Search NASA⌕ Search

SEARCH · Search NASA

Results for “defectivity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation

In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughput.

Yin, Keyi↗
DOE OSTI2023

Defect Recognition for Eddy Current Testing of Spent Nuclear Fuel Canister using Convolutional Neural Network

This paper proposes an accurate and robust defect detection solution for 304L and 306L stainless steel (SS) weld. In the proposed solution, Eddy current testing (ECT) is employed to generate 2-dimensional (2D) data for samples under test with defects. The 2D data can be treated as images for deep learning-based defect detection. Since convolutional neural networks (CNNs) are powerful in processing images, CNN is employed in this study for defect detection. Experiments are conducted on a submerged arc welding (SAW) 304L SS weld sample with an artificial crack generated by waterjet cutting. The ECT data on this seeded fault sample is utilized to verify the proposed solution. For this purpose, the ECT measurement are separated as from Fault area and Normal area, which are used for CNN training. After training, the testing data is used for verification. Experimental results demonstrate the feasibility and effectiveness of the proposed solution.

Niu, Guangxing↗
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
DOE OSTI2023

A Micro-scale Numerical Investigation of Internal and Interfacial Void Defects in Adhesive on Failure Behavior of Adhesively-Bonded Materials with Rough Surfaces

This paper studied the effects of air void defects on the failure behavior of adhesively-bonded materials under global shear via micro-scale computational modeling. The numerical results indicated that interfacial void defects can largely facilitate interfacial debonding of a weaker adherend/adhesive interface under shear. However, this is not the case for a stronger adherend/adhesive interface, showing the reduction on the nominal shear strength of an adhesive joint is mainly due to internal void defects. The reduced bonding performance due to voids can be improved by leveraging an appropriate surface roughness. This preliminary investigation is a first step to better understand the micro-mechanics of interfacial failure in the adhesion of a roughened/patterned adherend via surface modification(s) and an adhesive, and also shows the importance of minimizing interfacial void defects in particular at a weaker adherend/adhesive interface via different techniques.

Qiao, Yao↗
DOE OSTI2022

Logic operations with active topological defects

Logic operations performed by semiconductor-based transistors are the basis of modern computing. There is considerable interest in creating autonomous materials systems endowed with the capability to make decisions. In this work, we introduce the concept of using topological defects in active matter to perform logic operations. When an extensile active stress in a nematic liquid crystal is turned on, +1/2 defects can self-propel, in analogy to electron transport under a voltage gradient. By relying on hydrodynamic simulations of active nematics, we demonstrate that patterns of activity, when combined with surfaces imparting certain orientations, can be used to control the formation and transport of +1/2 defects. We further show that asymmetric high- and low-activity patterns can be used to create effective defect gates, tunnels, and amplifiers. The proposed active systems offer the potential to perform computations and transmit information in active soft materials, including actin-, tubulin-, and cell-based systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

One-dimensional alignment of defects in a flexible metal-organic framework

Crystalline materials are often considered to have rigid periodic lattices, while soft materials are associated with flexibility and nonperiodicity. The continuous evolution of metal-organic frameworks (MOFs) has erased the boundaries between these two distinct conceptions. Flexibility, disorder, and defects have been found to be abundant in MOF materials with imperfect crystallinity, and their intricate interplay is poorly understood because of the limited strategies for characterizing disordered structures. Here, we apply advanced nuclear magnetic resonance spectroscopy to elucidate the mesoscale structures in a defective MOF with a semicrystalline lattice. We show that engineered defects can tune the degree of lattice flexibility by combining both ordered and disordered compartments. The one-dimensional alignment of correlated defects is the key for the reversible topological transition. The unique matrix is featured with both rigid framework of nanoporosity and flexible linkage of high swellability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Defects vibrations engineering for enhancing interfacial thermal transport in polymer composites

