Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Visualizing extended defects at the atomic level in a Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire

The microstructure significantly influences the superconducting properties.Herein,the defect structures and atomic arrangements in high-temperature Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire are directly characterized via stateof-the-art scanning transmission electron microscopy.Interstitial oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped superconductor.Inclusion phases with varied numbers of CuO_(2) layers are found,and twist interfaces with different angles are identified.This study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.

Right Axillary Thoracotomy Should Be the Standard of Care for Repair of Non-Complex Congenital Heart Defects in Infants and Children

Minimally invasive approaches for cardiac surgery in children have been lagging in comparison to the adult world.A wide range of the most common congenital heart defects in infants and children can be repaired suc-cessfully through a variety of non-sternotomy incisions.This has been shown to be associated with superior cos-metic results,shorter hospital stays,and rapid return to full activity compared to sternotomy.These approaches have been around for decades,but they have not been widely adopted for a variety of reasons.Right axillary thor-acotomy is one of these approaches that we believe should be the new standard for the repair of a wide variety of heart defects in children and will be the focus of our current review.

Built defects of homogeneous junction to enhance the lithium storage capacity of niobium pentoxide materials

Niobium pentoxide(Nb_(2)O_(5))is deemed one of the promising anode materials for lithium-ion batteries(LIBs)for its outstanding intrinsic fast Li-(de)intercalation kinetics.The specific capacity,however,is still limited,because the(de)intercalation of excessive Li-ions brings the undesired stress to damage Nb_(2)O_(5) crystals.To increase the capacity of Nb_(2)O_(5) and alleviate the lattice distortion caused by stress,numerous homogeneous H-and M-phases junction interfaces were proposed to produce coercive stress within theNb_(2)O_(5)crystals.Such interfaces bring about rich oxygen vacancies with structural shrinkage tendency,which pre-generate coercive stress to resist the expansion stress caused by excessive Li-ions intercalation.Therefore,the synthesized Nb_(2)O_(5) achieves the highest lithium storage capacity of 315 mA h g−1 to date,and exhibits high-rate performance(118 mA h g^(-1) at 20 C)as well as excellent cycling stability(138 mA h g^(-1) at 10 C after 600 cycles).

Droplet Self-Driven Characteristics on Wedge-Shaped Surface with Composite Gradients:A Molecular Dynamics Study

The self-driven behavior of droplets on a functionalized surface,coupled with wetting gradient and wedge patterns,is systematically investigated using molecular dynamics(MD)simulations.The effects of key factors,including wedge angle,wettability,and wetting gradient,on the droplet self-driving effect is revealed from the nanoscale.Results indicate that the maximum velocity of droplets on hydrophobic wedge-shaped surfaces increases with the wedge angle,accompanied by a rapid attenuation of driving force;however,the average velocity decreases with the increased wedge angle.Conversely,droplet movement on hydrophilic wedge-shaped surfaces follows the opposite trend,particularly in terms of average velocity compared to the hydrophobic case.Both wedge-shaped and composite gradient wedge-shaped surfaces are found to induce droplet motion,with droplets exhibiting higher speeds and distances on hydrophobic surfaces compared to hydrophilic surfaces,regardless of surface type.Importantly,the inclusion of wettability gradients significantly influences droplet motion,with hydrophobic composite gradient wedge-shaped surfaces showing considerable improvements in droplet speed and distance compared to their hydrophilic counterparts.By combining suitable wettability gradients with wedge-shaped surfaces,the limitations inherent in the wettability gradient range and wedge-shaped configuration can be mitigated,thereby enhancing droplet speed and distance.The findings presented in this paper offer valuable insights for the design of advanced functional surfaces tailored for manipulating droplets in real-world applications.

Effects of Initial Defects on Effective Elastic Modulus of Concrete with Mesostructure

An exquisite mesostructure model was presented to predict the effective elastic modulus of concrete,in which concrete is realized as a four-phase composite material consisting of coarse aggregates,mortar matrix,interfacial transition zone(ITZ),and initial defects.With the three-dimensional(3D)finite element(FE)simulation,the highly heterogeneous composite elastic behavior of concrete was modeled,and the predicted results were compared with theoretical estimations for validation.Monte Carlo(MC)simulations were performed with the proposed mesostructure model to investigate the various factors of initial defects influencing the elastic modulus of concrete,such as the shape and concentration(pore volume fraction or crack density)of microspores and microcracks.It is found that the effective elastic modulus of concrete decreases with the increase of initial defects concentration,while the distribution and shape characteristics also exert certain influences due to the stress concentration caused by irregular inclusion shape.

