In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and...In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.展开更多
The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structu...The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.展开更多
The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Sw...The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Swin Transformer as the backbone network.This approach leverages Swin Transformer's multi-head self-attention mechanism for improved feature extraction of defects spanning various scales.Integrated with the YOLOv8 detection head,our model achieves a mean average precision of 85.1%on our dataset,outperforming baseline methods by 1.4%.The model's effectiveness is further demonstrated on a steel-surface defect dataset,indicating its broad applicability in industrial surface defect detection.Our work highlights the potential of combining Swin Transformer and YOLOv8 for accurate and efficient defect detection.展开更多
The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect...The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect detection.In contrast to the subjective and inefficient manual visual inspection,deep learning-based algorithms for concrete defect detection exhibit remarkable advantages,emerging as a focal point in recent research.This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years.It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning.Subsequently,it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects:image classification,object detection,and semantic segmentation.The paper summarizes the strengths and weaknesses of existing methods and the challenges they face.Additionally,it analyzes and prospects the development trends of surface defect detection in concrete bridges.展开更多
Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal t...Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal therapy hydrogel with a pulsed drug delivery mechanism.The system is predicated on a hydrogel matrix that is thermally responsive,characteristic of bone defect sites,facilitating controlled and site-specific drug release.The cornerstone of this system is the incorporation of mild photothermal nanoparticles,which are activated within the temperature range of 40–43°C,thereby enhancing the precision and efficacy of drug delivery.Our findings demonstrate that the photothermal response significantly augments the localized delivery of therapeutic agents,mitigating systemic side effects and bolstering efficacy at the defect site.The synchronized pulsed release,cooperated with mild photothermal therapy,effectively addresses infection control,and promotes bone regeneration.This approach signifies a considerable advancement in the management of infectious bone defects,offering an effective and patient-centric alternative to traditional methods.Our research endeavors to extend its applicability to a wider spectrum of tissue regeneration scenarios,underscoring its transformative potential in the realm of regenerative medicine.展开更多
Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping...Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.展开更多
Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version...Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.展开更多
The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary mate...The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary material.Consequently,this study successfully developed a MnS/MnO heterostructure through a straightforward hydrothermal and annealing method,marking its initial application in aqueous magnesium ion capacitors(AMICs).The fabricated MnS/MnO heterostructure,characterized by S defects,also generates Mn defects via in-situ initiation of early electrochemical processes.This unique dual ion defects MnS/MnO heterostructure(DID-MnS/MnO)enables the transformation of MnS and MnO,initially not highly active electrochemically for Mg^(2+),into cathode materials exhibiting high electrochemical activity and superior performance.Moreover,DID-MnS/MnO enhances conductivity,improves the kinetics of surface redox reactions,and increases the diffusion rate of Mg^(2+).Furthermore,this study introduces a dual energy storage mechanism for DID-MnS/MnO,which,in conjunction with dual ion defects,offers additional active sites for Mg^(2+)insertion/deinsertion in the host material,mitigating volume expansion and structural degradation during repeated charge-discharge cycles,thereby significantly enhancing cycling reversibility.As anticipated,using a three-electrode system,the developed DID-MnS/MnO demonstrated a discharge specific capacity of 237.9 mAh/g at a current density of 0.1 A/g.Remarkably,the constructed AMIC maintained a capacity retention rate of 94.3%after 10000 cycles at a current density of 1.0 A/g,with a specific capacitance of 165.7 F/g.Hence,DID-MnS/MnO offers insightful perspectives for designing alternative clean energy sources and is expected to contribute significantly to the advancement of the clean energy sector.展开更多
Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role ...Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.展开更多
Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,an...Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.展开更多
Selective catalytic reduction of NO_(x) with CO(CO-SCR)is a process that purifies both NO and CO pollutants through a catalytic reaction.Specifically,the cleavage of NO on the catalyst surface is crucial for promoting...Selective catalytic reduction of NO_(x) with CO(CO-SCR)is a process that purifies both NO and CO pollutants through a catalytic reaction.Specifically,the cleavage of NO on the catalyst surface is crucial for promoting the reaction.During the reaction,the presence of oxygen vacancies can extract oxygen from NO,thereby facilitating the cleavage of NO on the catalyst surface.Thus,the formation of oxygen vacancies is key to accelerating the CO-SCR reaction,with different types of oxygen vacancies being more conducive to their generation.In this study,Rh/CeCuO_(x) catalysts were synthesized using the co-crystallization and impregnation methods,and asymmetric oxygen vacancies were induced through hydrogen thermal treatment.This structuralmodification was aimed at regulating the behavior of NO on the catalyst surface.The Rh/Ce0.95Cu0.05O_(x)-H_(2) catalyst exhibited the best performance in CO-SCR,achieving above 90%NO conversion at 162℃.Various characterization techniques showed that the H_(2) treatment effectively reduced some of the CuO and Rh_(2)O_(3),creating asymmetric oxygen vacancies that accelerated the cleavage of NO on the catalyst surface,rather than forming difficult-to-decompose nitrates.This study offers a novel approach to constructing oxygen vacancies in new CO-SCR catalysts.展开更多
Phenanthrene(Phe)is one of the common polycyclic aromatic hydrocarbons in the environment,and recent studies show that it can cause cardiac developmental toxicity and immunotoxicity.However,it is still unknown whether...Phenanthrene(Phe)is one of the common polycyclic aromatic hydrocarbons in the environment,and recent studies show that it can cause cardiac developmental toxicity and immunotoxicity.However,it is still unknown whether it can affect the hematopoietic development in aquatic organisms.To address this question,zebrafish(Danio rerio)were chronically exposed to Phe at different concentrations.We found that Phe caused structural damage to the renal tubules in the kidney,induced malformed erythrocytes in peripheral blood,and decreased the proportion of myeloid cells in adult zebrafish,suggesting possible negative impacts that Phe posed to hematopoietic development.Then,using in situ hybridization technology,we found that Phe decreased the expression of primitive hematopoietic marker genes,specifically gata1 and pu.1,accompanied by an obstruction of primitive erythrocyte circulation.Furthermore,Phe impaired definitive hematopoiesis,increased aberrations of the transient hematopoietic site(PBI),and reduced the generation of hematopoietic stem cells,ultimately influencing the number of erythrocytes and myeloid cells.The findings suggested that Phe could induce hematopoietic toxicity in zebrafish embryos and pose unknown ecological risks.展开更多
As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially ...As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially in the context of an imbalance between osteoblast and osteoclast activities.Therefore,the development of new biomaterials has become the key.This article reviews various design strategies and their advantages and disadvantages for biomaterials aimed at osteoporotic bone defects.Overall,current research progress indicates that innovative design,functionalization,and targeting of materials can significantly enhance bone regeneration under osteoporotic conditions.By comprehensively considering biocompatibility,mechanical properties,and bioactivity,these biomaterials can be further optimized,offering a range of choices and strategies for the repair of osteoporotic bone defects.展开更多
To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,t...To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.展开更多
Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the e...Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the electronic structure of active sites.This optimization influences the adsorption energy of intermediates,thereby mitigating reaction energy barriers,altering paths,enhancing selectivity,and ultimately improving the catalytic efficiency of electrocatalysts.To elucidate the impact of defects on the electrocatalytic process,we comprehensively outline the roles of various point defects,their synthetic methodologies,and characterization techniques.Importantly,we consolidate insights into the relationship between point defects and catalytic activity for hydrogen/oxygen evolution and CO_(2)/O_(2)/N_(2) reduction reactions by integrating mechanisms from diverse reactions.This underscores the pivotal role of point defects in enhancing catalytic performance.At last,the principal challenges and prospects associated with point defects in current electrocatalysts are proposed,emphasizing their role in advancing the efficiency of electrochemical energy storage and conversion materials.展开更多
