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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics

The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dielectric screening and vacancy formation for large neutral and charged Si n H m ( n > 1500 ) nanocrystals using real-space pseudopotentials

A commonly used procedure for computing the properties of defects in crystalline materials is to consider a large supercell that includes the defect of interest. This is a straightforward technique as standard energy band codes can be used for such computations. For neutral defects, the only impediment of such an approach is to avoid defect-defect interactions between adjoining cells. However, this procedure can be complex if the defect of interest is charged as the system at large contains Coulombic divergences. Moreover, some have recently argued that the conventional definition of formation energies for charged defects cannot be reconciled with statistical mechanics. Here, we focus on an alternative approach. We consider large nanocrystals wherein a charged defect can be placed. Since the system is confined, a charged defect within the nanocrystal does not result in a Coulombic divergence. The chief impediment is computational, i.e., while no defect-defect or Coulombic divergences are present, the nanocrystal must be sufficiently large to allow the system to properly replicate a bulklike configuration. With the development of new algorithms and hardware advances, computations for systems of sufficient size to address this issue are feasible. In particular, we solve the Kohn-Sham equation in real space using pseudopotential-density-functional theory for large silicon nanocrystals, which contain thousands of atoms. Further, we focus on (i) the screening of a point charge and (ii) the formation of a charged vacancy in hydrogen-terminated silicon nanocrystals. This approach allows us to examine the role of quantum confinement in addition to exploring the bulk limit. Comparisons to other methods confirm the viability of this approach.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Low-Frequency Noise and Deep Level Transient Spectroscopy in n-p-n Si Bipolar Junction Transistors Irradiated with Si Ions

The properties of defects in n-p-n Si bipolar junction transistors (BJTs) caused by 17 MeV Si ions are investigated via current-voltage, low-frequency noise, and deep-level transient spectroscopy (DLTS) measurements. Four prominent radiation-induced defects in the base-collector junction of these transistors are identified via DLTS. At least two defect levels are observed in temperature-dependent low-frequency 1/ f noise measurements, one that is similar to a prominent defect in DLTS and another that is not. Defect microstructures are discussed. Here our results show that DLTS and 1/ f noise measurements can provide complementary information about defects in linear bipolar devices.

42 ENGINEERING↗
DOE OSTI2024

Theory of topological defects and textures in two-dimensional quantum orders with spontaneous symmetry breaking

In this article, we consider two-dimensional (2d) quantum many-body systems with long-range orders, where the only gapless excitations in the spectrum are Goldstone modes of spontaneously broken continuous symmetries. To understand the interplay between classical long-range order of local order parameters and quantum order of long-range entanglement in the ground states, we study the topological point defects and textures of order parameters in such systems. We show that the universal properties of point defects and textures are determined by the remnant symmetry enriched topological order in the symmetry-breaking ground states with a nonfluctuating order parameter, and provide a classification for their properties based on the inflation-restriction exact sequence. We highlight a few phenomena revealed by our theory framework. First, in the absence of intrinsic topological orders, we show a connection between the symmetry properties of point defects and textures to deconfined quantum criticality. Second, when the symmetry-breaking ground state has intrinsic topological orders, we show that the point defects can permute different anyons when braided around. They can also obey projective fusion rules in the sense that multiple vortices can fuse into an Abelian anyon, a phenomenon for which we coin “defect fractionalization.” Finally, we provide a formula to compute the fractional statistics and fractional quantum numbers carried by textures (skyrmions) in Abelian topological orders.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Effect of Cr and Al on Elastic Constants of FeCrAl Alloys Investigated by Molecular Dynamics Method

The FeCrAl alloy system is recognized as one of the candidate materials for accident-tolerant fuel (ATF) cladding in the nuclear power industry due to its high oxidation resistance under irradiation and high-temperature environments. The concentrations of Cr and Al have a significant effect on elastic properties of the FeCrAl alloy. In this work, elastic constants C 11 , C 12 , C 44 , bulk modulus and shear modulus of FeCrAl alloy were calculated with molecular dynamics methods. We explored compositions with 1–15 wt.% Cr and 1–5 wt.% Al at temperatures from 0 K to 750 K. The results show that the concentrations of Al and Cr have different effects on the elastic constants. When the concentration of Al was fixed, a decrease in bulk modulus and shear modulus with increasing Cr content was observed, consistent with previous experimental results. The dependence of elastic constants on temperature was also the same as in the experiments. Investigations into elastic properties of defect-containing alloys have shown that vacancies, voids, interstitials and Cr-rich precipitations have different effects on elastic properties of FeCrAl alloys. Investigations of elastic properties of defect-containing alloys have shown that vacancies, void, interstitials and Cr-rich precipitations have different effects on elastic properties of FeCrAl alloys. Therefore, the present results indicate that both the Cr and Al concentrations and radiation defects should be considered to develop and apply the FeCrAl alloy in ATF design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microscopic examination of rf-cavity-quality niobium films through local nonlinear microwave response

