Types Of Defects Research Articles

Bispherical metal augment improved biomechanical stability in severe acetabular deficiency reconstruction: a comparative finite element analysis

BackgroundThis study used finite element analysis (FEA) to compare the biomechanical stability of bispherical metal augment (BA) and wedge-shaped trabecular-metal augment (TA) in different acetabular defect reconstruction models, thereby explaining the application value of this novel bispherical augment in complex hip revision.MethodsThree different acetabular defect pelvis models originating from three representative patients with different types of severe acetabular defects (Paprosky IIC, IIIA, and IIIB) were constructed and reconstruction with BA and TA technique was simulated. Based on the FEA models, the displacement of reconstruction implants, relative displacement of bone implants, and hemi-pelvic von Mises stress were investigated under static loads.ResultsBA acquired smaller reconstruction system displacement, less relative displacement of bone implants, and lower pelvic von Mises stress than TA in all Paprosky IIC, IIIA, and IIIB defect reconstructions.ConclusionThe FEA results show that BA could acquire favourable biomechanical stability in severe acetabular defect reconstruction. This technique is a reliable method in complex hip revision.

Effect of forming strategies on the microstructure and mechanical properties of thin-walled CuCrZr alloy fabricated by selective laser melting

Thin-walled structures are widely used in industrial fields such as radiators and critical aerospace components due to their advantages in lightweight design. However, traditional manufacturing techniques struggle to meet the high-precision requirements for complex components. Selective Laser Melting (SLM) technology, characterized by high design flexibility and high geometric production freedom, is a promising alternative. This study employs SLM technology to fabricate CuCrZr alloy thin walls and systematically investigates the effects of interlayer rotation angles and placement strategies on the forming quality and mechanical properties of the thin walls produced by SLM. The results indicate that different interlayer rotation angles and placement strategies lead to variations in defect types and shapes. When the interlayer rotation angle is 0° and the specimens are placed perpendicular to the recoater blade, numerous lack-of-fusion (LOF) defects are observed on the specimens. These lack-of-fusion defects are caused by factors such as low scanning line flatness and uneven energy distribution when the interlayer rotation angle is 0°. In contrast to the 0° interlayer rotation angle, the specimens with a 67° rotation angle exhibit significantly more curved columnar grains in the growth direction. The thin-walled specimens with a 67° interlayer rotation angle and placed parallel to the recoater blade show the best mechanical performance, with a maximum tensile strength of 254.19 MPa and an elongation of 48.81 %.

Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna

In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB). In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured. Five vertical copper lines printed side by side on the top of the substrate are used to demonstrate the working mechanism of the designed antenna sensor. Two different defect scenarios of open and short circuits with controlled locations are studied to determine variations in the return loss data of the proposed structure. MTM cell structures with cross lines enclosed by an octagon ring are periodically placed as part of the ground plane of the proposed antenna to obtain higher sensitivity of the designed and manufactured sensor. Antenna return loss behaviors in terms of the locations of the faults are employed to prepare a database to detect not only the presence of the faults but also determine their locations. Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

Diagnostic challenges and outcome of fatty acid oxidation defects in a tertiary care center in Lebanon