High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,inclu...High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.展开更多
Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,re...Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.展开更多
Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simul...Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simultaneously control the growth of perovskite crystals and passivate defects.Here,4-(trifluoromethyl)benzoic anhydride(4-TBA)composed of benzene rings functionalized with carbonyl and trifluoromethyl groups was used as an example additive to study the characteristics of additives used for producing high-quality perovskites and controlling their surface properties.The interaction between4-TBA and perovskite precursor materials was investigated using density functional theory(DFT)simulations.The electron-rich carbonyl group efficiently passivated the under-coordinated lead-ion defects.Additionally,hydrogen bonding between trifluoromethyl and organic cations prevents the generation of cation vacancies.Because of its intrinsic hydrophobicity,the trifluoromethyl group simultaneously improves the moisture and heat stability of the film.4-TBA serves as a universal modifier for various perovskite compositions.The power conversion efficiency(PCE)of inverted perovskite solar cells(PSCs)based on methylammonium(MA)with 4-TBA was improved from 16.15%to 19.28%.Similarly,the PCE of inverted PSCs based on a cesium formamidinium MA(CsFAMA)perovskite film increased from20.72%to 23.58%,upon addition of 4-TBA.Furthermore,the moisture and thermal stability of 4-TBAtreated films and devices was significantly enhanced,along with prolonged device performance.Our work provides guidance on selecting the structure and functional groups that are essential for surface defect passivation and the production of high-quality perovskites.展开更多
Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,th...Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.展开更多
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it cha...Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.展开更多
基金funded by National Natural Science Foundation of China(Grant Nos.52130504,52305577,and 52175509)the Key Research and Development Plan of Hubei Province(Grant No.2022BAA013)+4 种基金the Major Program(JD)of Hubei Province(Grant No.2023BAA008-2)the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ047)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(Grant No.GZB20230244)the fellowship from the China Postdoctoral Science Foundation(2024M750995)。
文摘In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.
基金The“13th Five-Year Plan”National Science and Technology Major Project,2016ZX05052,Changchao QiThe China National Petroleum Corporation Science and Technology Project,2021DJ6505,Changchao Qi.
文摘The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.
基金supported by the Scientific and technological key project in Henan Province 22210224002the Natural Science Foundation of Henan Polytechnic University B2021-38.
文摘The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Swin Transformer as the backbone network.This approach leverages Swin Transformer's multi-head self-attention mechanism for improved feature extraction of defects spanning various scales.Integrated with the YOLOv8 detection head,our model achieves a mean average precision of 85.1%on our dataset,outperforming baseline methods by 1.4%.The model's effectiveness is further demonstrated on a steel-surface defect dataset,indicating its broad applicability in industrial surface defect detection.Our work highlights the potential of combining Swin Transformer and YOLOv8 for accurate and efficient defect detection.
基金supported by the Key Research and Development Program of Shaanxi Province-International Science and Technology Cooperation Program Project (No.2020KW-001)the Contract for Xi'an Municipal Science and Technology Plan Project-Xi'an City Strong Foundation Innovation Plan (No.21XJZZ0074)the Key Project of Graduate Student Innovation Fund at Xi'an University of Posts and Telecommunications (No.CXJJZL2023013)。
文摘The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect detection.In contrast to the subjective and inefficient manual visual inspection,deep learning-based algorithms for concrete defect detection exhibit remarkable advantages,emerging as a focal point in recent research.This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years.It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning.Subsequently,it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects:image classification,object detection,and semantic segmentation.The paper summarizes the strengths and weaknesses of existing methods and the challenges they face.Additionally,it analyzes and prospects the development trends of surface defect detection in concrete bridges.
基金supported by the National Natural Science Foundation of China(32171354,82222015,82171001)The National Key Research and Development Program of China2023YFC2413600Research Funding from West China School/Hospital of Stomatology,Sichuan University(No.RCDWIS2023-1).