The performance of superconducting radio-frequency (SRF) cavities is sometimes limited by local defects. To investigate the rf properties of these local defects, especially those that nucleate rf magnetic vortices, a near-field magnetic microwave microscope is employed. Local third-harmonic response ( P 3 f ) and its temperature dependence and rf power dependence are measured for one Nb / Cu film grown by direct current magnetron sputtering (DCMS) and six Nb / Cu films grown by high-power impulse magnetron sputtering (HiPIMS) with systematic variation of deposition conditions. Five out of the six HiPIMS Nb / Cu films show a strong third-harmonic response that is likely coming from rf vortex nucleation due to a low- T c surface defect with a transition temperature between 6.3 and 6.8 K, suggesting that this defect is a generic feature of air-exposed HiPIMS Nb / Cu films. A phenomenological model of surface-defect grain boundaries hosting a low- T c impurity phase is introduced and studied with time-dependent Ginzburg-Landau (TDGL) simulations of probe-sample interaction to better understand the measured third-harmonic response. The simulation results show that the third-harmonic response of rf vortex nucleation caused by surface defects exhibits the same general features as the data, including peaks in third-harmonic response with temperature, and their shift and broadening with higher microwave amplitude. We find that the parameters of the phenomenological model (the density of surface defects that nucleate rf vortices and the depth an rf vortex travels through these surface defects) vary systematically with film deposition conditions. From the point of view of these two properties, the Nb / Cu film that is most effective at reducing the nucleation of rf vortices associated with surface defects can be identified. Published by the American Physical Society 2024

Wang, Chung-Yang (ORCID:0000000184299633)↗
DOE OSTI2022

Effect of tool design and pass strategy on defect elimination and uniform, enhanced tensile properties of friction stir processed high-pressure die-cast A380 alloy

This work reports implementation of friction stir processing (FSP) to locally modify microstructure and create a defect-free processing zone with the aid of different tool designs and altering the passing strategy for two different process parameter combinations. Here, FSP is applied on high pressure die-cast (HPDC) A380 alloy, a widely used Al-Si alloy fabricated in the die casting sector for automotive and aerospace applications. However, the presence of gas and shrinkage porosity, brittle needle-shaped Fe-containing ß-FeSiAl 5 intermetallic compounds, Al-dendrites, coarse and acicular silicon particles, and large second-phase particulates have a detrimental influence on the uniformity of tensile properties across the thickness direction of an HPDC plate. FSP is able to eliminate porosity, refine microstructure and improve tensile properties. An FSP tool design and a passing strategy are identified for the best microstructure consolidation and uniform enhanced tensile properties. This study noted the importance of tool features such as scroll design and flats on tool pin and multiple pass orientation strategy to find best combination of defect-free refined microstructure and uniform tensile strength and ductility across the processing zone of HPDC Al alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ab initio calculations for void swelling bias in α - and δ -plutonium

Void swelling can develop in materials under persistent irradiation when nonequilibrium vacancy and self-interstitial populations migrate under sufficiently asymmetric interaction biases. In conventional metals, the propensity is determined to the first approximation by comparing point-defect relaxation strains. We thus present DFT-based calculations of structures and formation energies and volumes of point defects in the α and the δ phases of plutonium. Here we discuss the pros and cons of various levels of electronic structure theory: spin polarization, spin-orbit coupling, and orbital polarization. Our results show that lattice defects in δ-Pu, in contrast to most fcc metals, have surprisingly small formation volumes. Equally unexpected are the large defect formation volumes found in the low-symmetry α-Pu phase. Both these unusual properties can be satisfactorily explained from defect-induced spin/orbital moment formation and destruction in the Pu phases. Surprisingly, the point defects in α-Pu are found to induce far larger transformation of the local electronic structure than in δ-Pu. When we use the calculated defect properties to estimate the classic void swelling bias in each of the phases, we find it to be unusually small in δ-Pu but likely much larger in α-Pu. Hence, swelling rates and mechanisms can diverge dramatically between the different phases of Pu. Especially in the transient regime before the formation of large defect clusters, the swelling rate of α-Pu can reliably be expected to be much larger than δ-Pu. However, accurate forecasts over longer times will require the conventional void-swelling theory to be modified to handle the complexities presented by the different Pu phases. As a case in point, we show the possible anomalous temperature dependence of vacancy properties in δ-Pu, caused by entropic contributions from defect-induced spin-lattice fluctuations. Such complications may affect defect-defect interactions and thus alter the void swelling bias.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2022

Investigation of the effect of structural defects from hydride precipitation on superconducting properties of high purity SRF cavity Nb using magneto-optical and electron imaging methods

Abstract Nb is an elemental superconductor with a critical temperature of 9.3 K and is widely used to fabricate superconducting radiofrequency (SRF) cavities for particle accelerators. However, microstructural defects in Nb, such as grain boundaries (GBs) and dislocations, can act as pinning centers for magnetic flux that can degrade SRF cavity performance. Hydrogen contamination is also detrimental to SRF cavity performance due to the formation of normal conducting hydrides during cool down. In this study, disc shaped Nb bi-crystals extracted from high-purity large-grain Nb slices were investigated to study the effects of GBs, hydrogen, and dislocations on superconducting properties. Grain orientation and GB misorientation were measured using Laue x-ray diffraction and electron backscattered diffraction (EBSD) analyses. Cryogenic magneto-optical imaging was used to directly observe magnetic flux penetration below T c = 9.3 K. Damage caused by low temperature precipitation of hydrides and their dissolution upon reheating after cryogenic cycles was examined using electron channeling contrast imaging, and EBSD. The relationships between hydride formation, dislocation content, GBs, cryo-cooling, heat treatment (HT), and flux penetration indicate that both GB character and hydrogen content affect magnetic flux penetration. Such flux penetration could be facilitated by dislocation structures and low angle GBs resulting from hydride precipitation and HT.

