Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

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.

Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional properties.However,the material reliability is significantly affected by various microscopic defects.In this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and quantified.Molecular dynamics(MD)simulations are employed to observe the failure evolution of different microscopic defects.The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion.At the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain points.The impact of defect types on the failure process is also discussed.Furthermore,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale model.The validity of the crack propagation model is confirmed through tensile tests.Finally,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD model.Our findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

Recent progress in the research on magnesium and magnesium alloy foils:A short review

Magnesium and magnesium alloy foils have great potential for application in battery anodes,electromagnetic shielding,optics and acoustics,and biology because of their excellent specific damping,internal dissipation coefficients,magnetic and electrical conductivities,as well as high theoretical specific capacity.However,magnesium alloys exhibit poor deformation ability due to their hexagonal close-packed crystal structure.Preparing magnesium and magnesium alloy foils with thicknesses of less than 0.1 mm is difficult because of surface oxidation and grain growth at high temperatures or severe anisotropy after cold rolling that leads to cracks.Numerous methods have been applied to prepare magnesium alloy foils.They include warm rolling,cold rolling,accumulative roll bonding,electric plastic rolling,and on-line heating rolling.Defects of magnesium and magnesium alloy foils during preparation,such as edge cracks and breakage,are important factors for consideration.Herein,the current status of the research on magnesium and magnesium alloy foils is summarized from the aspects of foil preparation,defect control,performance characterization,and application prospects.The advantages and disadvantages of different preparation methods and defect(edge cracks and breakage)mechanisms in the preparation of foils are identified.

Icariin accelerates bone regeneration by inducing osteogenesisangiogenesis coupling in rats with type 1 diabetes mellitus

BACKGROUND Icariin(ICA),a natural flavonoid compound monomer,has multiple pharmacological activities.However,its effect on bone defect in the context of type 1 diabetes mellitus(T1DM)has not yet been examined.AIM To explore the role and potential mechanism of ICA on bone defect in the context of T1DM.METHODS The effects of ICA on osteogenesis and angiogenesis were evaluated by alkaline phosphatase staining,alizarin red S staining,quantitative real-time polymerase chain reaction,Western blot,and immunofluorescence.Angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis.A bone defect model was established in T1DM rats.The model rats were then treated with ICA or placebo and micron-scale computed tomography,histomorphometry,histology,and sequential fluorescent labeling were used to evaluate the effect of ICA on bone formation in the defect area.RESULTS ICA promoted bone marrow mesenchymal stem cell(BMSC)proliferation and osteogenic differentiation.The ICA treated-BMSCs showed higher expression levels of osteogenesis-related markers(alkaline phosphatase and osteocalcin)and angiogenesis-related markers(vascular endothelial growth factor A and platelet endothelial cell adhesion molecule 1)compared to the untreated group.ICA was also found to induce osteogenesis-angiogenesis coupling of BMSCs.In the bone defect model T1DM rats,ICA facilitated bone formation and CD31hiEMCNhi type H-positive capillary formation.Lastly,ICA effectively accelerated the rate of bone formation in the defect area.CONCLUSION ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs.

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.

Advances and challenges in direct additive manufacturing of dense ceramic oxides

Ceramic oxides,renowned for their exceptional combination of mechanical,thermal,and chemical properties,are indispensable in numerous crucial applications across diverse engineering fields.However,conventional manufacturing methods frequently grapple with limitations,such as challenges in shaping intricate geometries,extended processing durations,elevated porosity,and substantial shrinkage deformations.Direct additive manufacturing(dAM)technology stands out as a state-of-the-art solution for ceramic oxides production.It facilitates the one-step fabrication of high-performance,intricately designed components characterized by dense structures.Importantly,dAM eliminates the necessity for post-heat treatments,streamlining the manufacturing process and enhancing overall efficiency.This study undertakes a comprehensive review of recent developments in dAM for ceramic oxides,with a specific emphasis on the laser powder bed fusion and laser directed energy deposition techniques.A thorough investigation is conducted into the shaping quality,microstructure,and properties of diverse ceramic oxides produced through dAM.Critical examination is given to key aspects including feedstock preparation,laser-material coupling,formation and control of defects,in-situ monitoring and simulation.This paper concludes by outlining future trends and potential breakthrough directions,taking into account current gaps in this rapidly evolving field.

Loss of SHROOM3 affects neuroepithelial cell shape through regulating cytoskeleton proteins in cynomolgus monkey organoids

Neural tube defects(NTDs)are severe congenital neurodevelopmental disorders arising from incomplete neural tube closure.Although folate supplementation has been shown to mitigate the incidence of NTDs,some cases,often attributable to genetic factors,remain unpreventable.The SHROOM3 gene has been implicated in NTD cases that are unresponsive to folate supplementation;at present,however,the underlying mechanism remains unclear.Neural tube morphogenesis is a complex process involving the folding of the planar epithelium of the neural plate.To determine the role of SHROOM3 in early developmental morphogenesis,we established a neuroepithelial organoid culture system derived from cynomolgus monkeys to closely mimic the in vivo neural plate phase.Loss of SHROOM3 resulted in shorter neuroepithelial cells and smaller nuclei.These morphological changes were attributed to the insufficient recruitment of cytoskeletal proteins,namely fibrous actin(F-actin),myosin II,and phospho-myosin light chain(PMLC),to the apical side of the neuroepithelial cells.Notably,these defects were not rescued by folate supplementation.RNA sequencing revealed that differentially expressed genes were enriched in biological processes associated with cellular and organ morphogenesis.In summary,we established an authentic in vitro system to study NTDs and identified a novel mechanism for NTDs that are unresponsive to folate supplementation.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Effects of vacancy and external electric field on the electronic properties of the MoSi_(2)N_(4)/graphene heterostructure

