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In order to study the effect of laser-excited surface waves on the transmission of surface rectangular defects, the transmission information of surface waves is used to achieve the quantitative calculation of defect depth. In this paper, the finite element method is employed to analyse the transmission process of laser-excited surface waves on rectangular defects. Subsequently, the influence of the front and back edges of rectangular defects on the transmission of surface waves is examined, and the formation mechanisms of feature points in the time-domain signals at detection points are elucidated. Ultimately, the calculation formula for defect depth is derived on the basis of the propagation path of each component after transmission. The experimental results are consistent with the simulation results, and the relative error of the surface rectangular defect depth calculation is within 9%. The validation of the proposed method on homogeneous isotropic metallic materials demonstrates the effectiveness and applicability of the proposed method. These results will provide a reliable method for characterising the depth of rectangular defects on surfaces by laser ultrasonic technology. [ABSTRACT FROM AUTHOR]

The optical properties of quantum dots are significantly influenced by photoactivation. However, the progress in the development of photoactivation carbon dot (CDs) is still stagnant. Herein, a method for preparing photoactivated CDs/NiFe2O4 by introducing affluent defects onto the surface of CDs through modification with NiFe2O4, extremely filling the gap of photoactivated CDs is proposed. Interestingly, a fantastic fluorescence detection strategy in a unique “OFF‐ON‐OFF” mode, combined with Ag+‐assisted fluorescence enhancement, to establish a dual‐channel regulation mechanism for ultra‐sensitive detection of Hg2+ is first proposed. By increasing surface traps and non‐radiative recombination, defect‐induced manipulation on the surface of CDs creates favorable conditions for enhancing photoactivation capacity. Unexpectedly, the CDs/NiFe2O4 shows a 25‐fold enhancement in fluorescence intensity after exposure to UV‐lamp. Compared to CDs/NiFe2O4, Ag+‐assisted photoactivation CDs/NiFe2O4 exhibits superior selectivity and sensitivity for Hg2+ detection, with low detection limits of 0.7 nM. Moreover, TiO2@CDs/NiFe2O4 catalyst is successfully prepared by anchoring CDs/NiFe2O4 onto the surface of TiO2, which demonstrates efficient degradation of tetracycline within 90 min under visible light irradiation, with a degradation rate of 98.3%. This work first provides a novel defects modification strategy for developing highly active photoactivated CDs technology that is significant for future environmentally sensitive applications. [ABSTRACT FROM AUTHOR]

Grain boundaries (GBs) and surface defects within perovskite films are inherent challenges that affect the photovoltaic performance of perovskite solar cells  (PSCs. In this work, Nylon 11 (N11) is utilized, a long‐chain polymer, for post‐treating the GBs and surface defects within FAPbI3 films. The multifunctional groups of N11 exhibit unique passivation abilities, enabling self‐regulation and selective correction of reverse‐charged defects. Post‐treating with N11 results in high‐quality FAPbI3 films characterized by tight GBs and low surface defect density. Despite fabrication under full open‐air conditions, the N11 post‐treatment significantly enhances the power conversion efficiency (PCE) value of FAPbI3 PSCs, increasing it from the reference value of 21.89% to 23.54%. Importantly, the long alkyl chain present in N11 significantly enhances the humidity stability of the PSCs. Unencapsulated PSCs treated with N11 maintain 89% of their initial PCE after exposure to air with 30% relative humidity (RH) for 1000 h, demonstrating resilience to elevated humidity levels. This work highlights the substantial improvement in the photovoltaic performance of PSCs achieved through the post‐treatment with N11. [ABSTRACT FROM AUTHOR]

Ball burnishing (BB) is a surface finishing process that involves moving the tool while pressing it against the workpiece under rolling contact. We investigated the effects of BB on the fatigue strength of an aluminum alloy with a semicircular slit simulating an initial surface defect. After performing BB under a typical condition, compressive residual stress was induced to a depth of 0.4 mm, with a reduction in surface roughness by 81%. Consequently, the fatigue strength increased by 22%. BB prevented the fatigue strength reduction caused by the 0.1 mm deep slit. The result indicated that BB could render the below 0.1 mm deep surface defect harmless in terms of fatigue strength. The maximum depth of surface defect that can be rendered harmless is 0.18 mm according to fracture mechanics, which is in agreement with the experimental results. Highlights: Ball burnishing (BB) can introduce large compressive residual stress along with smoothing.BB can render a 0.1 mm deep slit harmless in terms of fatigue strength.Defect size rendered harmless can be estimated based on fracture mechanics.Compressive residual stress is the main factor rendering the surface defect harmless. [ABSTRACT FROM AUTHOR]

The surface of superalloy precision castings might exhibit defects after forming, posing a significant risk to their service life, necessitating inspection during post-process. Radiographic inspection, with its extensive research in automation, can achieve efficient and accurate detection of defects. However, it is limited in surface defects detection due to limited sensitivity to non-volumetric defects and high cost. In contrast, fluorescent penetrant inspection (FPI) is highly efficient for surface defect inspection due to its low cost, high sensitivity, and speed. However, manual examination introduces variability in the results, impacting the consistency and reliability of the inspection process. Automation is needed to ensure consistency and reliability of inspection. The implementation of an automated defect identification system based on FPI using convolutional neural networks (CNNs) was systematically investigated. Among the CNN models tested, MobileNetV2 exhibited exceptional performance, achieving a remarkable recall rate of 0.992 and an accuracy of 0.992. Additionally, the effect of class imbalance on model performance was carefully examined. Furthermore, the features extracted by the model were visualized using Grad-CAM to reveal the attention of the CNN model to the fluorescent display features of defects. This study underscores the strong capability of deep learning architectures in identifying defects of precision casting components, paving the way for the automation of the entire FPI process. [ABSTRACT FROM AUTHOR]

Steam reforming for bio-oil and its derivatives is a highly promising method for hydrogen production. The development of low-price, highly active and stable catalysts is crucial for this technology. The Ni/CeO 2 with three morphologies were prepared in this work. The obvious differences in surface defects and oxygen vacancy concentrations of catalysts with different morphologies were confirmed by Raman and XPS. The Ni/CeO 2 -R with the highest surface defects and oxygen vacancy concentrations possessed the greatest catalytic performance. The highest hydrogen yield (72.01 %) and carbon conversion rate (86.61 %) were achieved over Ni/CeO 2 -R at 600 °C with the complete conversation of acetic acid. In addition, Ni/CeO 2 -R possessed the lowest apparent activation energy, the highest hydrogen production rate, and turnover frequency within 400–600 °C. More importantly, Ni/CeO 2 -R maintained stability during a 15-h durability test. This work would provide references for the design of catalysts for SRAA in the future. [Display omitted] • CeO 2 with specific morphologies were prepared using a simple hydrothermal method. • The change in morphologies enhances the oxygen vacancy concentrations of catalysts. • The change in CeO 2 morphologies enhances the activity and stability of catalysts. • The catalytic performance of Ni/CeO 2 -R is higher than that of common oxide supports. [ABSTRACT FROM AUTHOR]