BackgroundFatty acid oxidation defects are rare autosomal recessive disorders with variable clinical manifestations and outcome. Early detection by systematic neonatal screening may improve their prognosis. Long-term outcome studies of these disorders in the Middle East and North Africa region are limited. The purpose of this study is to report the diagnostic challenges and outcome of fatty acid oxidation defects in a major tertiary care center in Lebanon, a resource-constrained country in the Middle East.MethodsA retrospective review of charts of all fatty acid oxidation defects sequential patients diagnosed and followed at our center was conducted. Collected data included: parental consanguinity, age at diagnosis, clinical presentation, biochemical profile, confirmatory diagnosis, treatment and outcome. A genotype–phenotype correlation was also performed, when available.ResultsSeven types of fatty acid oxidation defects were identified in a total of 34 patients from 21 families. Most families (79%) were consanguineous (first-degree cousins). The majority were diagnosed when clinically symptomatic (78%), at various ages between 10 days and 19 years (average: 2 years). Follow-up duration spanned between 2 months and 15 years (average: 5 years). The remainder of the patients were detected while still asymptomatic by systematic neonatal screening (9%) or due to positive family history (9%). The most common defect was carnitine transporter deficiency (50%) with an exclusive cardiac presentation related to a founder variant c.981C > T, (p.Arg254*) in the SLC22A5 gene. Medium chain acyl-CoA dehydrogenase deficiency was found in 13% only, which could be explained by the absence of systematic neonatal screening. Rare gene variants were detected in very long chain and multiple acyl-CoA dehydrogenase deficiency. The worse prognosis was observed in very long chain acyl-CoA dehydrogenase deficiency. The overall survival at last follow-up reached 75% with a complete reversal of symptoms with treatment in most patients (63%), despite their late diagnosis.ConclusionsOur experience highlights the diagnostic challenges and outcome of fatty acid oxidation defects in a resource-constrained country with high consanguinity rates. Physicians’ awareness and systematic neonatal screening are key for diagnosis. Larger genotype–phenotype studies are still needed to understand the natural history of these rare diseases and possibly improve their outcome.

Applications of Machine Learning in Textile Industry

It is a question to all of us that what is machine learning and its importance for business sectors. Smart textile when using AI and computer vision try to act on knitting machines and to reduce production rate of defective types with real-time defects along with irregularities to identify with the objective to bring defective production at zero level. When ML and SC technique, artificial neural network, genetic algorithm, and fuzzy logic have preponderance over all others like Bibliographic analysis revealed a drastic rise in number of studies since 2015. It is proposed that demand forecasting is quite volatile and sensitive to several factors. Data mining studies with classification and clustering techniques, ML algorithms were implemented in textile industry. In a study it was found that machine productivity is having negative effect on quality which was confirmed by the management and confirmed a huge important in quality processing in industrial sectors. Demand forecasting by ML driven techniques has proved minimization of forecasting errors by 50%. ML is being used by Adidas to forecast demand in products since 2016. Ridge Regression estimated the correlation between predictor and observation variables.

Application of Central Composite Design in the Drilling Process of Carbon Fiber-Reinforced Polymer Composite

Composite materials are utilized in various industries due to their advantageous properties. Drilling is a crucial process for joining these materials to construct the structures. During the drilling of composite materials, several types of defect can occur, with delamination being the most prevalent. Delamination adversely effects the properties of the drilled hole and diminishes the quality of the final structure. Thrust force is a key parameter used to monitor the drilling process; a higher thrust force increases the likelihood of defects, particularly delamination, in the drilled area. In this article, a central composite design is applied to the drilling process of carbon fiber-reinforced polymer (CFRP) composites, focusing on parameters such as rotational speed, feed rate, and the angle between the composite layer sequences. The objective is to minimize delamination factors and thrust force. The effect of drilling parameters on the responses is analyzed independently. The results indicate that the derived models can predict the thrust force and delamination factors in the drilling of CFRP composites.

First-principles study of vacancy-impurity (C, N, O) interactions in vanadium

Abstract Interstitial impurities of C, N, and O typically exist in V and its alloys, which have significant effects on the structure and properties of the materials due to their strong interaction with the vacancies. In this study, the structure and energy properties of vacancy-impurity complexes in V are comprehensively investigated by first principles calculations. The effects of impurity concentration and temperature on the concentration distribution of the complexes were studied using statistical methods. The results showed that the Vac n X 2 complexes exhibit a high sequential binding energy. There is an evident mutual attraction between the vacancy and the O atom, and it has a higher affinity than the C or N atom. The simple defect complexes of Vac 1 X 1–2 have always been the most abundant type of complex defect in V. The trapping effect of vacancy on C, N, and O atom is very significant at low temperatures rather than at high temperatures. This study would broaden the understanding of vacancy-impurity interactions in vanadium and provide new insights into the influence of C, N, and O on vacancy defect concentrations.