Effect of Vacancy Defects on the Properties of CoS_(2) and FeS_(2)

In order to explore the effect of vacancy defects on the structural,electronic,magnetic and optical properties of CoS_(2) and FeS_(2),first-principles calculation method was used to investigate the alloys.The calculated results of materials without vacancy are consistent with those reported in the literatures,while the results of materials with vacancy defect were different from those of literatures due to the difference vacancy concentration.The Co vacancy defect hardly changes the half-metallic characteristic of CoS_(2).The Fe vacancy defect changes FeS_(2) from semiconductor to half-metal,and the bottom of the spin-down conduction band changes from the p orbital state of S to the d(t_(2g))orbital state of Fe,while the top of the valence band remains the d orbital d(eg)state of Fe.The half-metallic Co vacancy defects of CoS_(2) and Fe vacancy defects of FeS_(2) are expected to be used in spintronic devices.S vacancy defects make both CoS_(2) and FeS_(2) metallic.Both the Co and S vacancy defects lead to the decrease of the magnetic moment of CoS_(2),while both the Fe and S vacancy defects lead to the obvious magnetic property of FeS_(2).Vacancy defects enhance the absorption coefficient of infrared band and long band of visible light obviously,and produce obvious red shift phenomenon,which is expected to be used in photoelectric devices.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Theoretical Design of Defects as a Driving Force for Ion Transport in Li_(3)OBr Solid Electrolyte

Due to ever-increasing concerns about safety issues in using Li ionic batteries,solid electrolytes have extensively explored.The Li-rich antiperovskite Li_(3)OBr has been considered as a promising solid electrolyte candidate,but it still suffers challenges to achieve a high ionic conductivity owing to the high intrinsic symmetry of the crystal lattice.Herein,we presented a design strategy that introduces various point defects and grain boundaries to break the high lattice symmetry of Li_(3)OBr crystal,and their effect and microscopic mechanism of promoting the migration of Li-ion were explored theoretically.It has been found that Li_(i)are the dominant defects responsible for the fast Li-ion diffusion in bulk Li_(3)OBr and its surface,but they are easily trapped by the grain boundaries,leading to the annihilating of the Frenkel defect pair V'_(Li)+Li_(i),and thus limits the V'_(Li)diffusion at the grain boundaries.The V_(Br)defect near the grain boundaries can effectively drive V'_(Li)across the grain boundary,thereby converting the carrier of Li^(+)migration from Li,in the bulk and surface to V'_(Li)at the grain boundary,and thus improving the ionic conductivity in the whole Li_(3)OBr crystal.This work provides a comprehensive insight into the Li^(+)transport and conduction mechanism in the Li_(3)OBr electrolyte.It opens a new way of improving the conductivity for all-solid-state Li electrolyte material through the defect design.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Kneadable dough-type hydrogel transforming from dynamic to rigid network to repair irregular bone defects

Irregular bone defects,characterized by unpredictable size,shape,and depth,pose a major challenge to clinical treatment.Although various bone grafts are available,none can fully meet the repair needs of the defective area.Here,this study fabricates a dough-type hydrogel(DR-Net),in which the first dynamic network is generated by coordination between thiol groups and silver ions,thereby possessing kneadability to adapt to various irregular bone defects.The second rigid covalent network is formed through photocrosslinking,maintaining the osteogenic space under external forces and achieving a better match with the bone regeneration process.In vitro,an irregular alveolar bone defect is established in the fresh porcine mandible,and the dough-type hydrogel exhibits outstanding shape adaptability,perfectly matching the morphology of the bone defect.After photocuring,the storage modulus of the hydrogel increases 8.6 times,from 3.7 kPa(before irradiation)to 32 kPa(after irradiation).Furthermore,this hydrogel enables effective loading of P24 peptide,which potently accelerates bone repair in Sprague–Dawley(SD)rats with critical calvarial defects.Overall,the dough-type hydrogel with kneadability,space-maintaining capability,and osteogenic activity exhibits exceptional potential for clinical translation in treating irregular bone defects.

