Deep Defects Research Articles

Halide Double Perovskite Nanocrystals Doped with Rare-Earth Ions for Multifunctional Applications.

Most lead-free halide double perovskite materials display low photoluminescence quantum yield (PLQY) due to the indirect bandgap or forbidden transition. Doping is an effective strategy to tailor the optical properties of materials. Herein, efficient blue-emitting Sb3+ -doped Cs2 NaInCl6 nanocrystals (NCs) are selected as host, rare-earth (RE) ions (Sm3+ , Eu3+ , Tb3+ , and Dy3+ ) are incorporated into the host, and excellent PLQY of 80.1% is obtained. Femtosecond transient absorption measurement found that RE ions not only served as the activator ions but also filled the deep vacancy defects. Anti-counterfeiting, optical thermometry, and white-light-emitting diodes (WLEDs) are exhibited using these RE ions-doped halide double perovskite NCs. For the optical thermometry based on Sm3+ -doped Cs2 NaInCl6 :Sb3+ NCs, the maximum relative sensitivity is 0.753% K-1 , which is higher than those of most temperature-sensing materials. Moreover, the WLED fabricated by Sm3+ -doped Cs2 NaInCl6 :Sb3+ NCs@PMMA displays CIE color coordinates of (0.30, 0.28), a luminous efficiency of 37.5lm W-1 , a CCT of 8035 K, and a CRI over 80, which indicate that Sm3+ -doped Cs2 NaInCl6 :Sb3+ NCs are promising single-component white-light-emitting phosphors for next-generation lighting and display technologies.

Defects studies of BiI3-Polymer composites with carbon fillers to achieve better charge transportation for direct X-ray detectors

Wide bandgap heavy semiconductor materials capable of detecting X-rays at room-temperature without cryogenic cooling have great advantages that include portability and wide-area deployment with major applications include medical imaging, spectroscopy, and astrophysics. BiI3 being a wide band gap material, is explored as a prospective material for X-ray and gamma ray detection. However, thick samples of Bismuth tri-iodide (BiI3) are more prone to defects, which trap the photo-generated charge carriers thereby limiting the charge collection efficiency. Here, we report the defects and charge transportation properties of BiI3-polymer (Polystyrene and Polymethyl-methacrylate) composite pellets of thickness 1.5 mm, upon irradiation with X-rays at tube voltage 60 kV. The defects in the samples have been studied using photoluminescence and the lifetime calculation has been done using Time Resolved Photoluminescence Spectroscopy. In order enhance the transport properties, defect engineering in the samples have been done by embedding carbon based conductive fillers into BiI3-polymer composites using hot compression method leading to decrease in the deep defect density. The mobility-lifetime product (μτ) of the charge carriers in composite samples has been calculated to be 6.53 × 10−1 cm2/V, using modified Hetch equation which is enhanced from 10−4 cm2/V for pristine BiI3 and from 10−2 cm2/V for CdZnTe and perovskites-based detectors. Also, the charge collection has been achieved up to 90% at a bias of 0.8 V for BiI3-polystyrene/super carbon composite samples. The results indicate that defects have been mitigated in the stable BiI3-polymer/filler composite pellets, making them a promising material for room temperature X-ray detection and imaging applications.

Optically Active Telecom Defects in MoTe2 Fewlayers at Room Temperature.

The optical and electrical properties of semiconductors are strongly affected by defect states. The defects in molybdenum ditelluride (MoTe2) show the potential for quantum light emission at optical fiber communication bands. However, the observation of defect-related light emission is still limited to cryogenic temperatures. In this work, we demonstrate the deep defect states in MoTe2 fewlayers produced via a standard van der Waal material transfer method with a heating process, which enables light emission in the telecommunication O-band. The optical measurements show evidence of localized excitons and strong interaction among defects. Furthermore, the optical emission of defects depends on the thickness of the host materials. Our findings offer a new route for tailoring the optical properties of two-dimensional materials in optoelectronic applications.