Hot-rolled strip steel is a material widely used in production activities and daily life. However, the appearance of surface defects during its production process is inevitable. To address this issue, we introduce a new detection method using Gold-Yolo to detect surface defects on hot-rolled strip steel. Our method effectively balances accuracy and real-time performance while detecting four common types of surface defects, achieving an average accuracy rate of 82,2 % for detecting individual types of surface defects. Experimental data prove that our method excels in classifying and locating surface defects on hot-rolled steel strip, demonstrating broad application prospects and promotional value. [ABSTRACT FROM AUTHOR]

In response to the inevitable surface defects in the manufacturing process of hot-rolled steel, this paper proposes an improved steel surface defect detection model based on YOLOv7. In the Extended Efficient Large Aggregation Network (E-ELAN), the model replaces conventional convolution with Omni-Dimensional Dynamic Convolution (ODConv) to enhance the network's sensitivity to feature extraction using a combination of various attention mechanisms. Additionally, the detection head in the head section is replaced with an Efficient Decoupled Detection Head, enhancing the model's capability to classify and locate small defects. The proposed model is tested on the public dataset NEU-DET, achieving a high mAP of 76,5 %. This effectively enhances the model's ability to detect surface defects in steel while maintaining a fast detection speed. [ABSTRACT FROM AUTHOR]

The quality control of steel products' surface is of utmost importance, where several inspection techniques and technologies have been proposed over the last few years. Traditional manual inspection procedures face several limitations and often fall short in ensuring flawlessness. Vision-based strategies for automatic steel surface inspection have emerged as powerful and effective tools to solve various industrial-related problems, including product quality control. Therefore, the current study aims to improve the recognition rate of steel surface defects classification systems by introducing a novel classifier combination approach. The proposed system utilizes two distinct feature sets, namely LCCMSP and DCP, which were carefully selected based on a comprehensive comparative study of 19 state-of-the-art texture descriptors, considering both accuracy and time consumption. These generated features are individually fed to two classifiers, SVM and RF, leading to the creation of four base classifiers. In the final step, the Bayesian fusion rule is applied to integrate the outputs of these classifiers, ultimately providing the definitive classification decision. To evaluate the proposed system, two steel surface defects datasets, NEU-CLS and X-SDD, are utilized. The experimental results demonstrate that the proposed combination approach surpasses classical combination methods achieving remarkable outcomes compared to existing steel surface defects classification approaches. This highlights the effectiveness and superiority of the proposed system in accurately identifying and classifying steel surface defects while maintaining fast execution time. [ABSTRACT FROM AUTHOR]

[Objectives]Aiming at the low detection accuracy of apple surface defects in the process of non-destructive testing, an apple surface defect detection method based on DSCS-YOLO was proposed. [Methods]Firstly, in order to improve the network's ability to extract detailed features of surface defects, a deep and shallow feature selection module DSCS based on Dense and SE modules was designed. The C3 module in Backbone was replaced by DSCS, which strengthened the learning of important features and weakened redundant features on the basis of retaining superficial information of surface defects. In order to solve the problem of parameter coupling caused by excessive output information of Backbone and Neck, the Decoupled Head principle was used to make hierarchical prediction of Head layer. At the same time, the ELU activation function was used to improve the original Decoupled Head, simplify the terminal structure, and make the network training easier. Finally, to solve the difficulty of labeling surface defects, Wise-IoU loss function was used to replace CIoU loss function, which provided nonlinear gain for labeling of different qualities and realized dynamic focused learning of the network. [Results]DSCS-YOLO improved the detection of small targets, and the mean average precision of the apple surface defect test set reached 90.9%, which was 4.5%, 1.9%, 6.3% and 16.3% higher than that of YOLOv3-tiny, YOLOv5s, YOLOX-s and SSD, respectively, showing the best detection effect. At the same time, the number of model parameters was 9.54 M, and the inference time was only 2.8 ms. The detection speed was fast, which could meet the needs of practical applications. [Conclusions]The improved DSCS-YOLO improved the accuracy of YOLOv5s algorithm and realized the accurate identification of apple surface defects. [ABSTRACT FROM AUTHOR]

A mathematical model of defects of the inner surface of a ferromagnetic plate is presented. The model compares crack-type defects with wide-opening corrosion defects. [ABSTRACT FROM AUTHOR]

In the present work, the influences of surface defects on the motion of magnetic vortices in a mesoscopic type-II superconductor with randomly distributed pinning centers are considered using the time-dependent Ginzburg-Landau equations. Two kinds of surface defects are located in the boundary: first, the pinning centers. and second, the geometric defects simultaneously with pinning centers. In the simulation, the magnetization curves, vorticity, and the density of superconducting electrons for both different contents of pinning centers and various geometric defects are analyzed. For the pinning centers as surface defects, the maximum magnetization values as a function of the contents exponentially decrease, and the field H 1 where the first vortex penetrates and the field H 2 where the complete transition from superconducting to a normal state in the system occurs is reduced. For the geometric defects as surface defects, the density of superconducting electrons and the magnetization curves depend on both size and form of them. In addition, a threshold on the size of geometric defects in which the motion of vortices and vorticity changes is presented. [ABSTRACT FROM AUTHOR]

AbstractDesigning structural grillages is an innovative solution to repair material surface defects. The grillages have advantages as lightweight and directionally enhanced, but their stability and stress concentration under dynamic loads may pose significant risks. Three analytical models of grillages above a semicircular defect are constructed. The analytical solution of the frequency-domain wave field around the semicircular defect under SH wave incidence is solved. The complex variable function method is introduced to simplify the wave field expression. The accuracy is verified by comparing it with classic examples. Applying the Modified Time-Frequency Transform to obtain the time-domain wave field. Based on the Multipoint Transient Input Method (MTIM), the transient surface displacement response of the grillage above the defect is derived, mainly discussing the superposition behavior of input waves in the grillage. MTIM is based on the analytical solution of local time-domain wave field and takes the time histories of the surface characteristic points as the wave input. It has been validated as an effective method for studying the dynamic response of structures. The numerical results show that grillage form, incident angle, and wave velocity ratio are important factors affecting the structural responses of grillage above the defect. This research aims to reveal the propagation characteristics of waves in grillages in filling material defects, providing theoretical guidance for the design and application of grillages in this field. [ABSTRACT FROM AUTHOR]

Rational defect regulation is an effective way to enhance the performance of photoelectrocatalytic (PEC) water splitting. In this paper, we firstly constructed internal and surface defects to modulate the crystal structure of CdS, and investigated the synergistic impact of 0D (0‐dimensional) internal and surface defects regulation coupled with pyroelectric effects for optimizing the photoelectrocatalytic properties of CdS. It was found that the synergistic impact had a significant enhancement effect on the carrier separation and transfer, and the current density reached 3.93 mA/cm2 at 1.23 V vs. RHE increased by 16.38 times, meanwhile, the stability of the photoelectrodes was greatly promoted. The advantages and intrinsic relationships are also described: the formation of 0D dual‐type of defects alters the atomic arrangement in the crystal, optimizes the energy band structure, reduces carrier recombination, and increases carrier density. What's more, the introduction of defects induces electron redistribution and changes the state of dipoles inside the crystal, thus increasing built‐in electric field and generating more thermally generated carriers to optimize the pyroelectric effect. This work demonstrates the feasibility of defect modulation for optimizing pyroelectric performance and photocatalytic performance and bringing new light to PEC water splitting. [ABSTRACT FROM AUTHOR]

