Surface Defects Research Articles

Unsupervised bearing raceway surface defect detection based on improved f-AnoGAN

Abstract For addressing bearing raceway surface defects, supervised deep learning methods face challenges such as insufficient defect samples and imbalance between defect and non-defect samples. To overcome these issues, we propose an enhanced fast anomaly detection with generative adversarial networks (f-AnoGAN) unsupervised detection algorithm. Firstly, SE-Block modules based on SE attention mechanism and residual structure are integrated into the encoder, aiding the network in focusing on channel information while alleviating gradient vanishing problems. Secondly, transfer learning is introduced to effectively enhance the algorithm’s detection performance and generalization ability. Finally, utilizing bearing images collected from industrial sites, a self-built bearing raceway surface defect dataset is constructed, and extensive experiments are conducted. Experimental results demonstrate that the improved algorithm achieves an area under curve score of 99.96% on the self-built bearing raceway surface defect dataset, representing a 7.07% improvement over the f-AnoGAN algorithm, meeting the requirements for online detection in bearing industry applications.

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

2,2'-Bipyridyl-4,4'-Dicarboxylic Acid Modified Buried Interface of High-Performance Perovskite Solar Cells.

The regulation of interfaces remains a critical and challenging aspect in the pursuit of highly efficient and stable perovskite solar cells (PSCs). Here, 2,2'-bipyridyl-4,4'-dicarboxylic acid (HBPDC) is incorporated as an interfacial layer between SnO2 and perovskite layers in PSCs. The two carboxylic acid moieties on HBPDC bind to SnO2 through esterification, while its nitrogen atoms, possessing lone electron pairs, interact with uncoordinated lead (Pb2+) atoms through Lewis acid-base interactions. This dual functionality enables simultaneous passivation of surface defects on both the SnO2 and buried perovskite layers. In addition, the electron-deficient nature of HBPDC enhances interfacial energy band alignment and facilitates electron transfer from the perovskite to SnO2. Furthermore, the incorporation of HBPDC strengthens the interfacial adhesion, improving mechanical reliability. As a result, the PSCs exhibited an impressive power conversion efficiency (PCE) of 25.41% under standard AM 1.5G conditions, along with remarkable environmental stability.

2,2′‐Bipyridyl‐4,4′‐Dicarboxylic Acid Modified Buried Interface of High‐Performance Perovskite Solar Cells

The regulation of interfaces remains a critical and challenging aspect in the pursuit of highly efficient and stable perovskite solar cells (PSCs). Here, 2,2′‐bipyridyl‐4,4′‐dicarboxylic acid (HBPDC) is incorporated as an interfacial layer between SnO2 and perovskite layers in PSCs. The two carboxylic acid moieties on HBPDC bind to SnO2 through esterification, while its nitrogen atoms, possessing lone electron pairs, interact with uncoordinated lead (Pb2+) atoms through Lewis acid‐base interactions. This dual functionality enables simultaneous passivation of surface defects on both the SnO2 and buried perovskite layers. In addition, the electron‐deficient nature of HBPDC enhances interfacial energy band alignment and facilitates electron transfer from the perovskite to SnO2. Furthermore, the incorporation of HBPDC strengthens the interfacial adhesion, improving mechanical reliability. As a result, the PSCs exhibited an impressive power conversion efficiency (PCE) of 25.41% under standard AM 1.5G conditions, along with remarkable environmental stability.

Intelligent Grinding System for Medium/Thick Plate Welding Seams in Construction Machinery Using 3D Laser Measurement and Deep Learning

With the rapid development of the construction machinery industry, thick plate welds are increasingly needing efficient, accurate, and intelligent processing. This study proposes an intelligent grinding system using 3D line laser measurement and deep learning algorithms to solve the problems of inefficiency and inaccuracy existing in traditional weld grinding methods. This study makes use of 3D line laser measurement technology and deep learning algorithms in tandem, which perform automated 3D measurement and analysis to extract key parameters of the weld seam, in conjunction with deep learning algorithms applied on image data of the weld seam for the automatic classification, positioning, and segmentation of the weld seam. The entire work is divided into the following: image acquisition, motion control, and image processing. Based on various weld seam detection algorithms, the selected model was MNet-based DeepLab-V3. An intelligent trimming system for welding seams based on deep learning was constructed. Experiments were conducted to verify the feasibility and accuracy of the 3D line laser measurement technology for weld seam inspections, and that the deep learning algorithm can effectively identify the type and location of the weld seam, thus predicting the trimming strategy. With an accuracy far superior to conventionally based methods in accurate detection and regrinding of weld surface defects, the system proves advantageous for improved weld regrinding productivity and quality. It was determined that the system presents significant advantages in reinforcing weld regrinding when it comes to efficiency and quality, thus initiating a paradigm of using intelligent treatments for medium/thick plate welds in the construction machinery industry.