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

Integrated experimental and computational study on the effect of hydrogen in mechanical responses of pure tungsten

Hydrogen irradiation profoundly changes the microstructure and morphological characteristics of tungsten, ultimately resulting in the modifications to its mechanical properties. Unfortunately, the specific mechanisms through which hydrogen irradiation influences the mechanical behavior of tungsten have not been clearly elucidated. Therefore, this study aims to further improve the understanding of the impact of hydrogen irradiation on the mechanical responses and its microstructure dependence in tungsten by employing two advanced techniques: nano-indentation experiments and Molecular dynamics (MD) simulations. The experimental findings revealed that deuterium (D) exposure decreased the maximum shear stress required for the incipient plasticity and led to the occurrence of multiple pop-ins. Particularly noteworthy was the most remarkable change in mechanical behaviors observed prominently in the recrystallized tungsten after D exposure. The microstructure dependence of irradiation-induced change in mechanical responses was investigated by two series of MD simulation, indentation and dislocation glide simulation, focusing on the independent effects of each irradiation-induced defect type (interstitial, substitutional, vacancy, vacancy-hydrogen (Vac-H) cluster, void-hydrogen (Vo-H) cluster) on dislocation activities. Based on the MD simulations, it was eventually revealed that Vo-H clusters were the most influential agent affecting dislocation activities. They not only lowered the pop-in stress by heterogeneous dislocation nucleation but also caused strain serration by a continuous pinning/unpinning process during dislocation glide. Consequently, we concluded that the dominant presence of d-induced Vo-H clusters facilitated the onset of plastic yielding and led to multiple pop-in phenomena in recrystallized tungsten, whereas inserted hydrogen caused minimal change in mechanical responses by interacting with pre-existing dislocation in cold-rolled tungsten.

Modern Oncologic Maxillary Reconstruction.

After studying this article, the participant should be able to: (1) Have a broad understanding of the oncological principles relating to cancers involving the maxilla. (2) Define anatomically the various types of maxillectomy defects and their associated morbidity. (3) Understand the goals and principles of maxillary reconstruction. (4) Demonstrate an understanding of the strengths, limitations, and alternative reconstructive options for the various types of maxillectomy defects. Reconstruction of the maxilla following cancer resection has evolved over the past decade. Underpinned by advances in virtual surgery technology and an increased focus on dental rehabilitation, more sophisticated workflows using free bone flaps have become more commonplace. There are few reconstructions anywhere in the body that require the restoration of similarly intricate bone and soft-tissue relations with implications for both form and function, like the maxilla. Success demands careful anatomical definition of the defect, an understanding of the goals and principles of reconstruction, and the understanding of relative strengths and limitations of the various reconstructive options. Equally, as no one technique is without shortcomings and maxillary surgery can be associated with a significant complication profile, it is just as important to match the patient and oncologic context to the reconstruction. This article is intended to provide the reader with a broad overview of these core factors.

First-principle calculations of the electronic structure and optical properties of β-Ga2O3 with various vacancy defects

In this work, the effects of five types of vacancy defects on the electronic, optical and thermodynamic properties of β-Ga2O3 were investigated using first-principle calculations. The findings revealed that oxygen vacancies were more prevalent than gallium vacancies, with tetrahedral gallium vacancies being easier to form than octahedral ones. Vacancy defects induced a shift in the energy bands towards the lower energy end, with gallium vacancies reducing the bandgap from 4.9 eV to 3.1 eV, while the effect of oxygen vacancies on the bandgap was negligible. Oxygen vacancies introduced deep impurity levels near the Fermi level, while gallium vacancies introduced shallow impurity levels, primarily due to contributions from O-2p, Ga-4s, and Ga-4p orbital electrons. The differential charge density diagrams illustrated that gallium vacancies had a more pronounced impact on electronic distribution compared to oxygen vacancies, with VO3 exhibiting the least influence. As the temperature increases, the β-Ga2O3 with VO1 and VO2 exhibit higher thermodynamic stability compared to the intrinsic β-Ga2O3 and the one with VO3, while the intrinsic β-Ga2O3 has a superior heat capacity value compared to the β-Ga2O3 containing the five vacancy defects. The defect energy levels resulting from vacancies enhanced light absorption and conductivity, particularly oxygen vacancies, which improved the absorption capacity of β-Ga2O3 in the visible light range. These findings provide valuable insights for elucidating optical absorption experimental phenomena and photoluminescence.