To push upper boundaries of thermal conductivity in polymer composites, understanding of thermal transport mechanisms is crucial. Despite extensive simulations, systematic experimental investigation on thermal transport in polymer composites is limited. To better understand thermal transport processes, we design polymer composites with perfect fillers (graphite) and defective fillers (graphite oxide), using polyvinyl alcohol (PVA) as a matrix model. Measured thermal conductivities of ~1.38 ± 0.22 W m -1 K -1 in PVA/defective filler composites is higher than those of ~0.86 ± 0.21 W m -1 K -1 in PVA/perfect filler composites, while measured thermal conductivities in defective fillers are lower than those of perfect fillers. We identify how thermal transport occurs across heterogeneous interfaces. Thermal transport measurements, neutron scattering, quantum mechanical modeling, and molecular dynamics simulations reveal that vibrational coupling between PVA and defective fillers at PVA/filler interfaces enhances thermal conductivity, suggesting that defects in polymer composites improve thermal transport by promoting this vibrational coupling.

42 ENGINEERING↗
DOE OSTI2017

Defect specific luminescence dead layers in CdS and CdSe

CdS and CdSe are often used in optoelectronic devices whose effectiveness is may be dictated by defects in the near surface region. Luminescence is one of the main tools for studying such defects. The energy dependence of the x-ray excited optical luminescence (XEOL) spectra of these materials enables the extraction of the depth dependence of the defect distribution. Normal and time-gated XEOL spectra were obtained from these materials in the energy range 600 to 1500 eV. Here, we find that the results can best be understood in terms of a luminescence dead layer whose width depends on the position of the defect level in the band gap.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of as-received defects on ceramic matrix composites properties using high-fidelity microstructures with periodic boundary conditions

The presence of microstructural defects in as-received specimens of ceramic matrix composites (CMCs) significantly influences their constitutive response and damage, highlighting the importance of characterization and quantification of these defects for accurate assessment of damage and failure in the service environment. In a recent effort, the authors developed an algorithm to generate stochastic representative volume elements (SRVEs) of Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs based on extensive multiscale material and defect characterization data. This paper implements this algorithm within a commercial finite element solver with periodic boundary conditions (PBCs) for high-fidelity micromechanics analysis and investigation of macroscopic material behavior of C/SiNC composites. Different loading directions are used to predict the global mechanical properties, and the results are in excellent agreement with theoretical (rule of mixture) predictions. Subsequently, the effects of as-received defects on the global and local responses are investigated. The results show that intratow porosity has pronounced degradation effects on the global elastic properties and results in complex stress localization patterns, which can be attributed to potential damage initiation sites.

Khafagy, Khaled H.↗
DOE OSTI2017

The role of defects in laser-induced modifications of silica coatings and fused silica using picosecond pulses at 1053 nm: I Damage morphology

Laser-induced damage with ps pulse widths straddles the transition from intrinsic, multi-photon ionization and avalanche ionization-based ablation with fs pulses to defect-dominated, thermal-based damage with ns pulses. We investigated the morphology of damage for fused silica and silica coatings between 1 ps and 60 ps at 1053 nm. Using calibrated laser-induced damage experiments, in situ imaging, and high-resolution optical microscopy, atomic force microscopy, and scanning electron microscopy, we show that defects play an important role in laser-induced damage down to 1 ps. Three types of damage are observed: ablation craters, ultra-high density pits, and smooth, circular depressions with central pits. For 10 ps and longer, the smooth, circular depressions limit the damage performance of fused silica and silica coatings. The observed high-density pits and material removal down to 3 ps indicate that variations in surface properties limit the laser-induced damage onset to a greater extent than expected below 60 ps. Below 3 ps, damage craters are smoother although there is still evidence as seen by AFM of inhomogeneous laser-induced damage response very near the damage onset. These results show that modeling the damage onset only as a function of pulse width does not capture the convoluted processes leading to laser induced damage with ps pulses. It is necessary to account for the effects of defects on the processes leading to laser-induced damage. In conclusion, the effects of isolated defects or inhomogeneities are most pronounced above 3 ps but are still discernible and possibly important down to the shortest pulse width investigated here.