Micro surface defects (SDs) would be unavoidably induced on most fused silica surfaces (FSSs), substantially reducing their laser-induced damage thresholds (LIDTs). There is still no clear understanding of the relation between SD structural feature and their LIDTs, which poses a challenge to the recognition and mitigation of the dangerous SDs with low LIDTs in industrial production. In this work, the brittle-plasticity and stress states of the SDs are found to dominate the atomic point-defect densities on FSSs and resultantly dominate their LIDTs for the first time. Based on this, the SDs have been divided into four categories: pressure-induced brittle defects (PBDs), pressure-induced plastic defects (PPDs), tension-induced brittle defects (TBDs) and tension-induced plastic defects (TPDs). Based on the tested LIDTs, laser damage morphologies and photoluminescence (PL) characteristics, the laser damage resistance of different kinds of SDs are compared, whose reasons are revealed on the basis of the molecular dynamics (MD) simulations. The sharp increase of the atomic point-defect density from the plasticity to the brittleness of the material, the significant disparity in tensile and compressive strength of fused silica, the distinctly different material response behaviours under tensile and compressive forces, as well as the opposite effects of the tensile and compressive forces on the material compactness significantly affect the atomic point-defect densities on FSSs and resultantly contribute to the different laser damage resistance of different kinds of SDs. This work possesses great significance in solving the laser damage on FSSs. It could also provide a new understanding of the impact of the tensile and compressive forces on the LIDTs of various optical elements. [ABSTRACT FROM AUTHOR]

The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm. [ABSTRACT FROM AUTHOR]

Recently, hybrid injection molding—a type of overmolding technology in which a short carbon fiber-reinforced thermoplastic is injection molded over a compression-molded carbon fiber-reinforced thermoplastic (CFRTP) sheet—has been introduced. A heat-and-cool hybrid injection molding technique has also been introduced for practical use. The technique yields high-quality molded products. This is achieved through the heating of the mold cavity surface to a temperature higher than the melting point of the base polymer impregnated into the carbon fiber fabric. However, few experimental analyses of the molding phenomena in heat-and-cool hybrid injection molding have been reported. In particular, the effect of the processing conditions on the transfer of the mold cavity surface shape to the CFRTP sheet has not been clarified in detail. Therefore, it has been impossible to take extensive measures when defects are generated in molded products. In this study, a mold is designed and fabricated for use with far-infrared radiation heating, a variotherm technology that is suitable for the experimental analysis of the heat-and-cool hybrid injection molding phenomenon. In particular, a mold is designed and fabricated to continuously perform the following three processes using only an injection molding machine: (1) the radiation heating of both the CFRTP sheet and the mold cavity surface using a far-infrared radiation heater, (2) the compression molding of the CFRTP sheet, and (3) the injection molding of the melt. The effects of the heating conditions of the mold and the injection molding process conditions on the appearance characteristics of the molded products are clarified using this mold and a far-infrared radiation heater. [ABSTRACT FROM AUTHOR]

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide. [ABSTRACT FROM AUTHOR]

Fe-6.5wt%Si alloy possesses excellent electromagnetic properties, including high resistivity, low iron loss, high magnetic permeability, and almost zero magnetostriction coefficient, making it an ideal core material for low-noise and low-iron-loss applications. However, the higher silicon content promotes the formation of ordered phases B2 and D03, which significantly increases the material's brittleness. Traditional rolling processes are inadequate for producing Fe-6.5wt%Si alloy thin ribbons. Planar Flow Casting, on the other hand, has a high cooling rate, which can suppress the formation and growth of ordered phases in high-silicon steel, reduce the material's brittleness, and produce Fe-6.5wt%Si alloy thin ribbons. However, Fe-6.5wt%Si alloy ribbons produced by Planar Flow Casting have gaps on the as-cast surface and micro-roughness defects on the free surface at small scales. Simulation results indicate that the gap on the as-cast surface is caused by the formation of air-pocket due to the entrapment of gas into the puddle. The presence of air-pocket can affect the heat transfer between the melt and the cooling roll, consequently influencing the fluid velocity and causing fluctuations on the free surface, and even leading to fracture of the thin ribbon. The wetting behavior of the melt on the cooling roll surface influences the entrainment of air-pocket, and favorable wetting conditions can reduce the entrainment of air-pocket, thereby improving the surface quality of the alloy thin ribbon. [ABSTRACT FROM AUTHOR]

In the present study, Neodymium (Nd), Cadmium (Cd), and the various molar ratios of Nd: Cd doped copper oxide nanocomposites (CuO NCs) were prepared by the co-precipitation method. The as-synthesized Nd, Cd, and Nd:Cd doped CuO NCs [Cu 1-x + y Nd x Cd y O, (x:y = 0.006:0.00, 0.00:0.006, 0.005:0.001, 0.004:0.002, 0.003:0.003, M)] were characterized through various instrumentation techniques such as TGA, UV–Vis–NIR, FTIR, Raman, FL, XRD, ZP, FE-SEM with EDAX elemental mapping, HR-TEM and XPS analyses. Further, the antibacterial activity of doped CuO NCs was tested against Staphylococcus aureus and Escherichia coli. An equal molar ratio of Nd and Cd doped CuO NCs showed excellent antibacterial activity mainly due to the synergistic effect of sufficient Nd3+, Cd2+, and Cu2+ ions releasing ability. Interestingly, the doping effect enhances surface defects and decreases the ability to scavenge free radicals compared to pure CuO nanomaterials. At the same time, the cytotoxicity of NCs was evaluated on the human lung epithelial L132 cell line. Evidently, 75 μg/ml concentration of Nd:Cd doped CuO NCs samples shows 80% viability, which confirms their negligible cytotoxic effect. The Nd:Cd doped CuO NCs (94:3:3) had a high aspect ratio shape, remarkable ion-releasing ability, and biocompatibility while being thermally stable. Because of these qualities, they are well suited for treating bacterial infections in the biomedical area. • Nd and Cd doped CuO NCs was successfully synthesized by co-precipitation method. • The equal molar ratio of Nd and Cd excellently substituted the CuO matrix and extends growth direction [0 1 0] plane. • CuO NCs showed ROS-mediated antibacterial activity in S. aureus and E. coli via releasing Nd3+ and Cd2+ ions. • Cytocompatibility of CuO NCs against L132 cell line opens-up the possibilities for further pre-clinical investigations. [ABSTRACT FROM AUTHOR]