Open-circuit photopotential characterization of photoelectrochemical activities of Au-modified TiO2 nanorods

The open circuit potential (OCP) of a semiconductor electrode can be used to quantify the transient photopotential (Ep), which represents wavelength-dependent charge accumulation and relaxation kinetics of a photoelectrode. Here OCP responses of a plasmonic Au@TiO2 nanorods (NRs) photoelectrode can be quantified without causing electrochemical corrosion of Au. The photogenerated charge accumulation kinetics data based on the wavelength-dependent growth rates of |Ep| can resolve the plasmonic effects on photoelectrochemistry (PEC) of Au@TiO2 NRs. Data fitting with Kohlrausch-Williams-Watts (KWW) stretched exponential kinetics model illustrates the complex charge relaxations at the Au/TiO2 Schottky contact, from which long relaxation lifetimes with broad lifetime distributions can be obtained. This is attributed to the abundant deep defects in the nanostructure TiO2, which has been strongly confirmed by reducing the oxygen vacancies using a post-thermal annealing treatment. Single-particle dark-field scattering (DFS) spectrum is measured with a tunable wavelength light source to support visible light activities of PEC characteristics of Au@TiO2 NRs. Light scattering spectra of >200 single Au@TiO2 NRs particles are collected to compare directly with PEC responses of OCP of the ensemble Au@TiO2 NRs.

A Deep Neural Network-Based Intelligent Detection Model for Manufacturing Defects of Automobile Parts

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

UV-resistant salicylic acid as interface modifier for efficient and stable perovskite solar cells

Interfacial defect is one of the main hurdles to impede the improvement of efficiency and stability of perovskite solar cells (PSCs). Additionally, the ultraviolet (UV) irradiate induces the generation of deep defects, and further accelerate the decomposition of perovskite films. Thus, the interfacial modification is crucial to improve the efficiency and stability of PSCs. Here, the salicylic acid (SA) as a multifunctional interface material is employed to modify the interface of mesoporous cerium oxide (m-CeOx) and perovskite. The introduced SA molecules can interact with Ce in m-CeOx and Pb in perovskite through carboxylic acid functional groups to passivate interfacial defects and promote interfacial electron extraction and transport. The PSC with SA modification exhibits an improved power conversion efficiency (PCE) of 23.33%. More importantly, the SA can absorb UV light and reduce the damage of UV light to perovskite film, then improving the UV stability and overall stability of devices. This work provides a novel insight to design the interfacial modification materials for preparing efficient and stable PSCs.

The free chimeric medial sural artery perforator flap for individualised and three-dimensional reconstruction of complex soft-tissue defects in extremities.

Trauma or lesion resection often causes complex wounds with deep soft tissue defects in extremities. Simply covering with a skin flap will leave a deep dead space resulting in infection, non-healing wounds, and poor long-term outcomes. Thus, effectively reconstructing complex wounds with dead space leaves a clinical challenge. This manuscript presents our experience using chimeric medial sural artery perforator (cMSAP) flap, to reconstruct complex soft tissue defects of the extremities, thereby exploring broader analysis and indications for future reference. Between March 2016 and May 11, 2022, patients (8 males and 3 females) with a mean age of 41 years (range from 26 to 55 years) underwent reconstructive surgery with the cMSAP flap. The cMSAP flap consists of an MSAP skin paddle and a medial sural muscle paddle. The size of the MSAP skin paddle ranged between 9 × 5cm and 20 × 6cm, and the size of the medial sural muscle paddle ranged between 2 × 2cm and 14 × 4cm. Primary closure of the donor site was achieved in all cases. Of the 11 patients, the cMSAP flap survived in 10 cases. The vascular compromise occurred in one special case and was treated with surgical procedures. The mean follow-up duration was 16.5months (range of 5-25 months). Most patients present satisfactory cosmetic and functional results. The free cMSAP flap is a good option for reconstructing complex soft tissue defects with deep dead space in extremities. The skin flap can cover the skin defect, and the muscle flap can fill the dead space against infection. In addition, three types of cMSAP flaps can be used in a broader range of complex wounds. This procedure can achieve an individualised and three-dimensional reconstruction of the defects and minimise the donor site morbidities.

