Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult t...Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
The interdependence of electrical parameters has long inhibited the progress of bismuth telluride(Bi_(2)Te3),limiting its widespread application in thermoelectric cooling and power generation.This work investigates th...The interdependence of electrical parameters has long inhibited the progress of bismuth telluride(Bi_(2)Te3),limiting its widespread application in thermoelectric cooling and power generation.This work investigates the n-type Bi_(2)Te_(2.79)Se_(0.21)I_(0.004)(Bi_(2)(Te,Se)_(3),BTS)system with light Zn doping,revealing that Zn addition simultaneously enhances the Seebeck coefficient(S)and electrical conductivity(σ)through the modulation of defect composition and multi-level band regulation.The substitution of Zn atoms at Bi sites enhances S via bandgap(E_(g))widening,band flattening,and band splitting effects,contributing to a competitive power factor(PF)of∼60μW⋅cm^(−1)⋅K^(−2).Additionally,thermal conductivity is maintained at a low level,leading to an extraordinary figure-of-merit(ZT)value of∼1.3 at room temperature.Furthermore,the Bi_(2)Zn_(0.01)Te_(2.79)Se_(0.21)I_(0.004) system demonstrates impressive thermoelectric device performance,with a maximum cooling temperature difference(ΔT_(max))of∼70.0 K at 300 K,rising to∼78.0 K at 323 K and∼85.7 K at 343 K,as well as a maximum conversion efficiency(η_(max))of∼6.2%under aΔT of 200 K.This study clarifies the mechanism of Zn doping and presents a cost-effective strategy for enhancing the performance of n-type BTS thermoelectrics and their devices.展开更多
Algal blooms in eutrophic water often produce microcystins,which can lead to multi-organ dysfunction and even mortality in many organisms.Therefore,the elimination of microcystins in water is an urgent issue that need...Algal blooms in eutrophic water often produce microcystins,which can lead to multi-organ dysfunction and even mortality in many organisms.Therefore,the elimination of microcystins in water is an urgent issue that needs to be addressed.Herein,we develop a dual defect engineering strategy to construct graphite-like carbon nitride with N vacancies and-C≡N groups(NgCN)nanosheets for microcystin-LR(MC-LR)photodegradation.According to our theoretical calculations and actual findings,the NgCN nanosheets enhanced recyclability for the removal of MC-LR while also demonstrating outstanding photocatalytic efficacy,significantly surpassing the graphite-like carbon nitride under visible light,which is ascribed to efficient charge separation as well as narrowing the bandgap.Impressively,the water quality after photodegradation has been proven to be safe based on International Organization for Standardization(ISO)standards.This finding provides meaningful insights into understanding the relationship between defect engineering and photodegradation performance to design photocatalytic materials with higher activity for environmental remediation.展开更多
Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supp...Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supported palladium catalyst(Pd-TiO_(2)-Ov)for efficient photocatalytic water-donating transfer hydrogenation of anethole towards 4-n-propylanisole in a high yield of 99.9%,which is significantly higher compared to the pristine TiO_(2)-supported palladium catalyst(Pd-TiO_(2),74%).The enhanced performance is ascribed to the presence of oxygen vacancies,which facilitate light absorption and suppress the recombination of photogenerated electron-hole pairs.Furthermore,the Pd-TiO_(2)-Ov is versatile in hydrogenating various alkene substrates including those with hydroxyl,ether,fluoride,and chloride functional groups in full conversion,thus offering a green method for transfer hydrogenation of alkenes.This study provides new insights and advances in current hydrogenation technology with water as the proton source.展开更多
The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precis...The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread...Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread application remains constrained by intrinsic mechanical limitations,fundamentally rooted in the nature of crystallographic defects.Atomic-scale modeling techniques are transforming our ability to unravel the structures,energetics,and dynamics of these defects and to explore their complex interactions,thereby guiding defect engineering in Mg alloys.However,the growing body of available data can make it difficult for researchers to identify critical knowledge gaps and promising areas for further exploration.To address this challenge,we highlight key research domains with significant potential for impactful advancements,aiming to illuminate these areas while inspiring innovative approaches and encouraging deeper exploration of pivotal topics that may shape the future of Mg alloy development.This review presents a comprehensive overview of the state-of-the-art in atomic-scale modeling of defects in Mg and its alloys.We introduce key simulation methodologies,including density functional theory and atomistic simulations,and highlight their applications to defect distribution,defect dynamics,and defect-defect interactions.By bridging fundamental insights in defects with alloy design strategies,this review aims to support and inspire the broader Mg research community and to underscore the growing impact of atomic-scale modeling in the accelerated development of high-performance Mg alloys.展开更多
Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable p...Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable porosity and defects for supercapacitors.Here,a novel strategy of coating ZIF-8 with coal tar pitch(CTP)is presented to tailor the porosity and defects of derived porous carbon,by which the inward contraction of ZIF-8 is prevented to enlarge the ultra-micropores,and the defects of ZIF-8-derived carbon are repaired to form a continuous conjugated network.The tradeoff between porosity and electrical conductivity endows this novel hard/soft carbon electrode with fast ion/electron diffusion,achieving high yet balanced capacitance and rate performance of a top-level specific area-normalized capacitance(40μF cm^(-2))and a capacitance retention of 52.1%at a 1000-fold increased current density.Meanwhile,the novel electrode realizes a high capacitance of 704 F g^(-1)at 1 A g^(-1)and capacitance retention of 91.9%after 50000 cycles in KOH+PPD electrolyte.This study provides an effective approach to designing novel hard/soft carbon with tuned porosity and carbon defects from MOFs and CTP for supercapacitors and other metal-ion batteries.展开更多
Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced li...Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced lightweight detection model based on the YOLOv11n framework.The proposed model introduces the Bi-level Routing Attention(BRAttention)mechanism to enhance defect feature extraction,enabling more detailed feature representation.It proposes Deep Progressive Cross-Scale Fusion Neck(DPCSFNeck)to better capture smallscale defects and incorporates a Multi-Scale Dilated Residual(MSDR)module to strengthen multi-scale feature representation.Furthermore,a Shared Detail-Enhanced Lightweight Head(SDELHead)is employed to reduce the risk of gradient explosion during training.Experimental results demonstrate that FD-YOLO achieves superior detection accuracy and Lightweight performance compared to the baseline YOLOv11n.展开更多
The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defec...The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset.展开更多
Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-base...Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-based machine learning(ML)technique combined with long-time ML-parameterized molecular dynamics simulations,we have systematically investigated charge transfer and friction at the interfaces between h-BN and water.The introduction of defects(including Stone-Wales,B-vacancy,N-vacancy,and B-vacancy/N-vacancy defects)into h-BN significantly enhances heterogeneous charge polarization and distribution at h-BN layers,as well as increases the friction coefficients at water/h-BN interfaces compared to perfect h-BN.The observed increase in interfacial friction of defected h-BN can be attributed to stronger charge transfer and higher charge density at the defected h-BN layers induced by interactions with water molecules.Our results offer deeper insights into the role of defects in modulating charge exchange and transfer between water and h-BN,as well as their impact on interfacial friction.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ...To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.展开更多
Cesium lead iodide perovskites offer promising stability and a bandgap near 1.7 eV,making them suitable as the top cell in tandem solar cells.However,the inorganic perovskite films suffer from a high defect density an...Cesium lead iodide perovskites offer promising stability and a bandgap near 1.7 eV,making them suitable as the top cell in tandem solar cells.However,the inorganic perovskite films suffer from a high defect density and substantial recombination losses,undermining their optoelectronic performances.Here,by activating the aromatic system,we develop 4-methoxybenzoylhydrazine(MeOBH)-modified CsPbI_(3) film with regulated crystallinity,suppressed non-radiative recombination,and improved interfacial energetic alignment.The resultant inorganic perovskite solar cells achieved a power conversion efficiency of 20.95%,along with enhanced phase stability owing to the strong coordination interaction between the lead cation and the hydrazide group.Encapsulated devices retain 90.4% of the initial performance after 624 h of maximum power point operation under the ISOS-L-1I protocol.展开更多
Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible s...Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible structural degradation.To overcome these limitations,we propose a rationally engineered nanoreactor architecture that stabilizes defect-rich MoS_(2)via interlayer incorporation of a carbon monolayer,followed by encapsulation within a nitrogen-doped carbon shell,forming a MoSSe@NC heterostructure.This tailored structure synergistically accelerates both K^(+)diffusion kinetics and electron transfer,enabling unprecedented rate performance(107 mAh g^(-1)at 10 Ag^(-1))and ultralong cyclability(86.5%capacity retention after 1200 cycles at 3 A g^(-1)).Mechanistic insights reveal a distinctive“adsorption-conversion”pathway,where sulfur vacancies on exposed S-Mo-S basal planes act as preferential K^(+)adsorption sites,effectively suppressing parasitic phase transitions during intercalation.In situ X-ray diffraction and transmission electron microscopy corroborate the structural reversibility of the conversion reaction,with the carbon matrix dynamically accommodating strain while preserving electrode integrity.This work not only advances the understanding of defect-driven interfacial chemistry in conversion-type materials but also provides a versatile strategy for designing high-performance anodes in next-generation PIBs through heterostructure engineering.展开更多
Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled t...Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.展开更多
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of ...To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.展开更多
Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluat...Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure,defective mechanical seals are often replaced empirically,which not only contributes to economic losses but also poses risks to reactor safety.To reveal the mechanism by which surface defects affect sealing performance,this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals.A defect characterization model was established by extracting key features of the observed defects.The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model.Changes in sealing gap,leakage rates,and film stiffness with respect to defect size,location,and other characteristics are discussed.This work contributes to a deeper understanding of defect failure mechanisms.These results can serve as a reference for evaluating defective seals.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC2410403。
文摘Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金supported by the National Key Research and Development Program of China (Grant No.2024YFA1210400)the National Science Fund for Distinguished Young Scholars (Grant No.52525101)+3 种基金the National Natural Science Foundation of China (Grant Nos.52450001 and 22409014)the International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No.52411540237)the Tencent Xplorer Prizethe support of the National High-Level Talent Special Support Programs—Young Talents。
文摘The interdependence of electrical parameters has long inhibited the progress of bismuth telluride(Bi_(2)Te3),limiting its widespread application in thermoelectric cooling and power generation.This work investigates the n-type Bi_(2)Te_(2.79)Se_(0.21)I_(0.004)(Bi_(2)(Te,Se)_(3),BTS)system with light Zn doping,revealing that Zn addition simultaneously enhances the Seebeck coefficient(S)and electrical conductivity(σ)through the modulation of defect composition and multi-level band regulation.The substitution of Zn atoms at Bi sites enhances S via bandgap(E_(g))widening,band flattening,and band splitting effects,contributing to a competitive power factor(PF)of∼60μW⋅cm^(−1)⋅K^(−2).Additionally,thermal conductivity is maintained at a low level,leading to an extraordinary figure-of-merit(ZT)value of∼1.3 at room temperature.Furthermore,the Bi_(2)Zn_(0.01)Te_(2.79)Se_(0.21)I_(0.004) system demonstrates impressive thermoelectric device performance,with a maximum cooling temperature difference(ΔT_(max))of∼70.0 K at 300 K,rising to∼78.0 K at 323 K and∼85.7 K at 343 K,as well as a maximum conversion efficiency(η_(max))of∼6.2%under aΔT of 200 K.This study clarifies the mechanism of Zn doping and presents a cost-effective strategy for enhancing the performance of n-type BTS thermoelectrics and their devices.