The defect-induced laser damage on mechanically machined fused silica optical surfaces under intense laser irradiation severely threatens the laser damage resistance of optical components. The lack of a quantitative relationship between the feature information of surface defects and the laser-induced damage thresholds (LIDTs) of optical surfaces poses a challenge to effectively evaluate the influence of the surface defects on the LIDTs of optical surfaces. Herein, the photoluminescence (PL) properties of the areas where laser damage tends to occur in surface-defect zones are explored. The PL induced by the point defects at the atomic scale in these areas is found to be obviously higher than that of other positions. The PL envelope area (S) at the position with the highest PL intensity among the whole surface defect is found to be inversely proportional to the LIDT (Φ) of the optical surface for the first time (SΦ = N 0). It is beneficial to quantitatively characterize the influence of the surface defect and provide new insights in elucidating the defect-induced laser damage mechanisms. Besides, we found that there is a minimum LIDT for the mechanically machined optical surfaces with various surface defects. On basis of this, the maximum influence of the surface defects with different species on the LIDTs of optical surfaces could be determined. Based on the constructed relationship (SΦ = N 0) and the developed MD model, two causes of the minimum LIDT are determined. The PL envelope area of the surface defect would slightly affect the LIDT when the PL envelope area is large enough, which is the first cause. The mechanical force could compact the optical surface, increasing the densities of Si and O atoms. However, the densities of Si and O atoms become stable when the mechanical force is large enough, causing the highest densities of point defects, which is the second cause. [ABSTRACT FROM AUTHOR]

A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the printout mesh to the closest point on the part model. These distances comprise the global deviation of the printout from the model. When the error is within a threshold, the algorithm compares the curvature at each printout vertex to that at the closest part point. Qualitative differences in curvature indicate local defects, such as blobs, cracks, surface roughness, and faded embossing and engraving. The results are grouped into local defect regions that are displayed. We demonstrate the algorithm on metal printing and on PLA 3D printing. [ABSTRACT FROM AUTHOR]

Epitaxial growth of InN films on GaN ( 0001 ) by metal organic chemical vapor deposition ( MOCVD) was investigated with adding an InGaN bedding layer. It is found that pure InN can be obtained on the InGaN bedding layer with quality higher than on bare GaN surface. By adopting proper In composition (In0. 23Ga0. 77N in this work) in the InGaN bedding layer, indium droplet formation can be totally suppressed during the growth of InN. The structure and optical properties of InN were investigated by using optical microscopy, high-resolution X-ray diffraction (HR-XRD), transmission electron microscopy (TEM), photoabsorption and photoluminescence at room temperature. From the HR-XRD measurements, the ω and ω-2θ scans show the elimination of In diffraction signal by using the InGaN bedding layer and the ω scan gives a linewidth of 0. 23° for 150 nm InN film. From the selective area electron diffraction pattern in the TEM, the InN is found to be almost strain free. Both photoabsorption and strong photoluminescence of InN film at room temperature indicate that the energy band gap of the InN is about 0. 74 eV. Abnormal excitation-dependent photoluminescence behavior of the InN was preliminarily investigated too. It is proved that the surface effect of InN materials has a strong effect on the radiation recombination. [ABSTRACT FROM AUTHOR]

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models' size. [ABSTRACT FROM AUTHOR]

Ski goggles help protect the eyes and enhance eyesight. The most important part of ski goggles is their lenses. The quality of the lenses has leaped with technological advances, but there are still defects on their surface during manufacturing. This study develops a deep learning-based defect detection system for ski goggles lenses. The first step is to design the image acquisition model that combines cameras and light sources. This step aims to capture clear and high-resolution images on the entire surface of the lenses. Next, defect categories are identified, including scratches, watermarks, spotlight, stains, dust-line, and dust-spot. They are labeled to create the ski goggles lenses defect dataset. Finally, the defects are automatically detected by fine-tuning the mobile-friendly object detection model. The mentioned defect detection model is the MobileNetV3 backbone used in a feature pyramid network (FPN) along with the Faster-RCNN detector. The fine-tuning includes: replacing the default ResNet50 backbone with a combination of MobileNetV3 and FPN; adjusting the hyper-parameter of the region proposal network (RPN) to suit the tiny defects; and reducing the number of the output channel in FPN to increase computational performance. Our experiments demonstrate the effectiveness of defect detection; additionally, the inference speed is fast. The defect detection accuracy achieves a mean average precision (mAP) of 55%. The work automatically integrates all steps, from capturing images to defect detection. Furthermore, the lens defect dataset is publicly available to the research community on GitHub. The repository address can be found in the Data Availability Statement section. [ABSTRACT FROM AUTHOR]

Object surface defect detection technology is an important subject in the field of industrial quality inspection, which is of great significance to industrial production. This paper mainly summarized the surface defect detection techniques based on machine vision in recent years. Firstly, it listed several application scenarios of defect detection in the industry. Secondly, from the perspective of feature extraction and classification algorithms, it expounded on traditional machine vision methods. Then, this paper focused on the classical neural network structure and the latest development of defect detection algorithms and introduced two commonly used optimization methods of the defect detection algorithm. Finally, it analyzed three major challenges in the field of defect detection; real-time problems, small sample problems and small target problems. The purpose was to provide useful references and context for the research of industrial surface defect detection. [ABSTRACT FROM AUTHOR]

Concrete surface defects are very complex and diverse, which is a great test for repair materials. The efficiency and durability of the repair system depend on the bonding effect between the concrete and the repair material. However, the rapid increase in system viscosity during the reaction of repair materials is an important factor affecting the infiltration effect. In the present work, the infiltration consolidation repair material was prepared, and its basic properties (viscosity, surface drying time and actual drying time, infiltration property) and mechanical properties were evaluated. Finally, the infiltration depth, film-forming thickness, and anti-spalling ability of concrete under a single-side freeze–thaw cycle are revealed. The results showed that using ethyl acetate could rapidly reduce the viscosity of the repair material, and the repair material could penetrate 20–30 mm into the concrete within 10 min. It was found by laser confocal microscopy that the thickness of the film formation after 3 days was only 29 µm. In the mortar fracture repair test to evaluate the bond strength, the bond strength of the repaired material reached 9.18 MPa in 28 days, and the new fracture surface was in the mortar itself. In addition, the freeze–thaw cycle test was carried out on the composite specimens under salt solution to verify the compatibility of the designed repair material with the concrete substrate. The data showed that the average amount of spalling was only 1704.4 g/m2 when 10% ethyl acetate was added. The penetrating repair material in this study has good infiltration performance, which can penetrate a certain depth in the surface pores and form a high-performance consolidation body, forming a "rooted type" filling. [ABSTRACT FROM AUTHOR]

This study investigated the formation of protrusion-type flow marks during electrochemical machining of 6061 aluminum. The results indicate that the growth of a viscous, byproduct layer on the anode surface can occur rapidly within regions of the interelectrode gap where electrolyte flow velocity is very low. The growth in thickness of this viscous layer can slow down aluminum cation diffusion and thus the local material removal rate. Differences in the material removal rate between these regions and surrounding regions of smaller byproduct thickness led to protrusion-type flow marks. Once electrolysis ceases, the by-product is quickly stripped away from the anode surface by flowing electrolyte, even in regions of low flow velocity. Consequently, this byproduct is typically not observed after the anode is removed cathode–anode assembly. This study also indicates that an easy way to avoid this type of flow mark formation is to avoid large, step transitions in the anode surface. These should be replaced with gentler transitions that minimize electrolyte flow separation. [ABSTRACT FROM AUTHOR]