Diffusion-compensated correlation analysis of frequency-modulated thermal signal for quantitative infrared thermography

Application of a broadband modulated excitation signal and its cross-correlation with the corresponding thermal response, so-called thermal wave radar or pulse compression thermography, is a well-known technique in active infrared thermography. The technique benefits from both the broadband spectrum and the pulse compression efficiency of excitation signal, providing lag and phase quantities which can be used for characterization of defects. However, the significant distortion of the thermal response due to heat diffusion makes the cross-correlation analysis inefficient, particularly for the detection of deep defects or the inspection of a material with low thermal diffusivity. To tackle this issue, diffusion-compensated correlation analysis (DCCA) of thermal signal is proposed for quantitative infrared thermography, using a frequency-modulated sweep signal as excitation waveform. DCCA is based on the correlation of the measured thermal response with a library of template thermal responses calculated using the 1D analytical solution of the heat diffusion problem. DCCA can then more reliably resolve the thermal response in the presence of measurement noise, and depending on the definition of the library, directly map it to the corresponding depth or diffusivity. Two inspection modes are studied: (i) transmission of heat from defects which act as sub-surface heat sources (e.g. vibrothermography), and (ii) reflection of an externally applied heat flux from sub-surface defects (e.g. optical infrared thermography in reflection mode). Outperformance of DCCA to thermal wave radar is analytically substantiated and verified by finite element simulation on a carbon fibre reinforced polymer plate. Furthermore, the technique is experimentally studied for thermographic inspection of a carbon fiber reinforced polymer (CFRP) coupon including artificial defects, and the advantages as well as limitations of the technique are discussed.

Developing a Deep Learning-Based Defect Detection System for Ski Goggles Lenses

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.

Keystone perforator island flaps in the reconstruction of lower limb defects resulting from shrapnel and mine-explosive combat injuries. Case series

In the conditions of warfare in Ukraine, the question of providing medical services to injured civilian and military is especially relevant and severe. In plastic surgeons` professional activities, the task is to restore extensive and deep wound defects in a short term and with a high degree of damaged organ`s restoration, especially supporting function. In this article the authors describe their experience with local keystone perforator island flaps, which are used to reconstruct skin and soft tissue defects of the lower limbs caused by combat injuries.
 Patients and methods. The authors conducted a retrospective review of 49 keystone perforator flaps for 28 patients (26 men and 2 women) who received treatment in the clinic for bullet, shrapnel, and mine‑explosive injuries between 2014 and 2022.
 Results. In all cases, extensive wound defects were completely closed during a single‑stage surgical procedure, and the patients were discharged after recovery. Non‑critical complications required secondary sutures in two cases (4%), extending the duration of treatment by 6 days. The time spent in the operating room on the transposition of one flap ranged from 40 to 95 min (mean: 67 min).
 Conclusions. The findings of the study show that local keystone perforator island flaps are highly effective in the successful reconstruction of lower limb defects caused by combat wounds. The keystone perforator island flap technique requires basic preoperative preparation of the patient, is easy‑to‑use, and exhibits a fairly high level of reliability at the same time. In most cases, keystone perforator island flaps provide primary and single‑stage closure of a large defect in the thigh, in the area of the knee joint, and in the lower leg in the absence of secondary defects that are common at donor sites when alternative techniques are chosen.

Insights into the Efficiency Improvement for CZTSSe Solar Cells with over 12% Efficiency via Ga Incorporation

AbstractSuppressing the band tailing and nonradiative recombination caused by massive defects and defect clusters is crucial for mitigating open‐circuit voltage (Voc) deficit and improving the device performance of CZTSSe thin film solar cells. Cation substitution is one of the most commonly used strategies to address the above issues. The latest world record efficiency of 13.0% is obtained through this strategy (Ag substitution for Cu). Nevertheless, the importance of the approach to implementing metallic ion doping is easily overlooked by researchers. Here, different approaches are adopted to realize Ga doping and the differences in the efficacy and mechanism are thoroughly investigated. It is found that the secondary phase easily emerged when the physical‐based method is employed, and thus challenging to regulate the doping effect. In the case of the chemical‐based method, Ga doping can enlarge the depletion region widthand lower the defect activation energyand Urbach energy. Furthermore, GaSn defects located at grain boundaries can expand the energy band bending between GBs and grain interiors (GIs), thereby suppressing the deep defect states and nonradiative recombination. Consequently, power conversion efficiency as high as 12.12% with a Voc of 522 mV is achieved at 2% Ga doping.