Simulation on dynamic characteristics of TC4 cutting with crack defects

Titanium alloys play an important role in aerospace and other fields.However,after precision forging and cold rolling process,some defects will appear on the subsurface of titanium alloy bars,thus reducing the surface quality and precision of turning process.This study aimed at exploring the effect of crack defects on TC4 cutting.Firstly,the finite element cutting simulation model of TC4 material with crack defects was established in ABAQUS.Then,the cutting parameters such as cutting force,stress concentration,chip morphology,residual stress were obtained by changing the variables such as the size and height of crack defects.Finally,the turning experiment was carried out on centerless lathe.The results show that the cutting force changes abruptly when the defect position is located on the cutting path,the maximal stress occurs at the tip of the defect,and the mutation of stress value is more serious with the increase of defect size;the buckling deformation of chip morphology occurs and becomes less serious with the increase of the distance between the defect position and the workpiece surface;the surface residual stress near the defect is related to the stress when the tool is close to the defect,the larger defect size and the closer to the machined surface,the greater the residual stress.Therefore,under certain processing conditions,the TC4 material should avoid large size defects or increase the distance between defects and the machined surface,so as to obtain better and stable surface quality.

Evaluation of internal void related defects in reinforced concrete slab using electromagnetic wave properties

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure.The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering.The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain,with the damage localized using the calculated relative permittivity of the measurements.The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage.As a result of verification,the proposed method quickly identified the presence of damage inside the concrete,especially for honeycomb-like defects located at the top of the rebar.This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods,such as ultrasonic methods.

Micro defects formation and dynamic response analysis of steel plate of quasi-cracking area subjected to explosive load

As the protective component,steel plate had attracted extensive attention because of frequently threats of explosive loads.In this paper,the evolution of microstructure and the mechanism of damage in the quasi-cracking area of steel plate subjected to explosive load were discussed and the relationships between micro defects and dynamic mechanical response were revealed.After the explosion experiment,five observation points were selected equidistant from the quasi-cracking area of the section of the steel plate along the thickness direction,and the characteristics of micro defects at the observation points were analyzed by optical microscope(OM),scanning electron microscope(SEM) and electron backscattered diffraction(EBSD).The observation result shows that many slip bands(SBs) appeared,and the grain orientation changed obviously in the steel plate,the two were the main damage types of micro defects.In addition,cracks,peeling pits,grooves and other lager micro defects were appeared in the lower area of the plate.The stress parameters of the observation points were obtained through an effective numerical model.The mechanism of damage generation and crack propagation in the quasicracking area were clarified by comparing the specific impulse of each observation point with the corresponding micro defects.The result shows that the generation and expansion of micro defects are related to the stress area(i.e.the upper compression area,the neutral plane area,and the lower tension area).The micro defects gather and expand at the grain boundary,and will become macroscopic damage under the continuous action of tensile stress.Besides,the micro defects at the midpoint of the section of the steel plate in the direction away from the explosion center(i.e.the horizontal direction) were also studied.It was found that the specific impulse at these positions were much smaller than that in the thickness direction,the micro defects were only SBs and a few micro cracks,and the those decreased with the increase of the distance from the explosion center.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

Printed circuit boards(PCBs)provide stable connections between electronic components.However,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable losses.Therefore,it is crucial to detect defective printed circuit boards during the generation process.Traditional detection methods have low accuracy in detecting subtle defects in complex background environments.In order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization methods.The neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep features.Simplify the aspect ratio of the bounding box to alleviate the issue of specificity values.After training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of 76.7.The comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

Perfecting HER catalysts via defects:Recent advances and perspectives

Defect engineering has become a promising approach to improve the performance of hydrogen evolution reaction(HER)catalysts.Non-noble transition metal-based catalysts(TMCs)have shown significant promise as effective alternatives to traditional platinum-group catalysts,attracting considerable attention.However,the industrial application of TMCs in electrocatalytic hydrogen production necessitates further optimization to boost both catalytic activity and stability.This review comprehensively examines the types,fabrication methods,and characterization techniques of various defects that enhance catalytic HER activity.Key advancements include optimizing defect concentration and distribution,coupling heteroatoms with vacancies,and leveraging the synergy between bond lengths and defects.In-depth discussions highlight the electronic structure and catalytic mechanisms elucidated through in-situ characterization and density functional theory calculations.Additionally,future directions are identified,exploring novel defect types,emphasizing precision synthesis methods,industrial-scale preparation techniques,and strategies to enhance structural stability and understanding the in-depth catalytic mechanism.This review aims to inspire further research and development in defect-engineered HER catalysts,providing pathways for high efficiency and cost-effectiveness in hydrogen production.