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At least 199 records · Page 11

Codimension-2 defects and higher symmetries in (3+1)D topological phases

(3+1)D topological phases of matter can host a broad class of non-trivial topological defects of codimension-1, 2, and 3, of which the well-known point charges and flux loops are special cases. The complete algebraic structure of these defects defines a higher category, and can be viewed as an emergent higher symmetry. This plays a crucial role both in the classification of phases of matter and the possible fault-tolerant logical operations in topological quantum error-correcting codes. In this paper, we study several examples of such higher codimension defects from distinct perspectives. We mainly study a class of invertible codimension-2 topological defects, which we refer to as twist strings. We provide a number of general constructions for twist strings, in terms of gauging lower dimensional invertible phases, layer constructions, and condensation defects. We study some special examples in the context of \mathbb{Z}_2 ℤ 2 gauge theory with fermionic charges, in \mathbb{Z}_2 \times \mathbb{Z}_2 ℤ 2 × ℤ 2 gauge theory with bosonic charges, and also in non-Abelian discrete gauge theories based on dihedral ( D_n D n ) and alternating ( A_6 A 6 ) groups. The intersection between twist strings and Abelian flux loops sources Abelian point charges, which defines an H^4 H 4 cohomology class that characterizes part of an underlying 3-group symmetry of the topological order. The equations involving background gauge fields for the 3-group symmetry have been explicitly written down for various cases. We also study examples of twist strings interacting with non-Abelian flux loops (defining part of a non-invertible higher symmetry), examples of non-invertible codimension-2 defects, and examples of the interplay of codimension-2 defects with codimension-1 defects. We also find an example of geometric, not fully topological, twist strings in (3+1)D A_6 A 6 gauge theory.

Barkeshli, Maissam↗

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗

Effects of defect recombination centers on radiation damage in solar cells

Defect production in silicon is modeled on a computer by solving a large system of rate equations. The model includes the main, known defects that are stable at operating temperatures of solar cells in outer space; most of these defects are secondary and tertiary defects. The preliminary result shows that the presence of defect recombination centers for primary defects (i.e., vacancy and interstitial) can effectively reduce the production rates of those stable defects and, consequentially, improve the lifetime of solar cells operating in radiation environment. The characteristics of the defect recombination center required for better solar cell performance along with prospective candidates are discussed.

Cheng, L. J.↗

An analysis of defect densities found during software inspections

Software inspection is a technical evaluation process for finding and removing defects in requirements, design, code, and tests. Detailed data was collected during the first three years of experience at JPL on 203 inspections. Statistics are discussed for this set of inspections. Included, on a per inspection basis, are averages of: staff time expended, pages covered, major defects found, minor defects found, and inspection team size. Analysis of variance showed a significantly higher density of defects during requirements inspections. It was also observed, that the defect densities found decreased exponentially as the work products approached the coding phase. Increasing the pace of the inspection meeting decreased the density of defects found. This relationship was observed to hold for both major and minor defect densities, although it was more pronounced for minor defects. Guidelines are provided for conducting successful software inspections based upon three years of JPL experience.

Kelly, John C.↗

Surface Inspection Tool for Optical Detection of Surface Defects

The Space Shuttle Orbiter windows were damaged both by micrometeor impacts and by handling, and required careful inspection before they could be reused. The launch commit criteria required that no defect be deeper than a critical depth. The shuttle program used a refocus microscope to perform a quick pass/fail determination, and then followed up with mold impressions to better quantify any defect. However, the refocus microscope is slow and tedious to use due to its limited field of view, only focusing on one small area of glass at a time. Additionally, the unit is bulky and unable to be used in areas with tight access, such as defects near the window frame or on the glass inside the Orbiter due to interference with the dashboard. The surface inspection tool is a low-profile handheld instrument that provides two digital video images on a computer for monitoring surface defects. The first image is a wide-angle view to assist the user in locating defects. The second provides an enlarged view of a defect centered in the window of the first image. The focus is adjustable for each of the images. However, the enlarged view was designed to have a focal plane with a short depth. This allows the user to get a feel for the depth of different parts of the defect under inspection as the focus control is varied. A light source is also provided to illuminate the defect, precluding the need for separate lighting tools. The software provides many controls to adjust image quality, along with the ability to zoom digitally the images and to capture and store them for later processing.

Nurge, Mark↗

Are Soft Short Tests Good Indicators of Internal Li-ion Cell Defects?