基金supported by the National Natural Science Foundation of China(Nos.51778618 and 52070192)the National Water Pollution Control and Treatment Science and Technology Major Project(No.2017ZX07102).
文摘Algal blooms in eutrophic water often produce microcystins,which can lead to multi-organ dysfunction and even mortality in many organisms.Therefore,the elimination of microcystins in water is an urgent issue that needs to be addressed.Herein,we develop a dual defect engineering strategy to construct graphite-like carbon nitride with N vacancies and-C≡N groups(NgCN)nanosheets for microcystin-LR(MC-LR)photodegradation.According to our theoretical calculations and actual findings,the NgCN nanosheets enhanced recyclability for the removal of MC-LR while also demonstrating outstanding photocatalytic efficacy,significantly surpassing the graphite-like carbon nitride under visible light,which is ascribed to efficient charge separation as well as narrowing the bandgap.Impressively,the water quality after photodegradation has been proven to be safe based on International Organization for Standardization(ISO)standards.This finding provides meaningful insights into understanding the relationship between defect engineering and photodegradation performance to design photocatalytic materials with higher activity for environmental remediation.
基金supported by the National Key Research and Development Program of China(2023YFD2200505)National Natural Science Foundation of China(22202105),Natural Science Foundation of Jiangsu Higher Education Institutions of China(21KJA150003)the Innovation and Entrepreneurship Team Program of Jiangsu Province(JSSCTD202345).
文摘Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supported palladium catalyst(Pd-TiO_(2)-Ov)for efficient photocatalytic water-donating transfer hydrogenation of anethole towards 4-n-propylanisole in a high yield of 99.9%,which is significantly higher compared to the pristine TiO_(2)-supported palladium catalyst(Pd-TiO_(2),74%).The enhanced performance is ascribed to the presence of oxygen vacancies,which facilitate light absorption and suppress the recombination of photogenerated electron-hole pairs.Furthermore,the Pd-TiO_(2)-Ov is versatile in hydrogenating various alkene substrates including those with hydroxyl,ether,fluoride,and chloride functional groups in full conversion,thus offering a green method for transfer hydrogenation of alkenes.This study provides new insights and advances in current hydrogenation technology with water as the proton source.
基金supported by the National Natural Science Foundation of China(No.52403035)the Shanghai Sailing Program(23YF1400300)+1 种基金the Fundamental Research Funds for the Central Universities(2232023D-05)the Weiqiao Teaching and Research Innovation Program.
文摘The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金support by the Deutsche Forschungsgemeinschaft(DFG)-Projektnummer 505716422the French National Research Agency(ANR)grants ANR22-CE92-0058-01(SILA)and ANR-21-CE08-0001(ATOUUM)+2 种基金support by the DFG through the projects A05 of the SFB1394 StructuralChemical Atomic Complexity-From Defect Phase Diagrams to Material Properties,project ID 409476157support funded by the DFG-Projektnummer 562592407 and 555365333.
文摘Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread application remains constrained by intrinsic mechanical limitations,fundamentally rooted in the nature of crystallographic defects.Atomic-scale modeling techniques are transforming our ability to unravel the structures,energetics,and dynamics of these defects and to explore their complex interactions,thereby guiding defect engineering in Mg alloys.However,the growing body of available data can make it difficult for researchers to identify critical knowledge gaps and promising areas for further exploration.To address this challenge,we highlight key research domains with significant potential for impactful advancements,aiming to illuminate these areas while inspiring innovative approaches and encouraging deeper exploration of pivotal topics that may shape the future of Mg alloy development.This review presents a comprehensive overview of the state-of-the-art in atomic-scale modeling of defects in Mg and its alloys.We introduce key simulation methodologies,including density functional theory and atomistic simulations,and highlight their applications to defect distribution,defect dynamics,and defect-defect interactions.By bridging fundamental insights in defects with alloy design strategies,this review aims to support and inspire the broader Mg research community and to underscore the growing impact of atomic-scale modeling in the accelerated development of high-performance Mg alloys.