Machine vision plays an increasingly important role in industrial product quality detection. During processing, scratches, dents and other defects are inevitable on the surface of a smooth part. Although surface defects do not affect the overall performance of the product, their existence is unacceptable when a perfect product is required. The surface defect detection method based on machine vision and deep convolutional neural networks overcomes, to a certain extent, the problem of low detection efficiency, high false detection and missing detection rates in the traditional detection method. In this paper, a multistream semantic segmentation neural network is proposed to identify defects on smooth parts. Taking a seatbelt buckle as an example, the scratch and crush defects on the surface are classified. The network takes DeepLabV3+ as the framework and three types of image stream as the input of the network. In the backbone feature extraction network, the Xception structure is improved to MobilenetV2 and the convolutional block attention module (CBAM) is introduced into the decoding network, which improves the operational efficiency and accuracy. Compared with other classical networks, this network demonstrates good performance in the image dataset of the seatbelt buckle and realises fast and accurate semantic segmentation and classification of surface defects. The evaluation results of the network model have been significantly improved. [ABSTRACT FROM AUTHOR]

Properties of oxidized InP surfaces, which are known to cause less electrical and optical losses than other III–V oxides, are relevant to develop the passivation of current and future applications of III–V crystals (e.g. lasers, detectors). We report that a proper low-temperature (LT) (<400 °C) gas exposure with NH3 or O2 enables beneficial modifications of InP native oxides, of which formation is difficult to avoid in devices. Effects of the gas exposure depend on the doping of InP. NH3 exposure without a plasma source at 250 °C or lower temperature increases photoluminescence (PL) intensity of native-oxide covered n-InP crystals, which interestingly provide a stronger PL signal than n-InP with the HCl-cleaned surface. In contrast, O2 exposure around 300 °C increases PL signal of native-oxide covered p-type InP. Core-level photoelectron spectra reveal that N atoms are incorporated into the native oxide system of InPO4/InP during LT NH3 exposures. Scanning tunneling microscopy shows a band bending and a tendency to crystallization at native-oxide covered InP surfaces. Photoelectron spectra, which are analyzed with recent calculations, show larger variation in the bonding environment for the host In atoms and for incorporated N atoms, as compared to the P bonding sites in the InPO4 native oxide. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) were considered a promising activator for persulfates due to their high electrical conductivity, large specific surface area and low toxicity. The functional groups and surface defects of CNTs could significantly affect their activation performance. In this study, CNTs with high C=O ratio and defect density (CNT-O-H) were prepared through a facile treatment of raw CNTs with HNO3 oxidation followed by calcination at 800 °C under an argon atmosphere. X-ray photoelectron spectroscopy (XPS) and Raman results showed that the C=O proportion and defect degree (ID/IG) rose to 75% and 1.53, respectively. The obtained CNT-O-H possessed a superior performance towards peroxydisulfate (PDS) activation, and the degradation efficiency of tetracycline (TC) in the CNT-O-H/PDS system was increased to 75.2% from 56.2% of the raw CNTs/PDS system within 40 min. Moreover, the activity of CNT-O-H after use could be easily recovered with re-calcination. In addition, the CNT-O-H/PDS system exhibited high adaptabilities towards wide solution pH (2–10), common coexisting substances and diverse organic pollutants. Singlet oxygen (1O2) was confirmed to be the dominant reactive oxygen species (ROS) generated in the CNT-O-H/PDS system. It was inferred that surface C=O groups and defects of CNTs were the key site to activate PDS for TC degradation. [ABSTRACT FROM AUTHOR]

Ceramic tiles are controlled to detect surface defects after production because many defects may occur on their surface during production. The detection of ceramic tile surface defects is usually performed by human observations in most factories. In this paper, an image processing method was proposed to detect the defects. In the proposed method, first, the user selects the homogenous region in the image. Then the gradient-based image processing algorithm is applied. We conducted our study using simulated and real images to which we applied the conventional image processing methods and our proposed method. Performance of the proposed method was evaluated with quality metric and subjective evaluation. The obtained results demonstrate that the proposed method has very good performance and is very promising for ceramic tile application. [ABSTRACT FROM AUTHOR]

• A deep learning-based framework is proposed to investigate defect-induced fatigue scatter. • X-ray tomography of surface morphology is regenerated using SliceGAN architecture for the first time. • Reveal the influence of microscale surface defect on stress concentration and fatigue scatter. • Identify fatigue lifetime indicator with correlation analysis. • Develop probabilistic stress-life curve to reduce uncertainty in fatigue design. The layer-based deposition mechanism during electron beam powder bed fusion (PBF-EB) inevitably induces significant surface irregularities, which often act as stress concentrators under cyclic fatigue load, resulting in early fatigue failure and scattered lifetime. Reliable design of additively manufactured metallic lattice structures necessitates a comprehensive understanding of the fatigue behaviour of micro-sized structural parts, typically obtained through expensive and time-consuming full-scale fatigue testing. This study proposed a deep learning-based framework to predict defect-induced fatigue scatter in micro-sized as-built titanium parts produced with PBF-EB technique. The surface morphology synthesized by generative adversarial networks (GANs) was incorporated with finite element modelling to investigate defect-induced stress concentrations. A fatigue damage model, based on continuum damage mechanics and theory of critical distances, was developed to predict fatigue lifetime. The synthesized morphology showed good agreement with experimental results in roughness evaluation. Using the proposed framework, the fatigue scatter was successfully reproduced, and a probabilistic lifetime design curve was developed accordingly to facilitate reliable and cost-effective fatigue design. The primary contributions of this study are the first-time application of a GAN architecture for synthesizing realistic morphology of micro-sized as-built PBF-EB Ti6Al4V parts with significant surface irregularities, and the investigation of defect-induced fatigue scatter using synthetic morphology and numerical methods. The results provide new insights into the effects of surface irregularity on stress concentration and scattering of lifetime in micro-sized as-built titanium parts. [Display omitted] [ABSTRACT FROM AUTHOR]