42 ENGINEERING↗
DOE OSTI2023

High external quantum efficiency (6.5%) InGaN V-defect LEDs at 600 nm on patterned sapphire substrates

Highly efficient long-wavelength InGaN LEDs have been a research focus in nitride LEDs for their potential applications in displays and solid-state lighting. A key breakthrough has been the use of laterally injected quantum wells via naturally occurring V-defects which promote hole injection through semipolar sidewalls and help to overcome the barriers to carrier injection that plague long wavelength nitride LEDs. In this article, we study V-defect engineered LEDs on (0001) patterned sapphire substrates (PSS) and GaN on (111) Si. V-defects were formed using a 40-period InGaN/GaN superlattice and we report a packaged external quantum efficiency (EQE) of 6.5% for standard 0.1 mm 2 . LEDs on PSS at 600 nm. We attribute the high EQE in these LEDs to lateral injection via V-defects.

Ewing, Jacob J.↗
DOE OSTI2026

Optical suppression of defect-related photoluminescence in GeSn

Reports of photoluminescence from GeSn grown on Ge substrates by molecular beam epitaxy have been limited. We find that one limiting factor to observing photoluminescence is due to localized defect states marked by photoluminescence at 2400 nm and originating from the Ge substrate and buffer layer. In this study, we report on an optical study utilizing doped Ge(001) substrates to effectively suppress defect-related photoluminescence in GeSn layers by filling localized defect trap states. For this experiment, a GeSn layer with Sn content up to 10.5 ± 0.5% was grown on a doped Ge(001) substrate. Analysis of the physics of the photoluminescence spectrum collected from the GeSn thin film shows an emission at the expected wavelength of 2300 nm for 10.5 ± 0.5% Sn content and the absence of the typically observed defect-related signal at 2400 nm. This understanding is further confirmed using short-pulse optical excitation of the GeSn grown on undoped Ge substrates.

Chemical vapor deposition↗
DOE OSTI2026

Point defects in semiconductors: Friends and foes for quantum technologies

Point defects in semiconductors are both a curse and a blessing in microelectronics: they enable the control of electrical conductivity through doping, yet can also act as trapping and recombination centers that degrade device performance. In quantum information science, defects play a similarly dual role. They can be harnessed as spin–photon interfaces enabling the coupling of electronic and nuclear spins to light and the creation of distributed entanglement for quantum networks or used as atomistic scale sensors for quantum sensing. At the same time, defects are a major source of decoherence for superconducting qubits, one of the leading quantum computing platforms. This article discusses how a deeper materials-level understanding of defects can guide the design of improved quantum devices for communication, sensing, and computation.

Zhu, Yizhi [Rice University, Houston, TX (United S↗
DOE OSTI2023

Charged point defect benchmark of Hybrid and GGA-PBE

Data used by the publication "High-throughput calculations of charged point defect properties with semi-local density functional theory - performance benchmarks for materials screening applications." This work presented an in-depth benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We considered three different a-posteriori correction sets for semi-local calculations, implemented in a fully automated workflow, and consider the qualitative and quantitative differences for four different categories of defect information: thermodynamic transition levels, formation energies, fermi levels, and dopability limits. We highlighted the type of qualitative information about point defect properties that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy that can be achieved by these approaches.

Broberg, Danny↗
DOE OSTI2018

Modelling Charged Defects in Non-Cubic Semiconductors for Radiation Effects Studies in Next Generation Materials

This final report summarizes the results of the Laboratory Directed Research and Development (LDRD) Project Number 212587 entitled "Modeling Charged Defects in Non-Cubic Semiconductors for Radiation Effects Studies in Next Generation Electronic Materials" . The goal of this project was to extend a predictive capability for modeling defect level energies using first principle density functional theory methods (e.g., for radiation effects assessments) to semiconductors with non-cubic crystal structures. Computational methods that proved accurate for predicting defect levels in standard cubic semiconductors, were found to have shortcomings when applied to the lowered symmetry structures prevalent in next generation electronic materials such as SiC, GaN, and Ga 2 0 3 , stemming from an error in the treatment of the electrostatic boundary conditions. I describe methods to generalized the local moment countercharge (LMCC) scheme to position a charge in bulk supercell calculations of charged defects, circumventing the problem of measuring a dipole in a periodically replicated bulk calculation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Structure and Voltage Recovery Driven by Defects Elimination in Li-Rich Layered Oxide Cathode Materials