文摘Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal therapy hydrogel with a pulsed drug delivery mechanism.The system is predicated on a hydrogel matrix that is thermally responsive,characteristic of bone defect sites,facilitating controlled and site-specific drug release.The cornerstone of this system is the incorporation of mild photothermal nanoparticles,which are activated within the temperature range of 40–43°C,thereby enhancing the precision and efficacy of drug delivery.Our findings demonstrate that the photothermal response significantly augments the localized delivery of therapeutic agents,mitigating systemic side effects and bolstering efficacy at the defect site.The synchronized pulsed release,cooperated with mild photothermal therapy,effectively addresses infection control,and promotes bone regeneration.This approach signifies a considerable advancement in the management of infectious bone defects,offering an effective and patient-centric alternative to traditional methods.Our research endeavors to extend its applicability to a wider spectrum of tissue regeneration scenarios,underscoring its transformative potential in the realm of regenerative medicine.
基金supported by the National Natural Science Foundation of China(22262010,22062005,22165005,U20A20128)Guangxi Science and Technology Fund for Distinguished HighTalent Introduction Program(AC22035091)Guangxi Science Fund for Distinguished Young Scholars(2019GXNSFFA245016)。
文摘Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.
基金supported by the National Natural Science Foundation of China(No.62103298)the Natural Science Foundation of Hebei Province(No.F2018209289)。
文摘Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.
基金supported by the National Natural Science Foundation of China(Nos.52071171,52202248)Liaoning BaiQianWan Talents Program(LNBQW2018B0048)+8 种基金Shenyang Science and Technology Project(21-108-9-04)Key Research Project of Department of Education of Liaoning Province(LJKZZ20220015)the Research Fund for the Doctoral Program of Liaoning Province(2022-BS-114)Chunhui Program of the Ministry of Education of the People’s Republic of China(202201135)Australian Research Council(ARC)through Future Fellowship(FT210100298,FT210100806)Discovery Project(DP220100603)Linkage Project(LP210100467,LP210200504,LP210200345,LP220100088)Industrial Transformation Training Centre(IC180100005)schemes,and the Australian Government through the Cooperative Research Centres Projects(CRCPXIII000077)the Australian Renewable Energy Agency(ARENA)as part of ARENA’s Transformative Research Accelerating Commercialisation Program(TM021).
文摘The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary material.Consequently,this study successfully developed a MnS/MnO heterostructure through a straightforward hydrothermal and annealing method,marking its initial application in aqueous magnesium ion capacitors(AMICs).The fabricated MnS/MnO heterostructure,characterized by S defects,also generates Mn defects via in-situ initiation of early electrochemical processes.This unique dual ion defects MnS/MnO heterostructure(DID-MnS/MnO)enables the transformation of MnS and MnO,initially not highly active electrochemically for Mg^(2+),into cathode materials exhibiting high electrochemical activity and superior performance.Moreover,DID-MnS/MnO enhances conductivity,improves the kinetics of surface redox reactions,and increases the diffusion rate of Mg^(2+).Furthermore,this study introduces a dual energy storage mechanism for DID-MnS/MnO,which,in conjunction with dual ion defects,offers additional active sites for Mg^(2+)insertion/deinsertion in the host material,mitigating volume expansion and structural degradation during repeated charge-discharge cycles,thereby significantly enhancing cycling reversibility.As anticipated,using a three-electrode system,the developed DID-MnS/MnO demonstrated a discharge specific capacity of 237.9 mAh/g at a current density of 0.1 A/g.Remarkably,the constructed AMIC maintained a capacity retention rate of 94.3%after 10000 cycles at a current density of 1.0 A/g,with a specific capacitance of 165.7 F/g.Hence,DID-MnS/MnO offers insightful perspectives for designing alternative clean energy sources and is expected to contribute significantly to the advancement of the clean energy sector.