The self discharge test at full state of charge, may not be a good one to detect subtle defects since the li-ion chemistry has the highest self discharge at full state of charge. One should characterize self discharge versus storage time for each cell manufacturer/design to differentiate between normal self discharge and that due to a subtle manufacturing defect. The various soft short test methods indicate that if this test is carried out at full discharge (0% SOC) with all capacity removed (by lowering the current load in a stepwise manner to the same end of discharge voltage), then the cells need to be placed in storage for more than 72 hours to get a good analysis on the presence of subtle defects since it takes more than 72 hours to achieve voltage stabilization. If the cells are to be charged up even to a small percentage (ex. 1%), 72 hours are sufficient to determine issues. However, the pass/fail criteria should be based on a valid OCV decline. Less than 10 mV voltage decline is not a good method to detect subtle defects. As mentioned in the first bullet, self discharge is a competing reaction when a charge is introduced and hence a characterization of the self discharge versus storage time is required to fully correlate voltage decline to a failure due to a subtle defect. Soft short test method cannot be relied on for defect detection because cells with and without voltage decline seemed to have similar defects and characteristics. Screening methods such as internal resistance and capacity as well as a 3-sigma range for OCV, mass and dimensions should be used to screen out outliers. A very critical aspect in the understanding of subtle defects is to carry out destructive analysis of cells from every lot to confirm the quality of production and screen all cells and batteries in a stringent manner to have a high quality set of flight cells. Self Discharge Test: Fully charged cells shall be placed in Open circuit stand for 72 hours (OCV measurement twice a day); continue for total of 14 days with 1 reading per day 2. Soft Short Test 1: Fully charge; cells discharged to manufacturer's end of disch. Voltage (EODV) cutoff at C/5 rate; stand for 30 minutes; discharge with C/500 to the same EODV. stand for another 30 minutes; discharge the cells again using C/1000 current to the same EODV. OCV measurements twice a day for 72 hours and then for total of 14 days (data collection same as in 1.) 3. Soft Short Test 2: Fully charge; cells discharged to the manuf. EODV with a C/13 constant current; provide a 10 hour rest, discharge again to the same EODV with a current of C/250, provide a 10 hour rest, discharge again using a C/250 rate, provide a 24 hour rest, charge using C/250 to 3.15 V (for ~12 hours). OCV measurements twice a day for at 72 hours. (data collection same as in 1.) 4. Soft Short Test 3: Fully charge; cells shall be discharged using C/10 current to manuf. EODV. Allow the cell to remain at Open circuit for 10 seconds. Discharge the cell at C/20 rate to the same EODV, hold open circuit for 24 hours. Discharge the cells at C/200 rate to the same end of voltage cutoff and hold open circuit for 24 hours. Discharge the cells one more time at C/200 rate to the same EODV and hold open circuit for 36 hours. Charge at C/200 rate to 3.15 V and hold for 3 days. Record OCV during the open circuit stand periods every 12 hours and at the beginning and end of the 3 day hold (include the 12 hour OCV recording during this time also). Capacity Cycling: Cells with declining voltages - one cell from each manufacturer chosen for cycling Destructive Physical Analysis (DPA): Cells with and without decline chosen from each lot for DPA.

Jeevarajan, J.↗

Defect Chemistry in High‐Voltage Cathode Materials for Lithium‐Ion Batteries

High-voltage cathodes (HVCs) have emerged as a paramount role for the next-generation high-energy-density lithium-ion batteries (LIBs). However, the pursuit of HVCs comes with inherent challenges related to defective structures, which significantly impact the electrochemical performance of LIBs. The current obstacle lies in the lack of a comprehensive understanding of defects and their precise effects. This perspective aims to provide insights into defect chemistry for governing HVCs. The classifications, formation mechanisms, and evolution of defects are outlined to explore the intricate relationship between defects and electrochemical behavior. The pressing need for cutting-edge characterization techniques that comprehensively investigate defects across various temporal and spatial scales is emphasized. Building on these fundamental understandings, engineering strategies such as composition tailoring, morphology design, interface modification, and structural control to mitigate or utilize defects are thoroughly discussed for enhanced HVCs performance. Furthermore, these insights are expected to provide vital guidelines for developing high-performance HVCs for next-generation high-energy lithium-ion batteries.

cross-scale characterization↗

Ab initio evaluation of the electronic and optical properties of V B C B defect in wurtzite boron nitride as promising single-photon emitter

Single-photon emitters (SPEs) in the near-infrared (NIR) range with sharp and intense zero-phonon lines (ZPLs) of emission are critical for quantum communications. Certain local defects in wide-bandgap semiconductors create isolated occupied and unoccupied states within the bandgap of the host semiconductor and thus exhibit sharp ZPLs of emission. We designed and studied a defect in the wurtzite boron nitride as a potential SPE. It consists of a boron vacancy and a carbon atom substituting another boron atom (V B C B defect). The density of states is obtained within the GW method to identify favorable local defect states that may dominate optical transitions. The dielectric function and oscillator strength of the V B C B defect are obtained using the Bethe-Salpeter equation method to identify the optical excitations of the V B C B defect, from which we conclude that the defect could be a source of NIR emission with a narrow bright ZPL peak, thus an efficient SPE.