基金funded by the National Natural Science Foundation of China (No. 52372037)the Natural Science Foundation of Anhui Province (Nos. 2408085MB032)+1 种基金the Outstanding Scientific Research and Innovation Team Program of Higher Education Institutions of Anhui Province (No. 2023AH010015)support from the Anhui International Research Center of Energy Materials Green Manufacturing and Biotechnology
文摘Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable porosity and defects for supercapacitors.Here,a novel strategy of coating ZIF-8 with coal tar pitch(CTP)is presented to tailor the porosity and defects of derived porous carbon,by which the inward contraction of ZIF-8 is prevented to enlarge the ultra-micropores,and the defects of ZIF-8-derived carbon are repaired to form a continuous conjugated network.The tradeoff between porosity and electrical conductivity endows this novel hard/soft carbon electrode with fast ion/electron diffusion,achieving high yet balanced capacitance and rate performance of a top-level specific area-normalized capacitance(40μF cm^(-2))and a capacitance retention of 52.1%at a 1000-fold increased current density.Meanwhile,the novel electrode realizes a high capacitance of 704 F g^(-1)at 1 A g^(-1)and capacitance retention of 91.9%after 50000 cycles in KOH+PPD electrolyte.This study provides an effective approach to designing novel hard/soft carbon with tuned porosity and carbon defects from MOFs and CTP for supercapacitors and other metal-ion batteries.
基金financially supported by the Fujian Provincial Department of Science and Technology,the Collaborative Innovation Platform Project for Key Technologies of Smart Warehousing and Logistics Systems in the Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone(No.2025E3024).
文摘Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced lightweight detection model based on the YOLOv11n framework.The proposed model introduces the Bi-level Routing Attention(BRAttention)mechanism to enhance defect feature extraction,enabling more detailed feature representation.It proposes Deep Progressive Cross-Scale Fusion Neck(DPCSFNeck)to better capture smallscale defects and incorporates a Multi-Scale Dilated Residual(MSDR)module to strengthen multi-scale feature representation.Furthermore,a Shared Detail-Enhanced Lightweight Head(SDELHead)is employed to reduce the risk of gradient explosion during training.Experimental results demonstrate that FD-YOLO achieves superior detection accuracy and Lightweight performance compared to the baseline YOLOv11n.
基金funded by Beijing Natural Science Foundation,grant number L241078.
文摘The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset.
基金supported by the National Natural Science Foundation of China(12472105)the Western Light Project of CAS(xbzg-zdsys-202118)+1 种基金the Fundamental Research Funds for the Central Universities(NO.NJ2024001)a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-based machine learning(ML)technique combined with long-time ML-parameterized molecular dynamics simulations,we have systematically investigated charge transfer and friction at the interfaces between h-BN and water.The introduction of defects(including Stone-Wales,B-vacancy,N-vacancy,and B-vacancy/N-vacancy defects)into h-BN significantly enhances heterogeneous charge polarization and distribution at h-BN layers,as well as increases the friction coefficients at water/h-BN interfaces compared to perfect h-BN.The observed increase in interfacial friction of defected h-BN can be attributed to stronger charge transfer and higher charge density at the defected h-BN layers induced by interactions with water molecules.Our results offer deeper insights into the role of defects in modulating charge exchange and transfer between water and h-BN,as well as their impact on interfacial friction.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金funded by Ministry of Education Humanities and Social Science Research Project,grant number 23YJAZH034The Postgraduate Research and Practice Innovation Program of Jiangsu Province,grant number SJCX25_17National Computer Basic Education Research Project in Higher Education Institutions,grant number 2024-AFCEC-056,2024-AFCEC-057.
文摘To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.
