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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Inherent interface defects in thermal (211)Si/SiO{sub 2}:{sup 29}Si hyperfine interaction

Low temperature electron spin resonance (ESR) studies were carried out on ‘higher index’ (211)Si/SiO{sub 2} interfaces thermally grown in the temperature range T{sub ox} = 400–1066°C. The data reveal the presence of two species of a P{sub b}-type interface defect, exhibiting a significant difference in defect density. On the basis of the pertinent ESR parameters and interface symmetry, the basic defect is typified as P{sub b0}{sup (211)}, close to the Pb0 center observed in standard (100)Si/SiO{sub 2}. The dominant type is found to pertain to defected Si atoms at (111)Si-face terraces with the dangling bond along the [111] direction at ∼19.5°C with the interface normal, these sites thus apparently predominantly accounting for interface mismatch adaptation. The total of the P{sub b}-type defect appearance clearly reflects the higher-index nature of the interface. It is found that T{sub ox} = 750°C is required to minimize the P{sub b0}{sup (211)} defect density through relaxation of the oxide (interface). Q-band ESR saturation spectroscopy reveals an anisotropic {sup 29}Si (nuclear spin I=1/2) hyperfine (hf) doublet associated with the central P{sub b0}{sup (211)} Zeeman signal, with hf parameters closest to those of the similar hf structure of the P{sub b0}{sup (110)} defect in thermal (110)Si/SiO{sub 2}, adducing independent support to the P{sub b0}{sup (211)} typification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

The fine structure of electron irradiation induced EL2-like defects in n-GaAs

Defects induced by electron irradiation in n-GaAs have been studied using deep level transient spectroscopy (DLTS) and Laplace DLTS (L-DLTS). The E{sub 0.83} (EL2) is the only defect observed prior to irradiation. Ru/n-GaAs Schottky diodes were irradiated with high energy electrons from a Sr-90 radionuclide up to a fluence of 2.45 × 10{sup 13} cm{sup −2}. The prominent electron irradiation induced defects, E{sub 0.04}, E{sub 0.14}, E{sub 0.38}, and E{sub 0.63}, were observed together with the metastable E{sub 0.17}. Using L-DLTS, we observed the fine structure of a broad base EL2-like defect peak. This was found to be made up of the E{sub 0.75}, E{sub 0.83}, and E{sub 0.85} defects. Our study reveals that high energy electron irradiation increases the concentration of the E{sub 0.83} defect and introduces a family of defects with electronic properties similar to those of the EL2.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Non-dilute helium-related defect interactions in the near-surface region of plasma-exposed tungsten

We report a systematic energetic analysis of helium-related defect interactions that mediate helium (He) segregation on surfaces of plasma-exposed tungsten at different levels of He ion implantation. We focus on high He fluences that increase the He content in the plasma-exposed material well beyond the dilute limit of He concentration and employ atomic configurations generated by large-scale molecular dynamics simulations of He-implanted tungsten. We perform systematic molecular statics computations of cluster– defect interaction energetics in the highly defect-rich near-surface region of plasma-exposed tungsten for small mobile helium clusters as a function of the cluster distance from the surface. In this region, mobile clusters are also subjected to the stress fields generated by defects such as helium bubbles and other clusters, which govern cluster–defect interactions in addition to the cluster–surface interaction. Based on systematic investigation of individual cluster–defect interactions, we develop a mathematical framework to describe the interaction energy landscapes consisting of elastic interaction potential perturbations to finite-width square-well potentials, where the potential well accounts for cluster trapping by the defect at close range and subsequent coalescence and the perturbation potential is parameterized according to elastic inclusion theory. Superposition of all the relevant interaction potentials provides a comprehensive description of the interaction energy landscape that would be experienced by a small mobile cluster along its migration path toward the plasma-exposed surface at high He fluence. Such descriptions are particularly important for developing atomistically-informed, hierarchical multi-scale models of helium cluster dynamics in plasma-facing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thermal conductivity of α -U with point defects

Here, we develop a theoretical model for thermal conductivity of α-U that combines density functional theory calculations and the coupled electron–phonon Boltzmann transport equation. The model incorporates both electron and phonon contributions to thermal conductivity and achieves good agreement with experimental data over a wide temperature range. The dominant scattering mechanism governing thermal transport in α-U at different temperatures is examined. By including phonon–defect and electron–defect scatterings in the model, we study the effect of point defects including U-vacancy, U-interstitial, and Zr-substitution on the thermal conductivity of α-U. The degradation of anisotropic thermal conductivity due to point defects as a function of defect concentration, defect type, and temperature is reported. This model provides insights into the impact of defects on both phonon and electron thermal transport. It will promote the fundamental understanding of thermal transport in α-U and provide a ground for investigation of coupled electron–phonon transport in metallic materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A first-principles investigation of point defect structure and energetics in ThO 2