36 MATERIALS SCIENCE↗

Tuning Point Defects by Elastic Strain Modulates Nanoparticle Exsolution on Perovskite Oxides

Exsolution generates stable and catalytically active metal nanoparticles via phase precipitation out of a host oxide. An ability to control the size and dispersion of the exsolution particles is desirable for design of nanostructured (electro)catalysts. In this work, we demonstrate that tuning point defects by lattice strain affects both the thermodynamics and the kinetics of iron (Fe 0 ) exsolution on La 0.6 Sr 0.4 FeO 3 (LSF) thin film model. By combining in situ surface characterization and ab initio defect modeling, we show oxygen vacancy and Schottky defects to be the primary point defects formed upon Fe0 exsolution. Lattice strain tunes the formation energy, and thus the abundance of these defects, and alters the amount and size of the resulting exsolution particles. In addition, we find that the density of exsolved nanoparticles matches the concentration of oxygen vacancy pairs, thus pointing to the surface oxygen vacancy pairs as preferential nucleation sites for exsolution. The tensile-strained LSF with a facile formation of these critical point defects results in a higher Fe 0 metal concentration, a larger density of nanoparticles, and a reduced particle size at its surfaces. These results provide important mechanistic insights and highlight the role of point-defect engineering in designing nanostructured catalysts in energy and fuel conversion technologies.

36 MATERIALS SCIENCE↗

Manipulating ferroelectric behaviors via electron-beam induced crystalline defects

Ferroelectric nanoplates are attractive for applications in nanoelectronic devices. Defect engineering has been an effective way to control and manipulate ferroelectric properties in nanoscale devices. Defects can act as pinning centers for ferroelectric domain wall motion, altering the switching properties and domain dynamics of ferroelectrics. However, there is a lack of detailed investigation on the interactions between defects and domain walls in ferroelectric nanoplates due to the limitation of previous characterization techniques, which impedes the development of defect engineering in ferroelectric nanodevices. In this study, we applied in situ biasing transmission electron microscopy to explore how dislocation loops, which were judiciously introduced into barium titanate nanoplates via electron beam irradiation, affect the motion of ferroelectric domain walls. The results show that the motion was dramatically suppressed by these localized defects, because of the local strain fields induced by the defects. Additionally, the pinning effect can be further enhanced by multiple domain walls embedded with defect arrays. These results indicate the possibility of manipulating domain switching in ferroelectric nanoplates via the electron beam.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Optoelectronic and structural characterization of trapezoidal defects in 4H-SiC epilayers and the effect on MOSFET reliability

To this day, trapezoidal defects are found in clusters and high counts in wafers representing the industry standard in terms of material quality being produced. This study sheds light on the nature, origin, behavior, and impact of this defect on device yield and reliability. Trapezoidal defects in 4H-SiC epitaxial layers were investigated by photoluminescence (PL) imaging, scanning electron microscopy (SEM), cathodoluminescence spectrum imaging (CLSI), SEM electron beam induced current (EBIC) imaging, and by transmission electron microscopy (TEM) observation. The bar-shaped stacking faults were identified by the PL and CL measurements with a peak emission wavelength of 420 and 450 nm. An optoelectronic behavioral study based on the recombination enhanced dislocation glide mechanism revealed how expanding dislocations and stacking faults interact with each other. Combining the luminescence and microscopy results, the nature of the stacking faults was identified as being a combination of Shockley-type and Frank-type stacking faults. The TEM analysis showed that these defects originate from the substrate and the stacking sequences of some of the faults were determined as (…2, 4, 2…) and (…2, 3, 2…) in the Zhdanov's notation by high-resolution TEM. The origin of this defect is speculated based on our results and previous reports. The EBIC imaging showed that the high density of SFs in these towers is a strong site of carrier recombination, which presumably has an impact on the transfer characteristics of SiC devices. Furthermore, these defects have shown to impact metal oxide semiconductor field effect transistors electrical performance via an increase in the on-state resistance depending on the coverage percentage of the tower of defects in the active area of the device.