基金support from the National Institute of Health(K99AR081897,R00AR081897)M.N.W.acknowledges funding support from the National Institute of Health(P01DK011794,R01DK116716)+1 种基金the Smith Family Foundation Odyssey Award,and the Chen Institute Massachusetts General Hospital Research Scholar(2024-2029)awardμCT and bone histomorphometry were performed by the Center for Skeletal Research at Massachusetts General Hospital,a NIH-funded program(P30AR066261 and AR075042)led by Mary Bouxsein and Marie Demay).
文摘Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.
文摘Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.
基金supported by the support of the National Natural Science Foundation of China(Nos.22072141,22176185 and 52304429)the National Key Research and Development Program of China(Nos.2022YFB3504200,2021YFB3501900)+4 种基金the Natural Science Foundation of Jiangxi Province for Distinguished Young Scholars(No.20232ACB213004)Jiangxi Provincial Key Research and Development Program(No.20232BBG70012)Jiangxi Provincial Natural Science Foundation(No.20212BAB213032)the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2018263)the Research Projects of Ganjiang Innovation Academy,Chinese Academy of Sciences(No.E355C001).
文摘Selective catalytic reduction of NO_(x) with CO(CO-SCR)is a process that purifies both NO and CO pollutants through a catalytic reaction.Specifically,the cleavage of NO on the catalyst surface is crucial for promoting the reaction.During the reaction,the presence of oxygen vacancies can extract oxygen from NO,thereby facilitating the cleavage of NO on the catalyst surface.Thus,the formation of oxygen vacancies is key to accelerating the CO-SCR reaction,with different types of oxygen vacancies being more conducive to their generation.In this study,Rh/CeCuO_(x) catalysts were synthesized using the co-crystallization and impregnation methods,and asymmetric oxygen vacancies were induced through hydrogen thermal treatment.This structuralmodification was aimed at regulating the behavior of NO on the catalyst surface.The Rh/Ce0.95Cu0.05O_(x)-H_(2) catalyst exhibited the best performance in CO-SCR,achieving above 90%NO conversion at 162℃.Various characterization techniques showed that the H_(2) treatment effectively reduced some of the CuO and Rh_(2)O_(3),creating asymmetric oxygen vacancies that accelerated the cleavage of NO on the catalyst surface,rather than forming difficult-to-decompose nitrates.This study offers a novel approach to constructing oxygen vacancies in new CO-SCR catalysts.
基金supported by the National Natural Science Foundation of China(Nos.22276117 and 22076108)the Science and Technology Innovation Talent Team Project of Shanxi Province(No.202204051002024).
文摘Phenanthrene(Phe)is one of the common polycyclic aromatic hydrocarbons in the environment,and recent studies show that it can cause cardiac developmental toxicity and immunotoxicity.However,it is still unknown whether it can affect the hematopoietic development in aquatic organisms.To address this question,zebrafish(Danio rerio)were chronically exposed to Phe at different concentrations.We found that Phe caused structural damage to the renal tubules in the kidney,induced malformed erythrocytes in peripheral blood,and decreased the proportion of myeloid cells in adult zebrafish,suggesting possible negative impacts that Phe posed to hematopoietic development.Then,using in situ hybridization technology,we found that Phe decreased the expression of primitive hematopoietic marker genes,specifically gata1 and pu.1,accompanied by an obstruction of primitive erythrocyte circulation.Furthermore,Phe impaired definitive hematopoiesis,increased aberrations of the transient hematopoietic site(PBI),and reduced the generation of hematopoietic stem cells,ultimately influencing the number of erythrocytes and myeloid cells.The findings suggested that Phe could induce hematopoietic toxicity in zebrafish embryos and pose unknown ecological risks.