The structure and energetics of charged point defects in thorium dioxide (ThO 2 ) have been investigated using the density functional theory (DFT) and phonon simulations. DFT simulations were performed under both zero-pressure and constant volume conditions. Termed as the free volume change of the point defects, the change in volume of the supercell has been computed in the zero-pressure case. Supercell expansion was observed with the increase of the (nominal) charge state of anion (O) interstitials and cation (Th) vacancies from neutral to its maximum. On the contrary, contraction of the supercell has been observed with anion vacancies and cation interstitials as the defect charge increases. The supercell volume change with respect to the charge state has been correlated with the resulting defect energetics. It has been observed that, as the defect charge increased, the internal energy and entropy of defect formation of the cation vacancies and anion interstitials were found to increase, while that of the cation interstitials and anion vacancies decreased. Here, the temperature dependence of internal energy and entropy has also been examined. It was found that, as the temperature increases, the internal energies of the formation of cation vacancies and anion interstitials decrease, while those of the cation interstitials and anion vacancies increase. An opposite observation is seen for the entropies of formation defects when above room temperatures.

42 ENGINEERING↗
DOE OSTI2024

Defect control strategies for Al 1- x Gd x N alloys

Tetrahedrally bonded III-N and related alloys are useful for a wide range of applications from optoelectronics to dielectric electromechanics. Heterostructural AlN-based alloys offer unique properties for piezoelectrics, ferroelectrics, and other emerging applications. Atomic-scale point defects and impurities can strongly affect the functional properties of materials, and therefore, it is crucial to understand the nature of these defects and the mechanisms through which their concentrations may be controlled in AlN-based alloys. In this study, we employ density functional theory with alloy modeling and point defect calculations to investigate native point defects and unintentional impurities in Al 1-x Gd x N alloys. Among the native defects that introduce deep midgap states, nitrogen vacancies (V N ⁠) are predicted to be in the highest concentration, especially under N-poor growth conditions. We predict and experimentally demonstrate that V N formation can be suppressed in thin films through growth in N-rich environments. We also find that Al 1-x Gd x N alloys are prone to high levels of unintentional O incorporation, which indirectly leads to even higher concentrations of deep defects. Growth under N-rich/reducing conditions is predicted to minimize and partially alleviate the effects of O incorporation. The results of this study provide valuable insights into the defect behavior in wurtzite nitride-based alloys, which can guide their design and optimization for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of threading dislocations on the V-defect assisted lateral carrier injection and recombination in InGaN quantum well LEDs

The nonuniform hole distribution between InGaN quantum wells (QWs) of light emitting diodes (LEDs) has a negative impact on LED efficiency. The uniformity can be increased by using lateral hole injection through sidewalls of V-defects, which form at threading dislocations. However, the inherent coupling between the V-defects and dislocations might affect efficiency of the hole injection and nonradiative recombination. In this work, we have tested the possible impact of the dislocations on the injection and recombination by means of scanning near-field electroluminescence and photoluminescence spectroscopy on single green-emitting InGaN QW LEDs containing large (∼0.5 μm) V-defects. The measurements have not provided any evidence of a lower hole injection efficiency or enhanced nonradiative recombination at the dislocations located at the V-defect facets or their apexes. This shows that large V-defects are excellent volumetric injectors for long wavelength InGaN LEDs. Furthermore, it was established that V-defects are preferential hole injectors even in single quantum well devices. Compared to vertical injection, the V-defect injection allows lowering the operating voltage, which should contribute to an enhanced wall plug efficiency.

42 ENGINEERING↗
DOE OSTI2016

Carrier dynamics and the role of surface defects: Designing a photocatalyst for gas-phase CO 2 reduction

In 2 O 3-x (OH) y nanoparticles have been shown to function as an effective gas-phase photocatalyst for the reduction of CO 2 to CO via the reverse water–gas shift reaction. Their photocatalytic activity is strongly correlated to the number of oxygen vacancy and hydroxide defects present in the system. To better understand how such defects interact with photogenerated electrons and holes in these materials, we have studied the relaxation dynamics of In 2 O 3-x (OH) y nanoparticles with varying concentration of defects using two different excitation energies corresponding to above-band-gap (318-nm) and near-band-gap (405-nm) excitations. Our results demonstrate that defects play a significant role in the excited-state, charge relaxation pathways. Higher defect concentrations result in longer excited-state lifetimes, which are attributed to improved charge separation. This correlates well with the observed trends in the photocatalytic activity. These results are further supported by density-functional theory calculations, which confirm the positions of oxygen vacancy and hydroxide defect states within the optical band gap of indium oxide. This enhanced understanding of the role these defects play in determining the optoelectronic properties and charge carrier dynamics can provide valuable insight toward the rational development of more efficient photocatalytic materials for CO 2 reduction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗
DOE OSTI2019