Lithium-ion batteries (LIBs) are ubiquitous in a wide range of technologies, including cell phones, electric vehicles, and energy grid, and are indispensable in the transition toward sustainable energy technology. The battery performance is inherently connected to the capability of lithium ions to reversibly intercalate with the electrodes. The capacity of classical layered transition metal (TM) oxides, the primary commercial cathode materials, is limited to cationic redox activity. Anionic redox has thus emerged as a new paradigm for designing novel cathodes for next generation LIBs. It is recently confirmed oxygen redox in lithium-rich layered oxides (LRLO) with composition xLi 2 MnO 3 ∙(1-x)LiTMO 2 , which enables this group of materials exhibit reversible capacities exceeding 280 mAh g -1 . Unfortunately, this material undergoes voltage fading and instability that is rooted in irreversible structural transformation in terms of defects formation including lithium and oxygen vacancies, lithium tetrahedron, TM migration, edge dislocations, stacking fault, and local strain. Modification methods including morphology control, composition gradient, surface treatment, foreign elements doping, etc., have been proposed in the literatures to mitigate the defects formation and maintain the structure reversibility. Despite their improvement, the intrinsic voltage fading is still inevitable and remains a challenge that will require considerable effort to overcome. In this work, we demonstrate a path to recover the layer structure and working voltage through high-temperature annealing. The treatment at high temperature recovers the local Li-excess environments around oxygen, oxygen stacking sequence, and eliminates microstrain associated with different defects. As a result, the voltage profile once more shows plateau region during charge and the average discharge voltage is restored of the cycled cathode after heat treatment. Both theoretical calculations and experimental characterizations illustrate the mechanisms underlying the heating induced recovery. The structure metastability and defects removal are decisive in structure and voltage recovery of LRLO cathode material.

Zhang, Minghao↗
DOE OSTI2022

Ellipsoidal Fitting Methodology for Defect Clusters in Gallium Arsenide

In assessing the initial spatial distribution of defects from neutron or heavy ion irradiation, it is useful to have a reliable, automated, and fast-running tool to evaluate characteristic metrics such as the number of sub-clusters or the overall cluster volume. The latter metric, for instance, can be utilized to estimate a reference neutron fluence level at which inter-cluster interaction effects begin to become significant. This paper details a methodology to fit an arbitrarily complex defect map with a set of ellipsoids (one per identified sub-cluster) in which the constituent defects of a sub-cluster are determined using fuzzy degree-of-membership analysis. Specifically, a parameterized model is developed for point defects in gallium arsenide. Cluster volume calculations based on the model are compared against convex hull and single- ellipsoid representations. Results show that the parameterized sub-cluster model begins to deviate from the two reference models at a recoil energy of about 100 keV in GaAs, with the convex hull and single-ellipsoid representations increasingly overestimating the volume thereafter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automatic Detection of Defects in High-Reliability Components

Disastrous consequences can result from defects in manufactured parts—particularly the high consequence parts developed at Sandia. Identifying flaws in as-built parts can be done with nondestructive means, such as X-ray Computed Tomography (CT). However, due to artifacts and complex imagery, the task of analyzing the CT images falls to humans. Human analysis is inherently unreproducible, unscalable, and can easily miss subtle flaws. We hypothesized that deep learning methods could improve defect identification, increase the number of parts that can effectively be analyzed, and do it in a reproducible manner. We pursued two methods: 1) generating a defect-free version of a scan and looking for differences (PandaNet), and 2) using pre-trained models to develop a statistical model of normality (Feature-based Anomaly Detection System: FADS). Both PandaNet and FADS provide good results, are scalable, and can identify anomalies in imagery. In particular, FADS enables zero-shot (training-free) identification of defects for minimal computational cost and expert time. It significantly outperforms prior approaches in computational cost while achieving comparable results. FADS’ core concept has also shown utility beyond anomaly detection by providing feature extraction for downstream tasks.

47 OTHER INSTRUMENTATION↗