Defect-free Regions Research Articles

Tuning Electronic and Functional Properties in Defected MoS2 Films by Surface Patterning of Sulphur Atomic Vacancies.

Defects are inherent in transition metal dichalcogenides and significantly affect their chemical and physical properties. In this study, surface defect electrochemical nanopatterning is proposed as a promising method to tune in a controlled manner the electronic and functional properties of defective MoS₂ thin films. Using parallel electrochemical nanolithography, MoS₂ thin films are patterned, creating sulphur vacancy-rich active zones alternated with defect-free regions over a centimetre scale area, with sub-micrometre spatial resolution. The patterned films display tailored optical and electronic properties due to the formation of sulphur vacancy-rich areas. Moreover, the effectiveness of defect nanopatterning in tuning functional properties is demonstrated by studying the electrocatalytic activity for the hydrogen evolution reaction.

Experimental Evaluation of the Possibility of Through Defects Detection in Thermally Expanded Graphite Workpieces by Acoustic Method

The paper presents the results of evaluating the possibility of detecting defects (e.g., through holes) in workpieces made of thermally expanded graphite used as sealing materials using a non-contact air-coupled acoustic inspection system. The deviation of the acoustic signal amplitude when it passes through a sample of thermally expanded graphite in defective and defect-free regions is used as an informative parameter. The obtained dependences of the change in the amplitude of the passed signal through the sample on the size of the artificial through hole, density and thickness of the object showed a significant influence of these parameters on the detectability of the defect, which should be taken into account in the development of inspection technologies and in the process of its implementation. The change in the amplitude of the signal passed through the object directly depends on the increase in the parameters of the sample (density, thickness) and the size of the through hole, which increases the sensitivity of acoustic control and detectability of defects.On the basis of the obtained dependences the criteria for rejection of workpieces can be developed at the stage of production or immediately before manufacturing of their products. The influence of defect positioning relative to the acoustic axis of the transmitter-receiver system is shown, which leads to a decrease in the informative parameter, and consequently to a decrease in sensitivity. This dependence is characteristic for all thicknesses and densities of objects, as well as for all sizes of artificial through holes when control is carried out immediately after defect formation. When acoustic inspection is carried out some time after the defect formation, the maximum of this dependence may shift due to the shift of the amount of substance to fill the defect cavity, which is confirmed by microscopy. The obtained results can be used in the development of workpieces acoustic control parameters.

Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min.

Synchrotron X-ray microtomography and finite element modelling to uncover flax fibre defect’s role in tensile performances

Flax fibres offer performance capabilities comparable to glass fibres, thereby enhancing their potential in the biobased composites industry. However, these fibres have morphological defects affecting their mechanical features. In the present work, flax elementary fibres geometries with defects assessed by synchrotron X-ray microtomography were meshed to simulate a tensile test using finite element analysis. For the first time, the distribution of stresses in the vicinity of defects is revealed. The geometrical irregularities at the surface of the fibre and the delamination of cellulose layers within fibre cell wall turned out to concentrate stress up to 7.5 times compared to defect-free regions. These results demonstrate why flax fibres cannot reach their full potential in comparison to what could be expected from a structure mainly constituted from crystalline cellulose microfibrils, and why fracture in a composite is likely to initiate in those defect zones.

Fast VGG: An Advanced Pre-Trained Deep Learning Framework for Multi-Layered Composite NDE via Multifrequency Near-Field Microwave Imaging

ABSTRACT Microwave nondestructive testing (NDT) techniques show promise for composite inspection due to microwave signals’ ability to penetrate and interact with internal structures. However, current microwave imaging approaches have poor spatial resolution, struggling to distinguish defects from defect-free regions. This limits reliable subsurface analysis and widespread adoption. This paper introduces a novel multi-frequency microwave imaging fusion method using an open-ended waveguide and deep learning to enhance defect detection accuracy in carbon fiber-reinforced polymer (CFRP) composites. The proposed technique employs an optimized feature extraction strategy to improve differentiation between defective and sound areas for superior subsurface visualization. The method leverages VGG-19 for efficient feature extraction and parallel processing to merge information from multi-frequency images. Our method significantly improved detection accuracy and F1 score, surpassing non-deep learning image fusion techniques by at least 50%. The optimized fusion strategy enables clear visualization of various defect types across multiple subsurface layers. Our approach also reduces computation time versus standard VGG implementation by 3–4×, showing scalability. The results demonstrate the proposed method’s potential to overcome existing constraints and provide rapid, accurate subsurface analysis of complex CFRP structures through an optimized deep learning framework, holding significance for expanding microwave NDE applications.