The Evaluation of Pulsed Eddy Current Technique for Detection and Characterization of Surface Defect in Carbon Steel Welded Sample: An Experimental Study

The Evaluation of Pulsed Eddy Current Technique for Detection and Characterization of Surface Defect in Carbon Steel Welded Sample: An Experimental Study

Post-Treating Grain Boundaries and Surface Defects by Long-Chain Polymer for Highly Efficient and Stable Perovskite Solar Cells.

Grain boundaries (GBs) and surface defects within perovskite films are inherent challenges that affect the photovoltaic performance of perovskite solar cells (PSCs. In this work, Nylon 11 (N11) is utilized, a long-chain polymer, for post-treating the GBs and surface defects within FAPbI3 films. The multifunctional groups of N11 exhibit unique passivation abilities, enabling self-regulation and selective correction of reverse-charged defects. Post-treating with N11 results in high-quality FAPbI3 films characterized by tight GBs and low surface defect density. Despite fabrication under full open-air conditions, the N11 post-treatment significantly enhances the power conversion efficiency (PCE) value of FAPbI3 PSCs, increasing it from the reference value of 21.89% to 23.54%. Importantly, the long alkyl chain present in N11 significantly enhances the humidity stability of the PSCs. Unencapsulated PSCs treated with N11 maintain 89% of their initial PCE after exposure to air with 30% relative humidity (RH) for 1000h, demonstrating resilience to elevated humidity levels. This work highlights the substantial improvement in the photovoltaic performance of PSCs achieved through the post-treatment with N11.

Studying the Defects in Spinel Compounds: Discovery, Formation Mechanisms, Classification, and Influence on Catalytic Properties.

Spinel ferrites demonstrate extensive applications in different areas, like electrodes for electrochemical devices, gas sensors, catalysts, and magnetic adsorbents for environmentally important processes. However, defects in the real spinel structure can change the many physical and chemical properties of spinel ferrites. Although the number of defects in a crystal spinel lattice is small, their influence on the vast majority of physical properties could be really decisive. This review provides an overview of the structural characteristics of spinel compounds (e.g., CoFe2O4, NiFe2O4, ZnFe2O4, Fe3O4, γ-Fe2O3, Co3O4, Mn3O4, NiCo2O4, ZnCo2O4, Co2MnO4, etc.) and examines the influence of defects on their properties. Attention was paid to the classification (0D, 1D, 2D, and 3D defects), nomenclature, and the formation of point and surface defects in ferrites. An in-depth description of the defects responsible for the physicochemical properties and the methodologies employed for their determination are presented. DFT as the most common simulation approach is described in relation to modeling the point defects in spinel compounds. The significant influence of defect distribution on the magnetic interactions between cations, enhancing magnetic properties, is highlighted. The main defect-engineering strategies (direct synthesis and post-treatment) are described. An antistructural notation of active centers in spinel cobalt ferrite is presented. It is shown that the introduction of cations with different charges (e.g., Cu(I), Mn(II), Ce(III), or Ce(IV)) into the cobalt ferrite spinel matrix results in the formation of various point defects. The ability to predict the type of defects and their impact on material properties is the basis of defect engineering, which is currently an extremely promising direction in modern materials science.

Scanning Electron Microscopic Evaluation of Surface Defects of Remover Retreatment File After Single and Multiple Uses.