Schnurri-3 inhibition rescues skeletal fragility and vascular skeletal stem cell niche pathology in the OIM model of osteogenesis imperfecta

Osteogenesis imperfecta (OI) is a disorder of low bone mass and increased fracture risk due to a range of genetic variants that prominently include mutations in genes encoding type I collagen. While it is well known that OI reflects defects in the activity of bone-forming osteoblasts, it is currently unclear whether OI also reflects defects in the many other cell types comprising bone, including defects in skeletal vascular endothelium or the skeletal stem cell populations that give rise to osteoblasts and whether correcting these broader defects could have therapeutic utility. Here, we find that numbers of skeletal stem cells (SSCs) and skeletal arterial endothelial cells (AECs) are augmented in Col1a2oim/oim mice, a well-studied animal model of moderate to severe OI, suggesting that disruption of a vascular SSC niche is a feature of OI pathogenesis. Moreover, crossing Col1a2oim/oim mice to mice lacking a negative regulator of skeletal angiogenesis and bone formation, Schnurri 3 (SHN3), not only corrected the SSC and AEC phenotypes but moreover robustly corrected the bone mass and spontaneous fracture phenotypes. As this finding suggested a strong therapeutic utility of SHN3 inhibition for the treatment of OI, a bone-targeting AAV was used to mediate Shn3 knockdown, rescuing the Col1a2oim/oim phenotype and providing therapeutic proof-of-concept for targeting SHN3 for the treatment of OI. Overall, this work both provides proof-of-concept for inhibition of the SHN3 pathway and more broadly addressing defects in the stem/osteoprogenitor niche as is a strategy to treat OI.

Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss

Considering the defect detection issues in electroluminescence (EL) of photovoltaic (PV) cell systems, lots of factors result in performance degradation, including defect diversity, data imbalance, scale difference, etc. Focal-EIoU loss, an effective defect detection solution for EL, is proposed based on the improved YOLOv5. Firstly, by analyzing the detection background and scale characteristics of EL defects, a binary classification is carried out in the system. Subsequently, a cascade detection network based on YOLOv5 is designed to further extract features from the binary-classified defects. The defect localization and classification are achieved in this way. To address the problem of imbalanced defect samples, a loss function is designed based on EIoU and Focal-F1 Loss. Experimental results are illustrated to show the effectiveness. Compared with the existing CNN-based deep learning approaches, the proposed focal loss calculation-based method can effectively improve the performance of handling sample imbalance. Moreover, in the detection of 12 types of defects, the Yolov5 algorithms can always obtain higher MAP (mean average precision) even with different parameter levels (Yolov5m: 0.791 vs. 0.857, Yolov5l: 0.798 vs. 0.862, Yolov5x: 0.802 vs. 0.867, Yolov5s: 0.793 vs. 0.865).

Thermal testing of corundum ceramics: Classical techniques for optical heating

The types of defects and ways of their formation in the production of tiles from corundum ceramics are considered. The integrity of tiles containing artificial defects has been investigated by the method of active thermal non-destructive testing using optical heating. Single- and double-sided thermal control schemes are applied, as well as various methods of software processing of thermograms. It has been established that the best results in detecting internal defects in ceramic tiles with a thickness of 10 mm during thermal stimulation using halogen lamps are obtained by the method of unilateral thermal testing.