Defect-Induced (Dis)Order in Relaxor Ferroelectric Thin Films

The effect of intrinsic point defects on relaxor properties of 0.68 $\mathrm{PbMg_{1/3}Nb_{2/3}O_{3}-0.32 PbTiO_{3}}$ thin films is studied across nearly 2 orders of magnitude of defect concentration via ex post facto ion bombardment. A weakening of the relaxor character is observed with increasing concentration of bombardment-induced point defects, which is hypothesized to be related to strong interactions between defect dipoles and the polarization. Finally, although more defects and structural disorder are introduced in the system as a result of ion bombardment, the special type of defects that are likely to form in these polar materials (i.e., defect dipoles) can stabilize the direction of polarization against thermal fluctuations, and in turn, weaken relaxor behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

First-principles engineering of charged defects for two-dimensional quantum technologies

Charged defects in two-dimensional (2D) materials have emerging applications in quantum technologies such as quantum emitters and quantum computation. The advancement of these technologies requires a rational design of ideal defect centers, demanding reliable computation methods for the quantitatively accurate prediction of defect properties. Here, we present an accurate, parameter-free, and efficient procedure to evaluate the quasiparticle defect states and thermodynamic charge transition levels of defects in 2D materials. Importantly, we solve critical issues that stem from the strongly anisotropic screening in 2D materials, that have so far precluded the accurate prediction of charge transition levels in these materials. Using this procedure, we investigate various defects in monolayer hexagonal boron nitride (h-BN) for their charge transition levels, stable spin states, and optical excitations. We identify C B V N (nitrogen vacancy adjacent to carbon substitution of boron) to be the most promising defect candidate for scalable quantum bit and emitter applications.

2D materials↗
DOE OSTI2018

First-Principles Study of Intrinsic and Extrinsic Point Defects in Lead-Based Hybrid Perovskites.

Intrinsic and extrinsic defects play a major role in determining solar cell efficiencies of lead halide hybrid perovskite absorbers. Here, we present the results of first-principles computations performed on MAPbBr3 (MA = methylammonium) to study the energetics and defect levels of intrinsic point defects, namely vacancy, self-interstitial and anti-site, and extrinsic Pb-substitution defects. While vacancies are the lowest formation energy intrinsic defects and create shallow transition levels, a number of extrinsic defects can have comparable formation energies under desirable chemical potential conditions. therefore, carrier concentrations may be tunable with these substituents. Further, some extrinsic defects create deeper transition levels which can potentially be exploited to enhance solar cell efficiencies via sub-gap absorption.

density functional theory↗
DOE OSTI2015

Correlation between electron-irradiation defects and applied stress in graphene: A molecular dynamics study

Molecular dynamics (MD) simulations are performed to study the correlation between electron irradiation defects and applied stress in graphene. The electron irradiation effect is introduced by the binary collision model in the MD simulation. By applying a tensile stress to graphene, the number of adatom-vacancy (AV) and Stone–Wales (SW) defects increase under electron irradiation, while the number of single-vacancy defects is not noticeably affected by the applied stress. Both the activation and formation energies of an AV defect and the activation energy of an SW defect decrease when a tensile stress is applied to graphene. Applying tensile stress also relaxes the compression stress associated with SW defect formation. These effects induced by the applied stress cause the increase in AV and SW defect formation under electron irradiation.

42 ENGINEERING↗
DOE OSTI2022

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material’s atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterization techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterization, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry of irradiated Ti measures defect densities five times greater than those determined using transmission electron microscopy. Our experiments also reveal two energetically distinct processes where the established annealing model predicts one. Molecular dynamics simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterization techniques and has the potential to uncover new mechanisms behind the evolution of defects in materials.

11 NUCLEAR FUEL CYCLE AND FUEL 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 OSTI2024

Atomistic Probing of Defect-Engineered 2H-MoTe 2 Monolayers

Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe 2 monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe 2 samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe 2 , respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe 2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality.

2H-MoTe 2↗
DOE OSTI2016

Emerging Diluted Ferromagnetism in High- T c Superconductors Driven by Point Defect Clusters

Here, defects in ceramic materials are generally seen as detrimental to their functionality and applicability. Yet, in some complex oxides, defects present an opportunity to enhance some of their properties or even lead to the discovery of exciting physics, particularly in the presence of strong correlations. A paradigmatic case is the high–temperature superconductor YBa 2 Cu 3 O 7–δ (Y123), in which nanoscale defects play an important role as they can immobilize quantized magnetic flux vortices. Here previously unforeseen point defects buried in Y123 thin films that lead to the formation of ferromagnetic clusters embedded within the superconductor are unveiled. Aberration–corrected scanning transmission microscopy has been used for exploring, on a single unit–cell level, the structure and chemistry resulting from these complex point defects, along with density functional theory calculations, for providing new insights about their nature including an unexpected defect–driven ferromagnetism, and X–ray magnetic circular dichroism for bearing evidence of Cu magnetic moments that align ferromagnetically even below the superconducting critical temperature to form a dilute system of magnetic clusters associated with the point defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