How defects in lanthanum iron manganite perovskite structures promote the catalytic reduction of NO by CO
Not Available
Mohammadi, Asghar↗
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Prediction of dislocation - grain boundary interactions in FCC aluminum bicrystals using a modified continuum criterion and machine learning methods
Mechanical properties of metals such as strength and toughness are strongly correlated to complex interactions between various defects in the crystalline structure. While elementary interactions between these defects have been investigated using recent micro- and nano-characterization techniques, understanding of the detailed interaction mechanisms has hardly been obtained. To understand defect-driven plasticity at various time and length scales, it is necessary to formulate a general guideline to predict both the interaction type (transmission or reflection) and the dislocation's subsequent slip system after the interaction. Many criteria based on the geometric alignment of the defects have been developed to predict this phenomenon, but these have yet to be found to be accurate when applied to general data sets of grain boundaries (GBs). With this motivation, we conduct a systematic study using molecular dynamics (MD) models of bicrystals to analyze defect interaction process between a prismatic dislocation loop and eleven different grain boundaries of the following character: three tilt, three twist, and five mixed. Based on the MD observations, two new prediction methods are developed: the first is a new data-driven parametric score function based on the classical geometric criteria, and the second is by applying Gaussian process machine learning methods to find the probability distribution of a hidden function. In conclusion, the proposed data-driven prediction methods could pave a new way to predict the unit interaction of dislocations with various GBs, which could show much higher accuracy compared to pre-existing geometric criteria.
36 MATERIALS SCIENCE↗
Point defects at grain boundaries can create structural instabilities and persistent deep traps in metal halide perovskites
Perovskite grain boundaries exhibit slow fluctuations and transient charge trap states. Pb interstitials lead to boundary reconstruction and long-lived trap states due to Pb trimers.
Wu, Yifan [Department of Chemistry, University of ↗
Insights into defect kinetics, mass transport, and electronic structure from spectrum effects in ion-irradiated Bi 2 O 3
Ion-irradiation of α-Bi 2 O 3 induces amorphization, altering mass transport and band structure.
36 MATERIALS SCIENCE↗
The Gaussian nature of the band-edge of ZnO microcrystalline thin films
A straightforward analytical approach based on the derivative of the absorption coefficient is presented, which enables probing the nature of the band edge (BE) of ZnO microcrystalline films. The study was conducted via transmission experiments at temperatures of 77–532 K and repeated for samples annealed up to 1073 K. It was found that the derivative of the natural log of the BE absorption coefficient resulted in a Gaussian function. The Gaussian linewidth is used in the electron–phonon (e–p) interaction model to characterize the defect-state of the films. The BE of the as-grown film was found to exhibit no thermal dependence and no e–p coupling, indicative of a disordered crystal. Upon annealing and improvement of the film quality, the thermal phonons became more activated, but only above room temperature with a phonon energy of ~75 meV, while up to room temperature, the impact of phonons on the BE is insignificant. A disorder–order transition was determined to take place at an annealing temperature of ~673 K. X-ray diffraction concurs with these results. The study indicates that the prevalent defects are of structural nature due to the inherent granular morphology of the films. This defect was found to dominate the behavior of the BE even at the elevated temperature regime rather than thermal phonons.
36 MATERIALS SCIENCE↗
Quantum Mechanical Evaluation of CZTS Properties for Photovoltaic Applications
As a quaternary semiconductor, CZTS (Cu 2 ZnSnS 4 ) shows promise as a thin-film solar cell material. The constituent elements in CZTS are abundant and non-toxic, and CZTS has an ideal band gap (~1.5 eV) for solar cell applications, which produces a large absorption coefficient. Unfortunately, the low efficiencies and difficulties in synthesizing the desired crystallographic phase (kesterite) and the associated formation of antisite defects limit its deployment. Using theoretical and computational tools, we investigated these current limitations, explored strategies to resolve these issues, and developed design guidelines to improve the efficiency of CZTS-based sunlight absorbers. We first sought to understand the challenges associated with synthesizing the kesterite phase and proposed ways to favor kesterite growth, thus adding to the understanding of CZTS photovoltaics. We also analyzed the major limitations at the atomic scale lowering the efficiency of CZTS: the low open-circuit voltage (V oc ) and the short carrier lifetime, thus addressing the technical effectiveness of CZTS photovoltaics. We believe the latter is due to Shockley-Read-Hall (SRH) recombination centers associated with point defects. We also studied the electronic structure of interfaces and defects introducing such recombination centers, and explored doping strategies to reduce SRH recombination. Ultimately, these efforts will benefit the public by contributing to the development of cheap and efficient solar energy conversion technologies made from abundant, non-toxic materials.