ZnO nanoparticles is a highly promising semiconductor material as an electron transfer layer layer (ETL) in various optoelectronic devices. The cost-effective and low-temperature preparation method renders it ideal for the development of flexible and large-scale modules, aligning seamlessly with the commercialization process. However, Solution-derived ZnO exhibits a significant number of surface defects, which affect the device's photoelectric conversion performance and stability. Herein, a simple strategy focusing on controlling the nano size of ZnO was employed to mitigate these surface defects. Welded ZnO films were prepared via spin coating with a precise amount of ZnO sol into dispersion. These films were characterized via techniques such as TEM, FSEM XRD, DLS, UV/vis and so on. PL spectra revealed a notable decrease in defect density with the increase in nano size. AFM reveals that films doped with 15 % sol exhibit root mean square (RMS) values of less than 1 nm, indicative of improved smoothness. Moreover, the four-point probe detected enhanced electrical conductivity attributed to the compact structure and improved integrity of the films. The presence of nanoparticles facilitates the crystallization of ZnO sol, allowing for an annealing temperature during the welding process to as low as around 90°C, compatible with flexible plastic substrates such as PET. The welded ZnO films produced at a low temperature in this study demonstrate high potential as ETL for flexible optoelectronic devices, which is conducive to their commercialization and large-scale deployment. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Ligand-coordinated Pt SAC on defective TiO 2 exhibits high activity for the water–gas shift reaction. • Pt-ligand SAC supported on defective TiO 2 follows redox mechanism in the WGS reaction. • The metal–ligand strategy stabilizes Pt as single atoms at reaction temperatures. • The metal–ligand strategy facilitates the redox mechanism by reducing CO binding energy and promoting CO 2 and H 2 formation. The water–gas shift (WGS) reaction (C O + H 2 O → CO 2 + H 2 , Δ H = - 41.1 k J / m o l) is an essential process in the production of hydrogen and has grown significantly in usage over time. In this study, we further explore a novel strategy that can create single-atom catalysts (SACs) from metal-ligand single-sites on high surface area oxide supports for catalyzing the WGS reaction. The metal–ligand approach results in high metal loading, exhibits an ultimate dispersion of metal species and maximizes atom efficiency. 1,10-phenanthroline-5,6-dione (PDO) was chosen as the ligand for its oxidative potential for stabilizing Pt metal cations via two different binding pockets. The single-atom nature of the Pt-ligand is characterized with extended X-ray absorption fine structure (EXAFS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and transmission electron microscopy (TEM) techniques. The catalytic activity is evaluated for the WGS reaction, revealing that Pt-ligand SACs supported on defective TiO 2 show higher inherent catalytic activity than Pt NPs and a significantly lower activation energy. This characteristic is generally desirable for the redox mechanism. Density functional theory (DFT) calculations reveal that metal–ligand SACs allow CO interaction with oxygen atoms from the TiO 2 surface. The redox mechanism of the WGS reaction demonstrates a lower effective activation barrier than the associative mechanism for Pt-ligand SAC. Moreover, the metal–ligand strategy, according to DFT results, facilitates the reaction redox mechanism by reducing the CO binding energy and promoting CO 2 and H 2 formation process. [ABSTRACT FROM AUTHOR]

• The wheel material accumulates at the leading edge of the defect and tends to flow continuously into the defect. • The material is removed at the trailing edge and the size of the surface defect is gradually reduced. • The RCF crack starts near the middle edge of the defect and then expands in a "C" shape. • FEA results show that the maximum residual tensile stress is located on the lower surface of the defect. • The experimental phenomena are basically consistent with the finite element simulation results. Severe wheel–rail rolling contact fatigue (RCF) can be affected by the surface defects, which threaten the operational safety of rail vehicles. To explore the RCF damage mechanisms of the wheel–rail under the influences of surface defects, an indentation device was used to prepare surface defects. Twin-disc RCF and wear tests and finite element analyses (FEAs) were conducted. The influence of surface defects on wheel–rail RCF damage were explored by observing the surfaces and cross-sections of defects. The results show that as the number of cycles increases, an RCF crack is generated in the maximum stress concentration area of the defect, i.e., the region near the middle edge, and it expands in the form of a "C" shape. The leading edge of the defect spalls first due to the large number of laminar cracks. As the cracks continue to propagate, large spalls can be seen. The FEA results show that the residual stress distributed on the shoulder around the defect is the largest. The maximum residual tensile stress is located on the lower surface of the defect and the largest stress concentration is located on both sides of the middle edge of the defect when the wheel–rail rolls. [ABSTRACT FROM AUTHOR]

• This study utilizes the integration of finite element simulation data and actual detection data to establish digital twin detection information. • An adaptively updating defect detection model based on GANS-ResNet50-RA is proposed. • The method effectively enhances detection accuracy and generalizes well across various settings, even with limited sample sizes. To enhance the efficiency and intelligence of laser ultrasonic detection of metal surface defects, a detection method based on digital twins is proposed. Given the scarcity of defect samples and the difficulty of obtaining labeled data in practical applications, the finite element method is used to construct a laser ultrasonic metal defect detection model, effectively generating twin data for defect detection. With the help of generative adversarial networks, the distribution difference between twin data and real data is minimized. Additionally, by using continuous wavelet transform, one-dimensional ultrasonic signals are converted into two-dimensional time–frequency images for detection input. In terms of detection algorithm development, a residual network with an optimized global attention mechanism is introduced to ensure real-time synchronization and updates between the digital twin model and the actual laser ultrasonic detection equipment. Finally, experimental validation of the proposed method shows that it effectively addresses the issue of small sample sizes in defect detection and achieves high detection accuracy. Moreover, comparative experiments on crack defect migration tasks under different settings demonstrate the method's good generalization ability. [ABSTRACT FROM AUTHOR]

• We examine the wetting resistance of different surface chemistries on MD membranes. • A longer chain alkylsilane shows higher crystallinity and wetting resistance. • PDMS exhibits the same surface energy as the longest silane, but lower wettability. • The high mobility of PDMS brushes leads to minimization of surface defect exposure. Membrane distillation (MD) equipped with omniphobic (non-wetting) membranes has found a niche in water reclamation from hypersaline industrial wastewater. Here, we examined the efficacy of non-fluorinated materials as surface coating agents for omniphobic MD membrane fabrication, and identified necessary mechanisms to attain a maximized wetting resistance using fluorine-free materials. We first prepared MD membranes with different surface chemistries using a series of linear alkylsilanes and polydimethylsiloxane (PDMS) as representative fluorine-free, low surface energy materials. Membranes modified with a longer chain alkylsilane exhibited a lower surface energy and demonstrated a greater wetting resistance in direct contact MD experiments using feedwaters of various surface tensions. Despite the nearly identical surface energy measured for the longest alkylsilane and PDMS, PDMS-modified membrane exhibited an extended antiwetting performance as compared to the membrane treated with the longest alkylsilane. To elucidate the source of the distinctive wetting resistance, we examined the nucleation and condensation kinetics on the surfaces with the different surface chemistries via environmental scanning electron microscopy. Our analysis suggests that the membranes treated with long chain alkylsilanes contain surface defects (i.e., hydrophilic regions) whereas the high mobility of the PDMS effectively minimizes the defect exposure, slowing down the condensation and subsequent surface wetting. [Display omitted] [ABSTRACT FROM AUTHOR]