Applying DMAIC Methodology to Reduce Egg Breakage Rates in Small-Scale Production Enterprise in Mandalay Region

This study employed the DMAIC methodology (Define, Measure, Analyze, Improve, Control) to investigate and minimize Unusable egg breakage (Completely broken eggs) at Golden Yolk farm. Data was collected using a triangulated approach that included direct observations of egg-handling processes, interviews with experienced farm personnel, and a review of historical breakage records in order to ensure reliability. Standardized checklists were also used, and initial analysis revealed a total defect rate of 0.74%, with Unusable breakage being the highest contributor at 0.47% among all types of defects. Qualitative tools like Pareto charts, FMEA, and Fishbone diagrams identified the main culprits of inadequate handling techniques and the absence of appropriate equipment. Subsequent interventions focused on implementing farm personnel training, standardized handling procedures, and introducing relevant equipment, significantly reducing Unusable egg breakage from 0.47% to 0.39%. This approach demonstrates the effectiveness of the DMAIC methodology for addressing Unusable egg breakage in the egg farm. These findings offer valuable insights for improving farm operations, ensuring consistent, high-quality egg production, and minimizing associated losses.

Task-Adaptive Angle Selection for Computed Tomography-Based Defect Detection.

Sparse-angle X-ray Computed Tomography (CT) plays a vital role in industrial quality control but leads to an inherent trade-off between scan time and reconstruction quality. Adaptive angle selection strategies try to improve upon this based on the idea that the geometry of the object under investigation leads to an uneven distribution of the information content over the projection angles. Deep Reinforcement Learning (DRL) has emerged as an effective approach for adaptive angle selection in X-ray CT. While previous studies focused on optimizing generic image quality measures using a fixed number of angles, our work extends them by considering a specific downstream task, namely image-based defect detection, and introducing flexibility in the number of angles used. By leveraging prior knowledge about typical defect characteristics, our task-adaptive angle selection method, adaptable in terms of angle count, enables easy detection of defects in the reconstructed images.

An insight into annealing mechanism of graphitized structures after irradiation

Graphite components are constantly subjected to a combined actions of annealing and irradiation due to high temperatures and thermal spike caused by irradiation when reactors are operating, resulting in complex microstructures along with matching changes in the material properties and dimensions. This study investigates the evolution process of initial irradiation defects at different annealing temperatures, which is difficult to captured by experiment. The findings indicate that 923K reactor-temperature annealing can also quickly restore isolated self-interstitial atoms and small-sized point defect clusters to intralayer locations on the nanosecond scale. However, multi-interlayer penetration damages and localized point defect aggregation from high-energy irradiation can lead to the disappearance of layered structural information, which allows these damage structures to develop into interlayer dislocations during annealing process. These interlayer dislocations further exacerbate the interlayer expansion of graphite crystal and may elucidate the mechanism behind volume expansion in nuclear graphite within reactors. Fully amorphized regions can also regain a layered structure approximately when guided by residual layered structures at 2000K, while chaotic regions or disordered layered structures form in the absence of guidance. These chaotic regions significantly exacerbate the volume expansion of graphite model, which may be related to the rapid volume increase observed in nuclear graphite components at the end of reactor life. The study provides insights into the transformation of initial irradiation defects into different types of defects under different temperatures and damage states, which serve as a critical foundation for assessing the evolution of irradiation defects in nuclear graphite for reactor applications and annealing studies.

AC breakdown and decomposition products detection characteristics of eco-friendly insulating medium in gas insulated switchgear

Gas Insulated Switchgear (GIS) was a crucial electrical equipment that provides the safety and security of power systems, and finding the eco-friendly alternatives of sulphur hexafluoride (SF6) as insulating medium in GIS has been an urgent demand in past decades. However, few studies reported the specific dielectric strength under various electrodes and partial discharge (PD) decomposition products detection of applying the eco-friendly gas in GIS. In this paper, the insulation properties among C4F7N/CO2 mixtures, dry air and CO2 were explored under the reduced-scale gas-insulated experimental device. The relationship between AC breakdown voltage and gas pressure was gained under the different electrodes and gap distances, and 0.6 MPa 7% C4F7N/93% CO2, 0.9 MPa dry air, 0.9 MPa CO2 all possess the capacity to be the insulating medium in eco-friendly gas insulated switchgear. Furthermore, the partial discharge experimental was also carried out to realize the decomposition products detection of dry air, which designs three common defect types including metal protrusion defects, air gap defects, and metal contamination defects. The decomposition products detection result shows that the contains of CO2, CO, and NO2 linearly increase with the increasing applied voltages and times, and the partial discharge defects are distinguished according to the ratios of c(CO2 + CO)/c(NO2) and c(CO2)/c(CO). The results can provide the basis for the further development of eco-friendly gas insulated switchgear.