基金supported by the National Natural Science Foundation of China(Nos.82160419 and 82302772)Guizhou Basic Research Project(No.ZK[2023]General 201)。
文摘As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially in the context of an imbalance between osteoblast and osteoclast activities.Therefore,the development of new biomaterials has become the key.This article reviews various design strategies and their advantages and disadvantages for biomaterials aimed at osteoporotic bone defects.Overall,current research progress indicates that innovative design,functionalization,and targeting of materials can significantly enhance bone regeneration under osteoporotic conditions.By comprehensively considering biocompatibility,mechanical properties,and bioactivity,these biomaterials can be further optimized,offering a range of choices and strategies for the repair of osteoporotic bone defects.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_4084).
文摘To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.
基金supported by the National Natural Science Foundation of China(U21A20281)the Special Fund for Young Teachers from Zhengzhou University(JC23557030,JC23257011)+1 种基金the Key Research Projects of Higher Education Institutions of Henan Province(24A530009)the Project of Zhongyuan Critical Metals Laboratory(GJJSGFYQ202336).
文摘Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the electronic structure of active sites.This optimization influences the adsorption energy of intermediates,thereby mitigating reaction energy barriers,altering paths,enhancing selectivity,and ultimately improving the catalytic efficiency of electrocatalysts.To elucidate the impact of defects on the electrocatalytic process,we comprehensively outline the roles of various point defects,their synthetic methodologies,and characterization techniques.Importantly,we consolidate insights into the relationship between point defects and catalytic activity for hydrogen/oxygen evolution and CO_(2)/O_(2)/N_(2) reduction reactions by integrating mechanisms from diverse reactions.This underscores the pivotal role of point defects in enhancing catalytic performance.At last,the principal challenges and prospects associated with point defects in current electrocatalysts are proposed,emphasizing their role in advancing the efficiency of electrochemical energy storage and conversion materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272103 and 52072010)Beijing Natural Science Foundation(Grant Nos.2242029 and JL23004).
文摘High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2022R1I1A3063493).
文摘Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.
基金supported by a Research Grant of Pukyong National University(2023)。
文摘Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simultaneously control the growth of perovskite crystals and passivate defects.Here,4-(trifluoromethyl)benzoic anhydride(4-TBA)composed of benzene rings functionalized with carbonyl and trifluoromethyl groups was used as an example additive to study the characteristics of additives used for producing high-quality perovskites and controlling their surface properties.The interaction between4-TBA and perovskite precursor materials was investigated using density functional theory(DFT)simulations.The electron-rich carbonyl group efficiently passivated the under-coordinated lead-ion defects.Additionally,hydrogen bonding between trifluoromethyl and organic cations prevents the generation of cation vacancies.Because of its intrinsic hydrophobicity,the trifluoromethyl group simultaneously improves the moisture and heat stability of the film.4-TBA serves as a universal modifier for various perovskite compositions.The power conversion efficiency(PCE)of inverted perovskite solar cells(PSCs)based on methylammonium(MA)with 4-TBA was improved from 16.15%to 19.28%.Similarly,the PCE of inverted PSCs based on a cesium formamidinium MA(CsFAMA)perovskite film increased from20.72%to 23.58%,upon addition of 4-TBA.Furthermore,the moisture and thermal stability of 4-TBAtreated films and devices was significantly enhanced,along with prolonged device performance.Our work provides guidance on selecting the structure and functional groups that are essential for surface defect passivation and the production of high-quality perovskites.
基金support of the National Natural Science Foundation of China(Grant No.22225801,22178217 and 22308216)supported by the Fundamental Research Funds for the Central Universities,conducted at Tongji University.
文摘Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.
基金supported in part by the National Natural Science Foundation of China under Grants 62463002,62062021 and 62473033in part by the Guiyang Scientific Plan Project[2023]48–11,in part by QKHZYD[2023]010 Guizhou Province Science and Technology Innovation Base Construction Project“Key Laboratory Construction of Intelligent Mountain Agricultural Equipment”.
文摘Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