Mapping the Ge/InAl(Ga)As interfacial electronic structure and strain relief mechanism in germanium quantum dots

Germanium quantum dots (QDs) with defect-free regions and clusters of stacking faults (SFs) relieved the strain from Ge QDs.

Automatic segmentation by the method of interval fusion with preference aggregation when recognizing weld defects

Quality control of welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In the paper, it is proposed an approach to automatic detection and classification of a defective region, where segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors' robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing of the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods such as Otsu method and k-means.

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches. This study introduces a multisensor fusion-based digital twin (MFDT) for localized quality prediction in the robotic L-DED process. The data used in multisensor fusion includes features extracted from a coaxial melt pool vision camera, a microphone, and an off-axis short wavelength infrared thermal camera. The key novelty of this work is a spatiotemporal data fusion method that synchronizes multisensor features with the real-time robot motion data to achieve localized quality prediction. Optical microscope (OM) images of the printed part are used to locate defect-free and defective regions (i.e., cracks and keyhole pores), which serve as ground truth labels for training supervised machine learning (ML) models for quality prediction. The trained ML model is then used to generate a virtual quality map that registers quality prediction outcomes within the 3D volume of the printed part, thus eliminating the need of physical inspections by destructive methods. Experiments show that the virtual quality map closely matches the actual quality observed by OM. Compared to traditional single-sensor-based quality prediction, the MFDT has achieved a significantly higher quality prediction accuracy (96%), a higher ROC-AUC score (99%), and a lower false alarm rate (4.4%). As a result, the MFDT is a more reliable method for defect prediction. The proposed MFDT also lays the groundwork for our future development of a self-adaptive hybrid processing strategy that combines machining with AM for defect removal and quality improvement.

The bain and orthorhombic paths of the bcc–fcc transformation in a bcc metal

The energy of the Bain and orthorhombic paths in niobium and the instability of phonons during uniaxial deformation along <001> are studied with the application of the ab initio method. The orthorhombic transformation path is refined with regard to its symmetry. The calculation of the phonon spectrum in the entire irreducible Brillouin zone depending on deformation makes it possible to find the softest branches of the phonon spectrum responsible for the loss of stability of the structure. The nature of the loss of stability is revealed, and the strain at which stability is lost both in tension and compression is evaluated. Possible mechanisms determining the stability of the structure and theoretical strength of niobium are discussed. The results obtained can relate to experimental situations when small defect-free regions are deformed, for example, in nanostructured materials, when surface layers are modified by modern methods of plastic deformation, and during nanoindentation.

Wood broken defect detection with laser profilometer based on Bi-LSTM network

Detecting wood broken defects through machine vision is challenging due to the similar appearance of defect and defect-free regions on images. Laser profilometer is a reasonable solution, nevertheless, imperfect point cloud representation, such as slope profile, incontinuity of tiny defects and similarity between broken defects and sound area, poses obstacles. To overcome these challenges, this study proposes a multi-line detection method based on bidirectional long- and short-term memory network (Bi-LSTM) for real-time wood broken defect detection. The feature that represents the extent of surface damage in line-level is designed by residual extraction and sorting operation. The Bi-LSTM combines adjacent information to exaggerate semantic information of detection line. Context information extracted by Bi-LSTM are concatenated for multi-line detection to reduce computation complexity. Finally, detection results are modified by considering the information of adjacent lines of point cloud. Experimental results show that the proposed method achieves real-time detection with high accuracy.

Mitigating the Recrystallization of a Cold-Worked Cu-Al2O3 Nanocomposite via Enhanced Zener Drag by Nanocrstalline Cu-Oxide Particles.

The strength of metals and alloys at elevated temperatures typically decreases due to the recovery, recrystallization, grain growth, and growth of second-phase particles. We report here a cold-worked Cu-Al2O3 composite did not recrystallize up to a temperature of 0.83Tm of Cu. The composite was manufactured through the internal oxidation process of dilute Cu-0.15 wt.% Al alloy and was characterized by transmission electron microscopy to study the nature of oxide precipitates. As a result of internal oxidation, a small volume fraction (1%) of Al2O3 particles forms. In addition, a high density of extremely fine (2-5 nm) Cu2O particles has been observed to form epitaxially within the elongated Cu grains. These finely dispersed second-phase Cu2O particles enhance the Zener drag significantly by three orders of magnitude as compared to Al2O3 particles and retain their original size and spacing at elevated temperatures. This limits the grain boundary migration and the nucleation of defect-free regions of different orientations and inhibits the recrystallization process at elevated temperatures. In addition, due to the limited grain boundary migration, a bundle of stacking faults appears instead of annealing twins. This investigation has led to a better understanding of how to prevent the recrystallization process of heavily deformed metallic material containing oxide particles.

