the basis of our second year activities. The set of artifacts from these co-design interactions are the lessons learned, that are used to re-express the applications and algorithms within the context of emerging architectures, programming models, and runtime systems.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Structure classification and melting temperature prediction in octet AB solids via machine learning

Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (r σ ,r π ), suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92%. Using just r σ and the excess Born effective charge ΔZ A of the A atom enabled an average success of 97%, but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [ 89%, 95%] and the accuracy of the new pair lies in the interval [96 %, 99%]. For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our resulting model to be 11% of the mean melting temperature of the data, but we note that if the accuracy of this predicted error is itself measured, our estimated fitting error itself has a root-mean-square error of 50%. In short, what we illustrate is that classification and regression predictions can vary significantly, depending on the details of how machine learning methods are applied to small data sets. This variation makes it important, if not essential, to average the predictions and compute confidence intervals about these averages to report results meaningfully. However, when properly used, these statistical methods can advance our understanding and improve predictions of material properties even for small data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Materials Characterization using the Facilities of the Center for Synchrotron Radiation Research and Instrumentation (CSRRI), a NSUF Partner Facility

Advanced nuclear energy systems are envisioned to operate at high temperatures, high burn-up, harsh neutron irradiation, and corrosive environments. New materials and nuclear fuels with improved performance, safety and reliability are needed for the increasingly demanding operating conditions. The fundamental challenge in developing new materials with improved irradiation, temperature, and corrosion resistance is to understand and control chemical and physical phenomena in complex systems over multiple time and length scales. Solving these scientific and technical challenges will require an integrated approach of all available computational and experimental capabilities. This talk will focus on the application of synchrotron radiation techniques to characterize materials of interest to nuclear material science at the facilities operated by the Center For Synchrotron Radiation Research and Instrumentation (CSRRI) at the Illinois Institute of Technology. The CSRRI is a partner facility with the U. S. Department of Energy's Nuclear Science User Facility (NSUF). Using the CSRRI facilities, such as the Materials Research Collaborative Access Team (MRCAT) beamline, we can study the materials science of irradiated and model materials to improve the understanding of the performance of materials in nuclear reactors. (author)

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unconventional excitonic states with phonon sidebands in layered silicon diphosphide

Complex correlated states emerging from many-body interactions between quasiparticles (electrons, excitons and phonons) are at the core of condensed matter physics and material science. In low-dimensional materials, quantum confinement affects the electronic, and subsequently, optical properties for these correlated states. Here, by combining photoluminescence, optical reflection measurements and ab initio theoretical calculations, we demonstrate an unconventional excitonic state and its bound phonon sideband in layered silicon diphosphide (SiP 2 ), where the bound electron–hole pair is composed of electrons confined within one-dimensional phosphorus–phosphorus chains and holes extended in two-dimensional SiP 2 layers. The excitonic state and emergent phonon sideband show linear dichroism and large energy redshifts with increasing temperature. Our ab initio many-body calculations confirm that the observed phonon sideband results from the correlated interaction between excitons and optical phonons. With these results, we propose layered SiP2 as a platform for the study of excitonic physics and many-particle effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray diffraction technique

This is a guest lecture for University of Idaho graduate-level course Chemical and Materials Analysis. I will be talking about the fundamentals of X-ray Diffraction technique and some examples for its application in material science research.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Analyzing machine learning models to accelerate generation of fundamental materials insights

Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

DMMSC Talk

The new monthly Dynamic Mesoscale Material Science Capability Seminar Series features Lab highlights from materials science areas, including cutting-edge research at national and international light sources, in-depth investigations of technical gaps, and advanced studies of accelerator technologies that might provide probes in this region.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of Radiation-Induced Defects on Corrosion

The next generation of nuclear reactors will expose materials to conditions that, in some cases, are even more extreme than those in current fission reactors, inevitably leading to new materials science challenges. Radiation-induced damage and corrosion are two key phenomena that must be understood both independently and synergistically, but their interactions are often convoluted. In the light water reactor community, a tremendous amount of work has been done to illuminate irradiation-corrosion effects, and similar efforts are under way for heavy liquid metal and molten salt environments. While certain effects, such as radiolysis and irradiation-assisted stress corrosion cracking, are reasonably well established, the basic science of how irradiation-induced defects in the base material and the corrosion layer influence the corrosion process still presents many unanswered questions. In this review, we summarize the work that has been done to understand these coupled extremes, highlight the complex nature of this problem, and identify key knowledge gaps.

36 MATERIALS SCIENCE↗
DOE OSTI2023

C-band high gradient research and facilities at Los Alamos National Laboratory [Slides]

Achieving high-gradient performance (low breakdown rates, low field emission, new materials for HOM absorption, cathodes at high gradient etc.) is a materials science problem. Los Alamos is, at core, a materials science laboratory with particular expertise in metallurgy. Los Alamos also considers itself the steward of accelerator science for the NNSA part of the DOE complex. Thus, Los Alamos has both an institutional interest in, and capability to address, this problem space. High gradient C-band work directly aligns with future NNSA and LANL missions.

36 MATERIALS SCIENCE↗