The Influence of Defects on the Structural, Optical, and Antibacterial Properties of Cr/Cu Co-Doped ZnO Nanoparticles

In this research, Cu/Cr codoped ZnO nanoparticles (Zn₀.₉₉₋ₓCu₀.₀₁CrxO) were prepared using the sol-gel technique. The dopant ratio was systematically varied, ranging from x = 0.01 to 0.05 in increments of 0.01. This variation allowed for an exploration of the impact of defects on the structural, microstructural, optical, and antibacterial properties of Cu/Cr-codoped ZnO nanoparticles. Structural analysis of the Zn₀.₉₉₋ₓCu₀.₀₁CrxO nanoparticles was conducted using X-ray diffraction. The findings confirmed the presence of a hexagonal wurtzite structure in the ZnCuCrO nanoparticles, as determined by the c/a ratios in the range of 1.6013 and 1.6039. The surface morphology, crystallite size, and nanoparticle shapes were determined through scanning electron microscopy. The nanoparticle elemental compositions were analyzed through electron dispersive spectroscopy. The optical characteristics of the samples were assessed using a UV spectrophotometer. The energy band gaps for the nanoparticles were computed, and we discussed how dopant elements and defects affected their optical properties. We determined the refractive index using five distinct models. Notably, the highest band gap was observed for Zn₀.₉4Cu₀.₀₁Cr₀.₀5O (Eg=3.27 eV).Positron annihilation lifetime spectroscopy was employed to investigate the defect type, defect density, and crystal quality of Cu/Cr-codoped ZnO nanoparticles. The shortest lifetime τ1 obtained the highest value (0.181 ns) for Zn0.97 Cu0.01Cr0.02O nanoparticles. Zn₀.₉₉₋ₓCu₀.₀₁CrxO NPs were assessed for their antibacterial effects on E. coli (gram-negative) and S. aureus (gram-positive). Cu/Cr codoped ZnO nanoparticles show potential as materials for optoelectronic and sensor applications due to their broad band gap (Eg > 3) and high refractive index (n > 2).

Role of self-assembled molecules’ anchoring groups for surface defect passivation and dipole modulation in inverted perovskite solar cells

Abstract Inverted perovskite solar cells have gained prominence in industrial advancement due to their easy fabrication, low hysteresis effects, and high stability. Despite these advantages, their efficiency is currently limited by excessive defects and poor carrier transport at the perovskite–electrode interface, particularly at the buried interface between the perovskite and transparent conductive oxide (TCO). Recent efforts in the perovskite community have focused on designing novel self-assembled molecules (SAMs) to improve the quality of the buried interface. However, a notable gap remains in understanding the regulation of atomic-scale interfacial properties of SAMs between the perovskite and TCO interfaces. This understanding is crucial, particularly in terms of identifying chemically active anchoring groups. In this study, we used the star SAM ([2-(9H-carbazol-9-yl)ethyl] phosphonic acid) as the base structure to investigate the defect passivation effects of eight common anchoring groups at the perovskite–TCO interface. Our findings indicate that the phosphonic and boric acid groups exhibit notable advantages. These groups fulfill three key criteria: they provide the greatest potential for defect passivation, exhibit stable adsorption with defects, and exert significant regulatory effects on interface dipoles. Ionized anchoring groups exhibit enhanced passivation capabilities for defect energy levels due to their superior Lewis base properties, which effectively neutralize local charges near defects. Among various defect types, iodine vacancies are the easiest to passivate, whereas iodine-substituted lead defects are the most challenging to passivate. Our study provides comprehensive theoretical insights and inspiration for the design of anchoring groups in SAMs, contributing to the ongoing development of more efficient inverted perovskite solar cells.