• A segmentation model for citrus surface defcets driven by superpixel features was proposed. • Multiscale superpixel region feature (MSRF) branch was proposed to strengthen the feature learning ability of multiscale defects. • Superpixel Loss was designed to optimize the segmentation performance of boundary. • Multiscale channel cross-fusion transformer (MCCT) was introduced to reconstruct the skip connection layer, realizing the fusion of multiscale features. The segmentation of surface defects in citrus fruit makes a significant contribution to quality assessment and post-harvest processing. However, semantic segmentation of citrus surface defects remains challenging owing to the complexity of defect features, unclear defect boundaries and semantic gaps in models. Therefore, a superpixel driven and multiscale channel cross-fusion transformer (MCCT)-based UNet (named SPMUNet) was proposed to segment citrus surface defects. The model first created a multiscale superpixel region feature (MSRF) branch using the simple linear iterative cluster (SLIC) algorithm to form the key features embedded in the downsampling layer. Then, the proposed Superpixel Loss optimized the loss function based on superpixel grid to provide error information for boundary segmentation. In addition, the MCCT module was used to improve the skip connection layers, and feature fusion was achieved by collaborative learning. Finally, the performance of the model was validated through ablation studies, feature map visualization, and comparison experiments. The results showed that the mPA, mIoU, and dice coefficients of SPMUNet for citrus surface segmentation were 89.1%, 83.8%, and 88.6%, respectively. Compared with the baseline, the proposed MSRF branch, MCCT, and Superpixel Loss improved mIoU by 2.6%, 1.3%, and 4.2%, respectively. And the effectiveness of the proposed methods was also validated by the feature map visualization. Furthermore, the proposed SPMUNet achieved optimal segmentation performance compared to other semantic segmentation models (UNet, ResUNet++, DeepLabv3+, SwinUNet, TransUNet). SPMUNet also excelled in the segmentation of small targets and boundaries, improving by 10.2% and 7.0%, respectively, over the baseline. The improvement in segmentation performance demonstrated the effectiveness of the selected superpixel features. The proposed citrus surface defect segmentation model achieved high-precision segmentation and serves as a technical reference for citrus sorting and quality assessment. [ABSTRACT FROM AUTHOR]

An experimental setup has been created that allows measuring the values of magnetic field components above the surface of a metal plate in automatic mode. The experimental setup takes the readings of magnetic Hall transducers and sends the processed information to a computer in the form of text and graphic files. Based on experimental data, a component of the measured magnetic field, the shape of surface defects in a plate made of steel 20 was determined using the technique of reconstructing the metal–air boundary along magnetic lines of force. The magnetic transducer, a three-axis digital Hall sensor ALS31300, took measurements over a defect-free metal surface. The nonlinear properties of the ferromagnet are taken into account. [ABSTRACT FROM AUTHOR]

CdZnTe thin films were prepared on ITO glass by magnetron sputtering method to explore the effect of mechanical grinding and polishing on the resistive switching characteristics of CdZnTe thin films. Through the analysis of experimental results such as XRD patterns, Raman spectroscopy, AFM micrograph, etc., the physical mechanism of mechanical grinding and polishing affecting the resistive switching characteristics of CdZnTe thin film was elucidated. The results show that the thin films prepared by magnetron sputtering are sphalerite structures with F43m space group. Mechanical grinding and polishing improves the crystallization quality of CdZnTe thin films; the roughness (Ra) of CdZnTe film decreases from 3.42 nm to 1.73 nm after grinding and polishing. Transmittance of CdZnTe thin film after grinding and polishing and the vibration peak of CdTe-like phonon peaks at 162 cm-1 were enhanced. After grinding and polishing, the resistive switching ratio of CdZnTe thin film increases from 1.2 to 4.9. The reason why mechanical grinding and polishing improves the quality and resistive switching characteristics of CdZnTe thin films may be that CdZnTe thin films are recrystallize during the grinding process. [ABSTRACT FROM AUTHOR]

Surface defects of fiber-reinforced resin matrix composites (FRRMCs) adversely affect their appearance and performance. To accurately and efficiently detect the three-dimensional (3D) surface defects of FRRMCs, a novel lightweight and two-stage semantic segmentation network, i.e., Mask-Point, is proposed. Stage 1 of Mask-Point is the multi-head 3D region proposal extractors (RPEs), generating several 3D regions of interest (ROIs). Stage 2 is the 3D aggregation stage composed of the shared classifier, shared filter, and non-maximum suppression (NMS). The two stages work together to detect the surface defects. To evaluate the performance of Mask-Point, a new 3D surface defects dataset of FRRMCs containing about 120 million points is produced. Training and test experiments show that the accuracy and the mean intersection of union (mIoU) increase as the number of different 3D RPEs increases in Stage 1, but the inference speed becomes slower when the number of different 3D RPEs increases. The best accuracy, mIoU, and inference speed of the Mask-Point model could reach 0.9997, 0.9402, and 320,000 points/s, respectively. Moreover, comparison experiments also show that Mask-Point offers relatively the best segmentation performance compared with several other typical 3D semantic segmentation networks. The mIoU of Mask-Point is about 30% ahead of the sub-optimal 3D semantic segmentation network PointNet. In addition, a distributed surface defects detection system based on Mask-Point is developed. The system is applied to scan real FRRMC products and detect their surface defects, and it achieves the relatively best detection performance in competition with skilled human workers. The above experiments demonstrate that the proposed Mask-Point could accurately and efficiently detect 3D surface defects of FRRMCs, and the Mask-Point also provides a new potential solution for the 3D surface defects detection of other similar materials [ABSTRACT FROM AUTHOR]

Martensitic AISI 420 steel has widespread applications in the manufacturing of shafts of large vertical pumps, various dental and surgical instruments because of its better mechanical, heat and corrosion resistance properties. However, the machining of this steel possesses a great challenge because of its poor thermal conductivity. Therefore, the current study aims to study the performance of different coated carbide tool (multi-coated TiN-Al2O3-TiCN-TiN, multi-coated TiN-TiCN-Al2O3-TiN coated, single TiAlN coated) and uncoated carbide tool during the dry turning of martensitic AISI 420 steel at different cutting parameters (speed, feed, depth). The machining performances such tool wear, surface defect, residual stress, cutting force and chip morphology are assessed. Abrasion, built-up edge, adhesion and micro-cracks are the wear mechanism observed in the cutting tools. The uncoated tool showed minimum generation of residual stress on the machined surface followed by single TiAlN coated tool. The influence of tool coatings and machining parameters on the various geometrical characteristics of the sawtooth chip has been investigated. It is perceived that the TiAlN coated tool results in minimum frequency and maximum chip segmentation ratio of chips compared to other tools. Out of the different coated and uncoated carbide tools utilized during the turning of martensitic AISI 420 steel, the single-layer TiAlN coated tool performed superior in terms of reduction of tool wear, cutting forces and surface roughness compared to the other coated tools and uncoated tool. The results indicate an average percentage reduction in flank wear, surface roughness, feed force and tangential force of 70.5, 42.8, 24.1 and 20.7%, respectively, using the TiAlN coated tool compared to the uncoated tool. The current investigations recommend the TiAlN coated tool with a feed rate in the range of 0.10–0.25 mm/rev while machining martensitic AISI 420 steel because of its excellent overall performance characteristics. [ABSTRACT FROM AUTHOR]

Hot-rolled steel strip is widely used in production life and surface defects inevitably occur during its production process. In order to solve this problem, this paper proposes a method for classifying and locating surface defects in hot-rolled strip steel based on the YOLOV7 target detection model, which takes into account both accuracy and real-time performance. The method is capable of distinguishing seven common surface defects with an average accuracy of 84,3 % and a maximum accuracy of 99,6 % in a single category of defects. [ABSTRACT FROM AUTHOR]