Self‐Healing Perovskite Grain Boundaries in Efficient and Stable Solar Cells via Incorporation of 502 Adhesive

Additive engineering has become an effective method to passivate the grain boundary defects of perovskite films for enhanced photovoltaic performance. Herein, inspired by daily life, a 502 adhesive is directly introduced into perovskite films to improve the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs), where the perovskite grain boundaries are self‐healed through spontaneous polymerization of the 502 adhesive by tracing water molecules in the air at room temperature. The C=O and –CN groups in 502 adhesive molecules provide double bonding points to chemically anchor with perovskite gain boundaries, which effectively block the invaded channels of moisture and oxygen into the perovskite film. Meanwhile, the –CN groups can combine with the deficient coordination of Pb2+ to passivate the deep defects. The optimized PSCs via incorporation of 502 adhesive into perovskite film achieve PCE of 20.12% and remarkable long‐term and moisture stability compared to the control device with PCE of 17.30%.

Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning.

To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer to the last layer of the backbone network to improve the feature extraction capability. Second, the neck network introduces the Bi-directional Feature Pyramid Network (BiFPN) module to replace the original Path-Aggregation Network (PAN) structure and enhance the multi-scale feature fusion. Finally, we use transfer learning to pre-train the model and improve the generalization ability of the model. The experimental results show that the method achieves an average accuracy of 93.6% mAP and a detection speed of 16.7 FPS for defect detection on the dataset, which can identify metal shaft surface defects quickly and accurately, and is of reference significance for practical industrial applications.

Deep CNN-based visual defect detection: Survey of current literature

In the past years, the computer vision domain has been profoundly changed by the advent of deep learning algorithms and data science. The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is extensively employed using automated industrial quality control systems. Defect inspection methods can be mainly grouped into manual inspection, traditional computer vision, and modern computer vision inspection. Initially developed two decades ago, the CNN algorithms recently became popular for solving complex machine vision problems, as big datasets and computationally potent hardware became widely available. Deep learning-based methods form the foundation for modern automatic optical inspection methods and can be grouped based on their network connections into two categories: dense networks and sparse networks. Another method for grouping considers the type of learning: supervised learning used primarily for defect classification and segmentation, and unsupervised learning models, which have the potential to overcome the challenges of supervised models such as labeling images and annotating pixels. In addition, pixel-level based segmentation techniques are considered to cover the state-of-the-art methodologies for the automatic optical inspection. Still, both supervised and unsupervised models pose challenges in regards to model training and attaining the expected detection accuracy. Identified open challenges include algorithmic, application, and data processing challenges. By addressing these challenges, in the future, the demand for automated optical inspection is expected to only grow in both industry practice and academic research.

Hybrid mutation moth flame optimization with deep learning-based smart fabric defect detection

The occurrence of faults in textile manufacturing methods results in major wastage of the properties. Additionally, it also affects the quality of the fabric products. Manual inspection methods fail to detect the defects with respect to efficiency, accuracy, and consistency due to size of the detects, carelessness, and optical illusion. Therefore, automatic fabric inspection has become a powerful tool to optimize the quality of fabric. In this background, the current study develops a novel Hybrid Mutation Moth Flame Optimization with Deep Learning-Based Smart Fabric Defect Detection (HMFODL-FDD) technique for sustainable manufacturing. The proposed HMFODL-FDD technique exploits the Computer Vision (CV) and Deep Learning (DL) techniques for the detection of defects in fabric. To accomplish this objective, the presented HMFODL-FDD technique employs contrast enhancement process to boost the quality of the images. For feature extraction, the HMFODL-FDD technique uses Inception v3 model with HMFO algorithm-based hyperparameter optimizer. Finally, the classification of the fabric defects is performed using Back Propagation Neural Network (BPNN) model. The experimental outcomes confirmed that the proposed HMFODL-FDD technique achieved superior performance over DL techniques in terms of effective defect classification in fabric images with a maximum accuracy of 95.47%.

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.

Optical characterization of high-quality ZnO (0002) / Cu (111) epilayers grown by electrodeposition