4H-SiC↗

First-principles study of quantum defect candidates in beryllium oxide

Beryllium oxide (BeO) is a promising host for quantum defects because of its ultrawide band gap. We conducted comprehensive first-principles investigations of the native point defects in BeO using density functional theory with a hybrid functional. We found that the beryllium and oxygen vacancies are the most stable defects, whereas other native defects such as interstitials or antisites have high formation energies. We investigate the point defects as candidates for quantum defects by examining spin states and internal optical transitions. Here, the oxygen vacancy ($V$$^{+}_{O}$) emerges as a suitable spin qubit or single-photon emitter; we also find its stability can be enhanced by forming a (V O – Li Be ) 0 complex with a Li acceptor. The $O$$^{–}_{Be}$ antisite also has desirable optical and spin properties. Overall, because of its desirable properties as a host material, BeO could be an excellent host for quantum defects, with $V$$^{+}_{O}$, (V O – Li Be ) 0 , and $O$$^{–}_{Be}$ as prime candidates.

36 MATERIALS SCIENCE↗

Primary intrinsic defects and their charge transition levels in β–Ga 2 O 3

A steady-state photocapacitance (SSPC) setup directly connected to the beamline of a MeV ion implanter is utilized to study primary intrinsic defects in β – Ga 2 O 3 generated by He implantation at cryogenic temperatures (120 K). At low temperatures, the migration of defects is suppressed, and hence the generation of primary intrinsic defects is expected to prevail. SSPC measurements reveal defect-related optical transitions in halide vapor-phase epitaxy (HVPE) -grown β – Ga 2 O 3 thin films with onset energies at 1.3 ( T 1 ) , 1.7 ( T 2 ) , 1.9 ( T 3 ) , 2.6 ( T 4 ) , 3.7 ( T 5 ) , and 4.2 eV ( T 6 ) . T 2 , T 4 , T 5 , and T 6 were observed in as-received HVPE-grown β – Ga 2 O 3 thin films, whereby T 2 is only sporadically observed. The introduction rates for T 3 , T 4 , as well as T 6 indicate an origin related to primary intrinsic defects. Notably, T 1 and T 3 are only observed after He implantation at cryogenic temperatures. Hybrid-functional calculations were performed to estimate the optical absorption cross-section spectra for the gallium ( Ga i ) and oxygen ( O i ) interstitials as well as the corresponding vacancies ( V Ga and V O , respectively), and compared with the measured onsets for optical absorption found by SSPC measurements. Indeed, we propose T 3 to be associated with Ga i ( + / + 2 ) and/or V GaI ( - 3 / - 2 ) , while T 4 is suggested to be related to V O K ( 0 / + ) ( K = I , II , III ) and/or V GaII ( - 3 / - 2 ) . Additionally, several further charge-state transition levels associated with V GaI and V GaII may contribute to T 4 and T 6 . We further studied the kinetics of the defects created with He implantation by exposing the sample to room temperature. The kinetics observed for T 3 and T 4 further support the proposed assignments of the corresponding defect signatures.

36 MATERIALS SCIENCE↗

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗

Extended defects-enhanced oxygen diffusion in ThO 2

Oxygen self-diffusion is key to understanding stoichiometry and defect structures in oxide nuclear fuels. Experimentally, low activation-barrier oxygen migration was found in ThO 2 , a candidate nuclear fuel, possibly due to short-circuit diffusion mechanisms. Here, we perform extensive molecular dynamics simulations to show that various types of extended defects can enhance oxygen self-diffusion with a much-reduced activation barrier in ThO 2 . In this work, we consider extended defects including 1D (dislocation), 2D (grain boundary), and 3D (void) defects. Due to the distinct characteristics of each type of extended defect, the modulation of oxygen diffusion varies. Further, these results provide a quantitative description of oxygen transport, which is significantly enhanced within a close distance (nanometer scale) from the extended defects. Among all these considered defects, grain boundary, particularly the low-energy $Σ$3 twin boundary, exhibits the strongest effect on increasing oxygen transport.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Revisiting point defect thermodynamics in group IVB and VB transition metal carbides

We present a comprehensive re-examination of point defect thermodynamics in group IVB and VB transition metal carbides (TMCs) with the rocksalt structure using a combination of density functional theory (DFT) calculations and a statistical mechanical Wagner-Schottky model within the canonical ensemble. The most stable configurations of point defects were discovered using basin-hopping global optimization, driven by either a machine learning interatomic potential (MLIP) or DFT. A key finding is the identification of previously unreported dicarbon antisites—a C–C dimer occupying a metal site—as the structural (constitutional) defects on the carbon-rich side of stoichiometry in all group IVB and VB TMCs except TaC. Furthermore, dicarbon antisite-containing thermal defect complexes, such as quadruple and interbranch defects, can dominate in TMCs under specific stoichiometric and temperature conditions. In conclusion, by incorporating dicarbon antisites into the defect landscape, this work provides a revised understanding of the thermodynamics of point defects in TMCs.

Carbides↗