An Inspection Technique for Steel Pipes Wall Condition Using Ultrasonic Guided Helical Waves and a Limited Number of Transducers.

This research utilizes Ultrasonic Guided Waves (UGW) to inspect corrosion-type defects in steel pipe walls, providing a solution for hard-to-reach areas typically inaccessible by traditional non-destructive testing (NDT) methods. Fundamental helical UGW modes are used, allowing the detection of defects anywhere on the pipe's circumference using a limited number of transducers and measurements on the upper side of the pipe. Finite element (FE) modeling and experiments investigated generating and receiving UGW helical waves and their propagation through varying corrosion-type defects. Defect detection is based on phase delay differences in the helical wave's signal amplitude peaks between defective and defect-free regions. Phase delay variations were noted for the different depths and spatial dimensions of the defects. These results highlight the phase delay method's potential for NDT pipeline inspection.

NFCF: Industrial Surface Anomaly Detection with Normalizing Flow Cross-Fitting Network

The effective fully-supervised defect detection methods in industry are done by training many defect labels. These methods face application challenges because of insufficient defect samples. Moreover, the subtlety of defects, the similarity between defect-free and defect regions, and the interference factors carried by defect-free images also pose problems. To solve these difficulties above, we propose a Normalizing Flow Cross-Fitting network (NFCF) to use only defect-free images as priori knowledge for training. Specifically, the incremental broadening module in the network focuses on information expansion for minor defects, and the interactive filtering module completes the weight filtering through same-scale mutual reasoning. The processed information is fitted to a defect-free distribution by the normalizing flow module and the distribution is used as the basis for the determination. The NFCF does experiments on four types of datasets, and it achieves an average of 96.3% and 95.88% on two metrics, AUC-Image and AUC-Pixel. Experimental results show that the network can distinguish defect images using only defect-free training. Moreover, visualization experiments show that it can also complete localization segmentation for minor defects. Based on semi-supervision with achieved accuracy, NFCF alleviates the labeling dependence problem of defects and significantly increases the application value.

In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.

Tuning the Mottness in Sr3Ir2O7 via Bridging Oxygen Vacancies

The electronic evolution of Mott insulators into exotic correlated phases remains puzzling, because of electron interaction and inhomogeneity. Introduction of individual imperfections in Mott insulators could help capture the main mechanism and serve as a basis to understand the evolution. Here we utilize scanning tunneling microscopy to probe the atomic scale electronic structure of the spin-orbit-coupling assisted Mott insulator Sr3Ir2O7. It is found that the tunneling spectra exhibit a homogeneous Mott gap in defect-free regions, but near the oxygen vacancy in the rotated IrO2 plane the local Mott gap size is significantly enhanced. We attribute the enhanced gap to the locally reduced hopping integral between the 5d electrons of neighboring Ir sites via the bridging planar oxygen p orbitals. Such bridging defects have a dramatic influence on local bandwidth, thus provide a new way to manipulate the strength of Mottness in a Mott insulator.

Detection of subsurface defects in silicon carbide bulk materials with photothermal radiometry

<sec>With excellent physical, mechanical and processing properties, silicon carbide (SiC) has gradually become a preferred lightweight optical material for primary mirrors of large space optical systems. The subsurface defects generated during the preparation and processing procedures of SiC will affect the optical quality of the primary mirrors and the imaging performance of the corresponding optical systems employing the SiC primary mirror as well. In this work, photothermal radiation (PTR), a powerful nondestructive testing technique for detecting sub-surface defects of solid materials, is employed to characterize the subsurface defects of bulk SiC material for primary mirrors.</sec><sec>Theoretically, three-dimensional one-layer and three-layer PTR theoretical models are developed to describe the defect-free and defect regions of an SiC bulk material. By analyzing the frequency dependence of PTR phase of the SiC bulk material with different defect depths, an empirical formula for estimating the defect depth via a characteristic frequency (appearing at the minimum of the PTR phase-frequency curve) defined thermal diffusion length is proposed, and simulation results show reasonably good agreement between the estimated and simulated defect depths in a depth range of 0.05–0.50 mm. Experimentally, an SiC bulk sample with a subsurface defect region is tested by the PTR via position scanning and modulation frequency scanning to obtain the position and frequency dependent PTR amplitude and phase. From the spatial distributions of PTR amplitude and phase measured at different frequencies and the phase difference frequency curves of measurement positions in the defect region, the depth and shape of the defect region are estimated and found to be in good agreement with the actual shape of the defect region, which is destructively measured via a depth profiler. The experimental and calculated results demonstrate that the PTR is capable of detecting non-destructively the subsurface defects of SiC bulk material. In addition, for subsurface defects with relatively flat interface, the defect depth can be determined accurately by the developed empirical formula.</sec>