Passivation of the defective surface in perovskite absorber layers effectively stabilizes the photovoltaic performance and electronic properties of perovskite solar cells (PSCs). We herein investigated the passivating effects of phenethylammonium iodide (PEAI) and 4-fluorophenethylammonium iodide (F-PEAI) on perovskite absorber layers comparatively, analyzing their impact on morphological, optical, and electrical properties of perovskite absorbers. Both passivation molecules significantly improved film characteristics, with enlarged domain size, uniform surface morphologies, and prolonged carrier lifetime observed in polycrystalline perovskite absorbers. Notably, non-fluorinated PEAI demonstrated superior effectiveness in reducing tail states and defective Pb2+ sites, resulting in more stable absorbers with fewer defects. The passivated perovskite absorbers exhibited higher champion efficiencies (22.07% for PEAI and 20.56% for F-PEAI) compared to control devices (19.53%), attributed to the suppression of interfacial nonradiative recombination and reduction in recombination energy losses. The devices with passivated perovskite absorbers also demonstrated promising indoor photovoltaic performance, achieving an efficiency of 39.04% and 37.02% under dim-light conditions (LED, 6500 K, 1000 Lux), compared to 29.43% in the control device. Importantly, the passivated absorbers maintained 94% of their initial efficiency even after 1250 hours of humidity aging, indicating enhanced hydrophobicity and reduced trap states, resulting in good ambient stability. Our findings highlight the effectiveness of passivation techniques using organic ammonium halide salts in improving the efficiency and stability of perovskite solar cells. • A comprehensive discussion on the passivating effect of fluorinated PEAI for perovskite films is present. • PEAI is more effective in reducing trap sites than F-PEAI, generating high-quality perovskite absorbers. • The passivated perovskite absorbers by PEAI could achieve device efficiency up to 22.07% (1-sun) and 39.04% (indoor). [ABSTRACT FROM AUTHOR]

In insulated cladding components, simultaneously detecting and distinguishing surface defects on the insulation layer, hidden defects in the insulation layer, and surface defects on the metal layer of insulated cladding components can help in formulating appropriate maintenance plans and promptly eliminating safety hazards, which is of great significance for ensuring the safety of insulated cladding components in relevant application fields. This paper applies capacitive imaging (CI) to detect three different types of defects. A three-dimensional simulation model of the CI sensor detecting the three types of defects is established using COMSOL software. The corresponding measurement sensitivity distribution (MSD) and volume of influence (VOI) are obtained, and the detection mechanisms of the three different types of defects are analyzed in detail. By analyzing the lift-off height variation, the VOI regions' changing patterns for different types of defects are determined, and the distinguishing mechanisms for the three types of defects are obtained. Based on the monotonicity of the normalized variation ratio (NVR) and the differences in the positions of the intersection points, a comparative distinguishing method for the three types of defects based on the variation of the lift-off height is established. Experimental results demonstrate that the defect comparative distinguishing method based on the lift-off effect can effectively differentiate surface defects on the insulation layer, hidden defects in the insulation layer, and surface defects on the metal layer. [Display omitted] • Elaborating on the detection mechanisms of three types of defects in insulation cladding components. • Using MSD and VOI to obtain the discriminatory mechanisms for three types of defects. • Established a comparative discrimination method for the three types of defects based on the lift-off effect. [ABSTRACT FROM AUTHOR]

P-doped graphitic carbon nitride tubes (P-CNTS) with different P concentrations were successfully fabricated via a pre-hydrothermal in combination with a calcination process under a nitrogen atmosphere. The as-prepared samples exhibited excellent photocatalytic performance with a hydrogen production rate (HPR) of 2749.3 μmol g−1 h−1, which was 17.5 and 6.6 times higher than that of the bulk graphitic carbon nitride (CNB) and graphitic carbon nitride tube (CNT). The structural and textural properties of the P-CNT samples were well-investigated via a series of characterization methods. Compared with the bulk g-C3N4, the tubular structure of the doped samples was provided with a larger specific surface area (SSA) and a relatively rough interior. Besides the above, surface defects were formed due to the doping, which could act as more active sites for the hydrogen production reaction. In addition, the introduction of the P element could effectively adjust the band-gap, strengthen the harvest of visible-light, and boost the effective separation of photogenerated charges. More interestingly, these findings can open up a novel prospect for the enhancement of the photocatalytic performance of the modified g-C3N4. [ABSTRACT FROM AUTHOR]

Although the defect detection technology based on machine vision is relatively mature, there is relatively little theoretical research applied to the detection of defects on the surface of bearing outer rings. The image stitching method is applied to stitch the captured images of bearing outer ring surface containing incomplete defect areas into a single image with complete defect information for subsequent defect identification. In the image stitching process, both SURF algorithm and image column projection principle are used to restore the real spatial information of the defect area at the edge of the image. The Hessian matrix (based on SURF algorithm) is established to extract and match feature points, and the weighted average fusion algorithm is applied to achieve seamless stitching of the bearing outer ring surface images containing defect information. After experimental analysis, the results show that the method effectively restores the spatial geometric characteristics of the bearing and retains the edge feature information, which can provide effective guarantee for the accuracy and quality of image stitching. From both theoretical and practical perspectives, there is a need for research and exploration. [ABSTRACT FROM AUTHOR]

Ga2O3‐decorated and ‐defective surface models based on anatase TiO2 have been established. The thermodynamic reaction pathways, including protonation, deoxygenation and hydroxylation steps, during CO2 conversion with H2O to C1 products were calculated. The calculation results demonstrate that a Ga2O3 cocatalyst enhances the selective adsorption of CO2 and slightly weakens the competitive adsorption of H2O. The promotion effect of Ga2O3 on the subsequent reaction depends on the availability of protons and electrons. Free‐energy calculations revealed that the basic functional site generated by Ga2O3 not only suppresses the back reaction of the OH group after H2O directly provides protons but also maintains the surface defect oxygen vacancy (VO), which promotes the reaction thermodynamics but tends to be consumed in the process. Additionally, Ga2O3 decoration promotes VO formation, and the coexistence of Ga2O3 and VO further decreases the reaction rate‐determining step energy barrier, promoting C1 production. [ABSTRACT FROM AUTHOR]

We study the regular surface defect in the Ω -deformed four-dimensional supersymmetric gauge theory with gauge group SU(N) with 2N hypermultiplets in fundamental representation. We prove its vacuum expectation value obeys the Knizhnik–Zamolodchikov equation for the 4-point conformal block of the sl ^ N -current algebra, originally introduced in the context of two-dimensional conformal field theory. The level and the vertex operators are determined by the parameters of the Ω -background and the masses of the hypermultiplets; the cross-ratio of the 4 points is determined by the complexified gauge coupling. We clarify that in a somewhat subtle way the branching rule is parametrized by the Coulomb moduli. This is an example of the BPS/CFT relation. [ABSTRACT FROM AUTHOR]