Recently,the newly synthesized septuple-atomic layer two-dimensional(2D)material MoSi_(2)N_(4)(MSN)has attracted attention worldwide.Our work delves into the effect of vacancies and external electric fields on the electronic properties of the MSN/graphene(Gr)heterostructure using first-principles calculation.We find that four types of defective structures,N-in,N-out,Si and Mo vacancy defects of monolayer MSN and MSN/Gr heterostructure are stable in air.Moreover,vacancy defects can effectively modulate the charge transfer at the interface of the MSN/Gr heterostructure as well as the work function of the pristine monolayer MSN and MSN/Gr heterostructure.Finally,the application of an external electric field enables the dynamic switching between n-type and p-type Schottky contacts.Our work may offer the possibility of exceeding the capabilities of conventional Schottky diodes based on MSN/Gr heterostructures.

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.

Selective his bundle pacing eliminates crochetage sign:A case report

BACKGROUND Crochetage sign is a specific electrocardiographic manifestation of ostium secundum atrial septal defects(ASDs),which is associated with the severity of the left-to-right shunt.Herein,we reported a case of selective his bundle pacing(SHBP)that eliminated crochetage sign in a patient with ostium secundum ASD.CASE SUMMARY A 77-year-old man was admitted with a 2-year history of chest tightness and shortness of breath.Transthoracic echocardiography revealed an ostium secundum ASD.Twelve-lead electrocardiogram revealed atrial fibrillation with a prolonged relative risk interval,incomplete right bundle branch block,and crochetage sign.The patient was diagnosed with an ostium secundum ASD,atrial fibrillation with a second-degree atrioventricular block,and heart failure.The patient was treated with selective his bundle pacemaker implantation.After the procedure,crochetage sign disappeared during his bundle pacing on the electrocardiogram.CONCLUSION S-HBP eliminated crochetage sign on electrocardiogram.Crochetage sign may be a manifestation of a conduction system disorder.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

Secondary diabetes due to different etiologies:Four case reports

BACKGROUND As research on diabetes continues to advance,more complex classifications of this disease have emerged,revealing the existence of special types of diabetes,and many of these patients are prone to misdiagnosis and underdiagnosis,leading to treatment delays and increased health care costs.The purpose of this study was to identify four causes of secondary diabetes.CASE SUMMARY Secondary diabetes can be caused by various factors,some of which are often overlooked.These factors include genetic defects,autoimmune disorders,and diabetes induced by tumours.This paper describes four types of secondary diabetes caused by Williams–Beuren syndrome,Prader–Willi syndrome,pituitary adenoma,and IgG4-related diseases.These cases deviate significantly from the typical progression of the disease due to their low incidence and rarity,often leading to their neglect in clinical practice.In comparison to regular diabetes patients,the four individuals described here exhibited distinct characteristics.Standard hypoglycaemic treatments failed to effectively control the disease.Subsequently,a series of examinations and follow-up history confirmed the diagnosis and underlying cause of diabetes.Upon addressing the primary condition,such as excising a pituitary adenoma,providing glucocorticoid supplementation,and implementing symptomatic treatments,all patients experienced a considerable decrease in blood glucose levels,which were subsequently maintained within a stable range.Furthermore,other accompanying symptoms improved.CONCLUSION Rare diseases causing secondary diabetes are often not considered in the diag-nosis of diabetes.Therefore,it is crucial to conduct genetic tests,antibody detection and other appropriate diagnostic measures when necessary to facilitate early diagnosis and intervention through proactive and efficient management of the underlying condition,ultimately improving patient outcomes.

Polarization pinning at antiphase boundaries in multiferroic YbFeO_(3)

The switching characteristics of ferroelectrics and multiferroics are influenced by the interaction of topological defects with domain walls.We report on the pinning of polarization due to antiphase boundaries in thin films of the multiferroic hexagonal YbFeO_(3).We have directly resolved the atomic structure of a sharp antiphase boundary(APB)in YbFeO_(3) thin films using a combination of aberration-corrected scanning transmission electron microscopy(STEM)and total energy calculations based on density-functional theory(DFT).We find the presence of a layer of FeO_(6) octahedra at the APB that bridges the adjacent domains.STEM imaging shows a reversal in the direction of polarization on moving across the APB,which DFT calculations confirm is structural in nature as the polarization reversal reduces the distortion of the FeO_(6) octahedral layer at the APB.Such APBs in hexagonal perovskites are expected to serve as domain-wall pinning sites and hinder ferroelectric switching of the domains.

Defect mediated losses and degradation of perovskite solar cells:Origin impacts and reliable characterization techniques

The rapid advancement of halide-based hybrid perovskite materials has garnered significant research attention,particularly in the domain of photovoltaic technology.Owing to their exceptional optoelec-tronic properties,they demonstrated power conversion efficiency(PcE)of over 25%in single junction solar cells.Despite the notable progress in PCE over the past decade,the inherent high defect density pre-senting in perovskite materials gives rise to several loss mechanisms and associated ion migration in per-ovskite solar cells(PsCs)during operational conditions.These factors collectively contribute to a significant stability challenge in PsCs,placing their longevity far behind for commercialization.While numerous reports have explored defects,ion migration,and their impacts on device performance,a com-prehensive correlation between the types of defects and the degradation kinetics of perovskite materials and PsCs has been lacking.In this context,this review aims to provide a comprehensive overview of the origins of defects and ion migration,emphasizing their correlation with the degradation kinetics of per-ovskite materials and PsCs,leveraging reliable characterization techniques.Furthermore,these charac-terization techniques are intended to comprehend loss mechanisms by different passivation approaches to enhance the durability and PCE of PSCs.