14 SOLAR ENERGY↗
Modeling The Influence of Printing Defects in Additively Manufactured Silicone Lattice Structures.
Abstract not provided.
Hamel, Craig↗
Impact of Defect Distributions on the Reliability of Structural Materials
The purpose of this study is to develop a probabilistic model to estimate the fatigue life of gas turbine disks made of powder superalloy metals. Such metals are known to have high strength and ductility under extremely high temperatures and known to have high fatigue lives. However, despite the high quality controls instituted during metal processing, sufficiently high numbers of ceramic inclusions materialize within the metal matrix creating weak links that lead to reduced fatigue lives. To study the effect of such ceramic inclusions on the fatigue life of gas turbine disks, an experimental program has been initiated at the NASA Glenn Research. In this phase of the study, a statistical analysis of the test results is undertaken which served as the basis of the probabilistic simulation model. The study is a part of the Crack Resistant Disk Materials task within the Ultra Safe Propulsion Project.
Ghosn, Michel J.↗
Identification of Defects and the Origins of Surface Noise on Hydrogen–Terminated (100) Diamond
Near-surface nitrogen vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)—2 × 1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping are determined based on the modeling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for the nucleation of new layers.
36 MATERIALS SCIENCE↗
Native and radiation induced point defects in AlN and Sc-doped AlN
Herein, we have performed first-principles calculations to investigate the electronic structure, configurations, formation, and binding energies of native and radiation induced point defects in pristine and Sc-doped wurtzite AlN. For the native defects, the nitrogen vacancy has the lowest formation energy in p-type material while the aluminum vacancy has the lowest formation energy in n-type material which is consistent with the previous studies. Several interstitial defect structures were modeled for Al, N, and Sc atoms. The effects of charge state on their relative stability were investigated. The binding energy of Sc with point defects was calculated and found to be dependent strongly on the defect type and charge state. The results obtained are discussed in light of the possible Sc effects on the radiation damage evolution in AlN. Thus the attraction of Sc atom to N vacancy and both Al and N interstitials reduces their mobility and increases Frenkel pair recombination distance.
36 MATERIALS SCIENCE↗
Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers
The Light Water Reactor Sustainability Program was initiated to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for most nuclear power generation costs. This report describes the current state of research related to ultrasonic guided wave testing and its application to detecting defects in commercial nuclear power plants. The report analyzes the applicability of the guided wave technology to secondary piping systems, as well as studying the potential for expanding the range of guided wave technology to include bent piping and other piping components. The ultrasonic guided waves can inspect long stretches of straight piping; however, more complex geometries such as elbows, welds, and tees are causing spurious reflections and coherent noise, which significantly decreases the sensitivity of the technique. To deal with these limitations, high-definition fiber optic sensors are applied to complex piping geometries, and advanced machine learning algorithms are used to detect deviations from healthy states. This report also analyzes the performance of advanced signal processing and machine learning-based pattern recognition algorithms in detecting defects in secondary structures. It is demonstrated on guided wave data collected at nuclear power plants that the independent component analysis can separate different coherent noise components and segregate them from useful signals. It also demonstrates that advanced machine learning techniques, such as neural networks, support vector machines, and deep learning networks, can detect minor defects present in inspected structures. Recommendation about the applicability of advanced machine learning techniques to online piping monitoring are also given.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Functional analysis of the cfdp1 gene in zebrafish provides evidence for its crucial role in craniofacial development and osteogenesis
Highlights: • The zebrafish cfdp1 gene maps to chromosome 8 and is 2222 bp long with seven exons and six introns. • The cfdp1 gene is highly expressed since very early embryonic stages until the completion of organogenesis. • The zebrafish CFDP1 protein is highly abundant in the head structures. • CFDP1 is an essential protein that actively participates to the correct definition and morphogenesis of craniofacial territories in the zebrafish model system. The CFDP1 proteins have been linked to craniofacial development and osteogenesis in vertebrates, though specific human syndromes have not yet been identified. Alterations of craniofacial development represent the main cause of infant disability and mortality in humans. For this reason, it is crucial to understand the cellular functions and mechanism of action of the CFDP1 protein in model vertebrate organisms. Using a combination of genomic, molecular and cell biology approaches, we have performed a functional analysis of the cfdp1 gene and its encoded protein, zCFDP1, in the zebrafish model system. We found that zCFDP1 is present in the zygote, is rapidly produced after MTZ transition and is highly abundant in the head structures. Depletion of zCFDP1, induced by an ATG-blocking morpholino, produces considerable defects in craniofacial structures and bone mineralization. Together, our results show that zCFDP1 is an essential protein required for proper development and provide the first experimental evidence showing that in vertebrates it actively participates to the morphogenesis of craniofacial territories.