Phonon instabilities in a metal on the bain FCC–BCC transformation path

In this paper, the energy of the Bain path in Al and the instability of phonons during uniaxial compression deformation along <001> are studied ab initio. It is shown that, at a strain of about 15%, dynamic loss of structure stability is observed due to short-wavelength phonons, which thus determine the theoretical strength of Al. Deformation causes shifts along the {111} planes of the initial fcc cell, leading to the formation of stacking faults. A similar formation of stacking faults was observed in [1] in the framework of simulation of compression along the <001> Ni3Al nanoparticle (L12 superstructure based on the fcc structure). The results obtained can be applied to situations in the experiment, when small defect-free regions are deformed, for example, as in nanostructured materials and during nanoindentation.

Micro-strains, local stresses, and coherently diffracting domain size in shock compressed Al(100) single crystals

In situ x-ray diffraction (XRD) measurements and their analysis in Al single crystals shock compressed along the [100]-direction were utilized to examine shock wave induced microstructural heterogeneities. High-resolution XRD line profiles for the 200, 400, and 600 Al peaks were measured in uniaxial strain compression states to either 5.6 or 11.7 GPa and partial stress release to 3.5 or 6.6 GPa, respectively. Broadening of the XRD line profiles was analyzed to determine the magnitude of the longitudinal micro-strain distribution (0.195% and 0.28% full width at half maximum for 3.5 and 6.6 GPa stresses, respectively) and the size of coherently diffracting domains (CDDs) (0.125 and 0.07 μm for 3.5 and 6.6 GPa stresses, respectively). From the longitudinal micro-strain distributions, the distribution of local stress differences (or stress deviators) was obtained in the shocked state. The full width at half maximum of this distribution, a measure of the local stress inhomogeneities, is greater than half of the macroscopic stress difference for both 3.5 and 6.6 GPa peak stresses, suggesting considerable variation in local stress deviators. The CDD sizes determined here are comparable to characteristic length scales for defect-free regions determined from defect density measurements in post-shock recovery experiments. The present work represents an important step in understanding material microstructure and inhomogeneities in the shock-compressed state.

A consistent picture of excitations in cubic BaSnO3 revealed by combining theory and experiment

Among the transparent conducting oxides, the perovskite barium stannate is most promising for various electronic applications due to its outstanding carrier mobility achieved at room temperature. However, most of its important characteristics, such as band gaps, effective masses, and absorption edge, remain controversial. Here, we provide a fully consistent picture by combining state-of-the-art ab initio methodology with forefront electron energy-loss spectroscopy and optical absorption measurements. Valence electron energy-loss spectra, featuring signals originating from band gap transitions, are acquired on defect-free sample regions of a BaSnO3 single crystal. These high-energy-resolution measurements are able to capture also very weak excitations below the optical gap, attributed to indirect transitions. By temperature-dependent optical absorption measurements, we assess band-gap renormalization effects induced by electron-phonon coupling. Overall, we find for the effective electronic mass, the direct and the indirect gap, the optical gap, as well as the absorption onsets and spectra, excellent agreement between both experimental techniques and the theoretical many-body results, supporting also the picture of a phonon-mediated mechanism where indirect transitions are activated by phonon-induced symmetry lowering. This work demonstrates a fruitful connection between different high-level theoretical and experimental methods for exploring the characteristics of advanced materials.

Особенности эпитаксиального роста YBCO в окнах задающей маски

This work is devoted to the study of epitaxial YBCO films obtained by laser sputtering during deposition of YBCO into the windows of the special preliminary topology mask. The morphology of the surface of the obtained structures was studied by electron microscopy, the electrical characteristics of superconducting bridges were measured: the critical temperature and the critical current density. The possibility of forming YBCO structures of a given topology with defect-free regions of micron sizes while maintaining high electrophysical parameters of the superconductor is shown. This opens up the possibility of reproducibly forming submicron-scale elements in the created defect-free areas using ion etching or ion implantation.