基金financially supported by the National Ten Thousand Talent Program (H.C.)。
文摘Cesium lead iodide perovskites offer promising stability and a bandgap near 1.7 eV,making them suitable as the top cell in tandem solar cells.However,the inorganic perovskite films suffer from a high defect density and substantial recombination losses,undermining their optoelectronic performances.Here,by activating the aromatic system,we develop 4-methoxybenzoylhydrazine(MeOBH)-modified CsPbI_(3) film with regulated crystallinity,suppressed non-radiative recombination,and improved interfacial energetic alignment.The resultant inorganic perovskite solar cells achieved a power conversion efficiency of 20.95%,along with enhanced phase stability owing to the strong coordination interaction between the lead cation and the hydrazide group.Encapsulated devices retain 90.4% of the initial performance after 624 h of maximum power point operation under the ISOS-L-1I protocol.
基金financially supported by the supported by Shandong Provincial Natural Science Foundation(ZR2024MB108)Taishan Young Scholar Program(tsqn202312312)Excellent Young Scholars of the Shandong Provincial Natural Science Foundation(Overseas)(2023HWYQ-112)。
文摘Conversion-type electrode materials hold significant promise for potassium-ion batteries(PIBs)due to their high theoretical capacities,yet their practical deployment is hindered by sluggish kinetics and irreversible structural degradation.To overcome these limitations,we propose a rationally engineered nanoreactor architecture that stabilizes defect-rich MoS_(2)via interlayer incorporation of a carbon monolayer,followed by encapsulation within a nitrogen-doped carbon shell,forming a MoSSe@NC heterostructure.This tailored structure synergistically accelerates both K^(+)diffusion kinetics and electron transfer,enabling unprecedented rate performance(107 mAh g^(-1)at 10 Ag^(-1))and ultralong cyclability(86.5%capacity retention after 1200 cycles at 3 A g^(-1)).Mechanistic insights reveal a distinctive“adsorption-conversion”pathway,where sulfur vacancies on exposed S-Mo-S basal planes act as preferential K^(+)adsorption sites,effectively suppressing parasitic phase transitions during intercalation.In situ X-ray diffraction and transmission electron microscopy corroborate the structural reversibility of the conversion reaction,with the carbon matrix dynamically accommodating strain while preserving electrode integrity.This work not only advances the understanding of defect-driven interfacial chemistry in conversion-type materials but also provides a versatile strategy for designing high-performance anodes in next-generation PIBs through heterostructure engineering.
基金supported by the Research Project on Strengthening the Construction of an Important Ecological Security Barrier in Northern China by Higher Education Institutions in the Inner Mongolia Autonomous Region(STAQZX202313)the Inner Mongolia Autonomous Region Education Science‘14th Five-Year Plan’2024 Annual Research Project(NGJGH2024635).
文摘Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.
基金supported by the Jilin Science and Technology Development Plan (20240101029JJ) for the following study:synchronized high-speed detection of surface shape and defects in the grinding stage of complex surfaces (KLMSZZ202305)for the high-precision wide dynamic large aperture optical inspection system for fine astronomical observation by the National Major Research Instrument Development Project (62127901)+2 种基金for ultrasmooth manufacturing technology of large diameter complex curved surface by the National Key R&D Program(2022YFB3403405)for research on the key technology of rapid synchronous detection of surface shape and subsurface defects in the grinding stage of large diameter complex surfaces by the International Cooperation Project(2025010157)The Key Laboratory of Optical System Advanced Manufacturing Technology,Chinese Academy of Sciences (2022KLOMT02-04) also supported this study
文摘To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.
基金Supported by National Natural Science Foundation of China(Grant No.51975315)National Science and Technology Major Project of China(Grant No.2019-IV-0020-0088).
文摘Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure,defective mechanical seals are often replaced empirically,which not only contributes to economic losses but also poses risks to reactor safety.To reveal the mechanism by which surface defects affect sealing performance,this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals.A defect characterization model was established by extracting key features of the observed defects.The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model.Changes in sealing gap,leakage rates,and film stiffness with respect to defect size,location,and other characteristics are discussed.This work contributes to a deeper understanding of defect failure mechanisms.These results can serve as a reference for evaluating defective seals.