60 APPLIED LIFE SCIENCES↗
Defect graph neural networks for materials discovery in high-temperature clean-energy applications
We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. In conclusion, it will help to tackle future materials discovery problems in clean energy and beyond.
97 MATHEMATICS AND COMPUTING↗
Applying Acoustic Emission to Large Format Additive Manufacturing
Large format additive manufacturing (AM) is being adapted as a method of producing large structures in a short lead time and cost-effective way. With the growing advancement in AM techniques and application, machine monitoring and part qualification is highly needed. There has been leading research focused on the manufacturing development and feedstock material but minimum research on the structural health monitoring (SHM), defect detection, and nondestructive evaluation (NDE) for large format AM. Scanning large structure using conventional nondestructive testing (NDT) techniques, such as ultrasound or X-ray, and searching for potential defects can be very time consuming, challenging and cost prohibitive. Acoustic emission (AE) is a passive technique that can be used to monitor and locate defect progression in large structures by distributing group of sensors around the part. This research outlines the necessary procedure for implementing AE to AM as a reliability method. The topics this research will display are: (a) Wave propagation/velocity evaluation, (b) an AE attenuation characterization for the anisotropic printed structure and material, (c) background noise measurements of the extruder and gantry system, and (d) assist in optimal sensor selection and placement for monitoring large AM structures with demonstration. This work establishes the foundation for scaling up the SHM-AE system for the large additive platform.
Spencer, Ryan↗
Rare isotope-containing diamond colour centres for fundamental symmetry tests
Detecting a non-zero electric dipole moment in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such ‘pear-shaped’ nuclei, capturing, detecting and manipulating them efficiently is a crucial prerequisite. Incorporating them into synthetic diamond optical crystals can produce defects with defined, molecule-like structures and isolated electronic states within the diamond band gap, increasing capture efficiency, enabling repeated probing of even a single atom and producing narrow optical linewidths. In this study, we used density functional theory to investigate the formation, structure and electronic properties of crystal defects in diamond containing 229 Pa, a rare isotope that is predicted to have an exceptionally strong nuclear octupole deformation. In addition, we identified and studied stable lanthanide-containing defects with similar electronic structures as non-radioactive proxies to aid in experimental methods. Our findings hold promise for the existence of such defects and can contribute to the development of a quantum information processing-inspired toolbox of techniques for studying rare isotopes.
07 ISOTOPE AND RADIATION SOURCES↗
Application of critical COD and plastic instability concepts to fracture of shells
The paper deals with the initiation, growth, and possible arrest of fracture in shell structures containing initial defects which may be approximated by an isolated part-through crack. The main study is restricted to the structures in which the net section of the shell wall around the defect zone is fully yielded. The problem is solved by using an 8th order shallow shell theory with a conventional plastic strip model to account for the plastic deformations. Using the critical COD or the plastic instability as fracture criterion, the results are applied to the fracture propagation and arrest in shells. The calculated results are then compared with those obtained from the experiments on zircaloy, aluminum, and steel pipes.
Erdogan, F.↗
On the Importance of Chemical Precision in Organic Electronics: Fullerene Intercalation in Perfectly Alternating Conjugated Polymers
Abstract The true structure of alternating conjugated polymers—the state‐of‐the‐art materials for many organic electronics—often deviates from the idealized picture. Homocoupling defects are in fact inherent to the widely used cross‐coupling polymerization methods. Nevertheless, many polymers still perform excellently in the envisaged applications, which raises the question if one should really care about these imperfections. This article looks at the relevance of chemical precision (and lack thereof) in conjugated polymers covering the entire spectrum from the molecular scale, to the micro and mesostructure, up to the device level. The different types of polymerization errors for the alkoxylated variant of the benchmark (semi)crystalline polymer poly[2,5‐bis(3‐tetradecylthiophen‐2‐yl)thieno[3,2‐ b ]thiophene (PBTTT) are identified, visualized, and quantified and a general strategy to avoid homocoupling is introduced. Through a combination of experiments and supported by simulations, it is shown that these coupling defects hinder fullerene intercalation and limit device performance as compared to the homocoupling‐free analog. This clearly demonstrates that structural defects do matter and should be generally avoided, in particular when the geometrical regularity of the polymer is essential. These insights likely go beyond the specific PBTTT derivatives studied here and are of general relevance for the wider organic electronics field.
36 MATERIALS SCIENCE↗
Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel
Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.
36 MATERIALS SCIENCE↗