Wurtzite ZnO thin films were grown on Cu substrates of three orientations – (111), (100), and (130), respectively – by a simple electrodeposition technique. Among them, the films deposited on the (111) and (130) substrates were epilayers with different crystallinity. Optical characterizations were therefore conducted for the (0002)ZnO/(111)Cu epilayers showing promising crystallinity while having different morphologies. Photoluminescence (PL) spectra of epilayers show a sharp UV near band edge (NBE) emission band, owing to excitonic transitions in ZnO layer, whereas a wide feature in visible spectral region is related to deep defect (O-vacancy) states in ZnO. The strong excitonic ZnO band (NBE) suppresses the defect (DEF) band intensity by ∼100 times, indicating good optical quality of ZnO epilayers. Using the Arrhenius-type equation, activation energies were determined as Ea = 23 meV and 27 meV, which appeared to be related with the Stokes shift between emission and absorption spectra. From the excitation-dependent (80–700 kW/cm2) PL measurements at 3 K, a linear behavior of ratio NBE:DEF was deduced, which indicated that excitonic- and defect-related radiative recombination paths are independent. Furthermore, from the dependence of the PL intensity on the laser power density, characterized with exponential factor of k ≃ 1.4, the donor-bound nature of the excitonic transitions (DoX) in the ZnO/Cu epilayers is suggested. Corroborative structural and optical findings with the bandgap values estimated (3.362 eV and 3.371 eV) in a very good agreement with the literature data for ZnO single crystal, confirm high-quality of the studied ZnO/Cu (111) epilayers.

A Deep Learning-Based Surface Defects Detection and Color Classification Method for Solar Cells

In recent years, solar photovoltaic-based power generation technology has become the key planning direction of many countries around the world. In the process of making solar cells, the quality inspection requirements are very particular, such as physical damages, surface scratches, broken grids and microcracks. In traditional factory production, the detection of the above defects requires professional inspectors to carry out visual inspection, which often leads to low detection efficiency, subjective assumption and fatigue, as well as some detection errors. In recent years, the rapid development of computer vision makes it possible to be used to detect the defects in solar cells. To overcome existing barriers, this paper proposes a method for detecting surface defects in solar cells based on deep neural network. Specifically, a specified image segmentation model named U-Net is developed for this purpose. By automatically segmenting little objects using the proposed recognition approach, surface defects detection can be realized. At last, we use a set of experiments on images from real scenes to verify the proposed method.

Role of Lipid Packing Defects in Determining Membrane Interactions of Antimicrobial Polymers.

Understanding the emergence and role of lipid packing defects in the detection and subsequent partitioning of antimicrobial agents into bacterial membranes is essential for gaining insights into general antimicrobial mechanisms. Herein, using methacrylate polymers as a model platform, we investigate the effects of inclusion of various functional groups in the biomimetic antimicrobial polymer design on the aspects of lipid packing defects in model bacterial membranes. Two antimicrobial polymers are considered: ternary polymers composed of cationic, hydrophobic, and polar moieties and binary polymers with only cationic and hydrophobic moieties. We find that differing modes of insertion of these two polymers lead to different packing defects in the bacterial membrane. While insertion of both binary and ternary polymers leads to an enhanced number of deep defects in the upper leaflet, shallow defects are moderately enhanced upon interaction with ternary polymers only. We provide conclusive evidence that insertion of antimicrobial polymers in bacterial membrane is preceded by sensing of interfacial lipid packing defects. Our simulation results show that the hydrophobic groups are inserted at a single colocalized deep defect site for both binary and ternary polymers. However, the presence of polar groups in the ternary polymers use the shallow defects close to the lipid-water interface, in addition, to insert into the membrane, which leads to a more folded conformation of the ternary polymer in the membrane environment, and hence a different membrane partitioning mechanism compared to the binary polymer, which acquires an amphiphilic conformation.

Superior Antihelix Composite Graft for Repair of Nasal Ala and Lateral Nasal Tip Defects: A Retrospective Case Series.

Deep defects on the nasal ala and lateral nasal tip may result in nasal valve insufficiency or alar notching and are often repaired with a 2-stage reconstruction. Previous literature has demonstrated high failure rates of composite grafts. Identify survival rates and cosmetic outcomes of nasal composite grafts harvested from the antihelix. A retrospective review of 52 patients who underwent ala or lateral nasal tip composite graft repair from April 2019 through May 2022, with statistical analysis of cosmetic outcomes graded by 2 surgeons. Defect size ranged from 0.7 cm × 0.8 cm to 1.9 cm × 2.5 cm. 48 grafts survived (92.3% survival rate). Four patients sustained at least partial integument sloughing (epidermal necrosis), but the cartilage survived in all 52 cases. Overall, aesthetic results yielded the following: excellent (19.5%), very good (35.5%), good (11.5%), decent (16.5%), and poor (6%). In 93% of cases, there was no evidence of nasal collapse or retraction. Two patients (3.8%) required surgical revision. Donor site morbidity was low. The antihelical composite skin graft is a 1-step reliable repair option for ala and lateral nasal tip defects with an acceptable cosmetic outcome.