36 MATERIALS SCIENCE↗
DOE OSTI2023

First-Principles Investigation of Near-Surface Divacancies in Silicon Carbide

The realization of quantum sensors using spin defects in semiconductors requires a thorough understanding of the physical properties of the defects in the proximity of surfaces. We report a study of the divacancy (V Si V C ) in 3C-SiC, a promising material for quantum applications, as a function of surface reconstruction and termination with -H, -OH, -F and oxygen groups. Here we show that a V Si V C close to hydrogen-terminated (2 x 1) surfaces is a robust spin-defect with a triplet ground state and no surface states in the band gap and with small variations of many of its physical properties relative to the bulk, including the zero-phonon line and zero-field splitting. However, the Debye-Waller factor decreases in the vicinity of the surface and our calculations indicate it may be improved by strain-engineering. Overall our results show that the V Si V C close to SiC surfaces is a promising spin defect for quantum applications, similar to its bulk counterpart.

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

Point defects in CdTe and CdSeTe alloy: a first principles investigation with DFT+U

CdTe and its alloy CdSeTe are widely used in optoelectronic devices, such as radiation detectors and solar cells, due to their superior electrical properties. However, the formation of defects and defect complexes in these materials can significantly affect their performance. As a result, understanding the defect formation and recombination processes in CdTe and CdSeTe alloy is of great importance. In recent years, density functional theory (DFT) calculations have emerged as a powerful tool for investigating the properties of defects in semiconductors. In this paper, we use DFT+U calculations to comprehensively study the properties of intrinsic defects as well as extrinsic defects induced by commonly used dopants, such as Cu and group V elements, in CdTe and CdSeTe alloy. This work provides insights into the effects of these defects on the electrical and optical properties of the material.

14 SOLAR ENERGY↗
DOE OSTI2024

Tutorial: Defects in topological semimetals

Three-dimensional topological semimetals are a class of electronic materials in which their bulk and surface states contain linear band touching nodes near the Fermi level. Like semiconductors, their properties will be affected by point and extended defects in their crystal structures, although the extent to which defects and disorders influence topological semimetals may differ in key ways due to their unique electronic structures. In this Tutorial, we provide an overview of the defects in topological semimetals, covering both computational and experimental methods for exploring defect-property relationships. We also include a discussion on open questions that still need to be explored further.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamical Scaling Reveals Topological Defects and Anomalous Evolution of a Photoinduced Phase Transition

Nonequilibrium states of quantum materials can exhibit exotic properties and enable unprecedented functionality and applications. These transient states are inherently inhomogeneous, characterized by the formation of topologically protected structures, requiring nanometer spatial resolution on femtosecond timescales to resolve their evolution. Using ultrafast total x-ray scattering at a free electron laser and a sophisticated scaling analysis, we gain unique access to the dynamics on the relevant mesoscopic length scales. Our results provide direct evidence that ultrafast excitation of LaTe 3 leads to formation of topological vortex strings of the charge density wave. These dislocations of the charge density wave exhibit anomalous, subdiffusive dynamics, slowing the equilibration process, providing rare insight into the nonequilibrium mesoscopic response in a quantum material. Our findings establish a general framework to investigate properties of topological defects, which are expected to be ubiquitous in nonequilibrium phase transitions and may arrest equilibration and enhance competing orders.

Orenstein, Gal [SLAC National Accelerator Laborato↗
DOE OSTI2023

Dynamical Scaling Reveals Topological Defects and Anomalous Evolution of a Photoinduced Phase Transition

Nonequilibrium states of quantum materials can exhibit exotic properties and enable unprecedented functionality and applications. These transient states are inherently inhomogeneous, characterized by the formation of topologically protected structures, requiring nanometer spatial resolution on femtosecond timescales to resolve their evolution. Using ultrafast total x-ray scattering at a free electron laser and a sophisticated scaling analysis, we gain unique access to the dynamics on the relevant mesoscopic lengthscales. Our results provide direct evidence that ultrafast excitation of LaTe$_3$ leads to formation of topological vortex strings of the charge density wave. These dislocations of the charge density wave exhibit anomalous, subdiffusive dynamics, slowing the equilibration process, providing rare insight into the nonequilibrium mesoscopic response in a quantum material. Our findings establish a general framework to investigate properties of topological defects, which are expected to be ubiquitous in nonequilibrium phase transitions and may arrest equilibration and enhance competing orders.

FOS: Physical sciences↗