This study aimed to evaluate surface defects of Remover rotary Nickel-Titanium (NiTi) files after single and multiple uses in conventional endodontic retreatment procedures using scanning electron microscopy (SEM). Eighty acrylic blocks, simulating root canals with a 1.5 mm internal diameter, a 5 mm radius of curvature, and a 55° curvature, were utilized. After chemomechanical preparation and obturation, 24 new Remover files (N30, 7%, L23) were randomly assigned to three groups: single use, triple use, and six uses. The files were operated at 600 rpm with a torque of 2.5 Ncm, cleaned, and sterilized after each use. SEM analysis at magnifications of 100x, 250x, and 500x revealed surface defects, including tip deformation, microcracks, fracture, unwinding, surface pitting, and blade disruption. Deformation was observed in 75% of the files after a single use and in 100% of the files after three and six uses. Microcracks were absent after single use but appeared in 25% and 87.5% of files after three and six uses, respectively, showing a statistically significant increase (p < 0.001). Surface pitting also significantly increased among groups (p = 0.004). No fractures were observed in any group. The most common defects were tip deformation (91.7%) and surface pitting (70.8%). The findings suggest that repeated use of NiTi files significantly increases surface defects, elevating the risk of fatigue fractures. Thus, the results recommend limiting the reuse of Remover files to a maximum of 3x. Further research is needed to correlate defect types with anatomical factors and to assess file effectiveness in retreatment scenarios.

Research on Surface Defect Positioning Method of Air Rudder Based on Camera Mapping Model

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.

Microenvironment modulation of biocatalyst derived from natural cellulose of wheat straw for enhancing p-nitrophenol degradation via boosting peroxymonosulfate activation

Defect-rich nitrogen-doped biocatalyst (B-NC) was synthesized from natural cellulose of wheat straw using straightforward mechanical method and one-step pyrolysis approach. In contrast to the nitrogen-doped biocatalyst (NC), by leveraging the synergistic effects of nitrogen dopants and surface defects, the microenvironment-modulated B-NC exhibited the enhanced mass transfer efficiency and a significant improvement in reactivity for p-nitrophenol degradation (111 %–196 %). The catalyst's exceptional performance primarily arose from graphitic N, pyridinic N and CO active sites, which mainly derived from the cellulose structure of wheat straw and nitrogen dopants. Electron paramagnetic resonance and quenching tests confirmed that the B-NC/peroxymonosulfate system generated more reactive species (SO4•−, •OH, O2•−, and 1O2) during p-nitrophenol degradation, surpassing the NC/peroxymonosulfate system. Additionally, both density functional theory calculations and electrochemical experiments provided evidence of peroxymonosulfate strongly adsorbing onto B-NC's defect sites, facilitating the formation of catalyst/peroxymonosulfate* complexes and promoting electron transfer processes. This research provides valuable insights into the regulation of defects in nitrogen-doped biocatalyst derived from natural cellulose, presenting a promising solution for remediating refractory organic pollutants.

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments.

Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection.

Deciphering the structural origin for the remarkable CO oxidation activities of the CuO-based catalysts with diverse morphological CeO2 supports

In this work, octahedral, cubic, particle-like, spherical and rod-shaped nano-CeO2 supports were synthesized by hydrothermal method. CuO/CeO2 catalysts were then prepared via an ultrasonic-assisted impregnation method, employing these differently shaped CeO2 supports. The resulting catalysts were tested for their performance in CO oxidation. The findings revealed that the CuO active sites significantly influenced the oxygen storage and release capacity of CeO2, thereby improving its catalytic activity for low-temperature CO oxidation. The key factors affecting the CO oxidation performance of CuO/CeO2 catalysts with different CeO2 morphologies were investigated through various characterizations. It was found that the specific surface area was not the primary factor in determining catalytic activity. Instead, the exposed crystal planes played a crucial role. By adjusting the crystal plane exposure of CeO2, the supported 1CuO/CeO2 catalysts exhibited different crystal surface configurations. Notably, the 1CuO/CeO2-S catalyst, with exposed (100) and (110) crystal planes, and the 1CuO/CeO2-P catalyst, with exposed (111), (100), and (110) planes, exhibited higher surface defect concentrations. This increased defect concentration facilitated the formation of Cu+ species, enhancing the redox properties and further improving the overall catalytic performance of CO oxidation.

Enhancing Thermochemical Energy Storage Performance of Perovskite with Sodium Ion Incorporation

Perovskite materials are promising for thermochemical energy storage due to their ability to undergo redox cycling over a wide temperature range. Although BaCoO3 exhibits excellent air cycling properties, its heat storage capacity in air remains suboptimal. This study introduces Na into the lattice structure to enhance oxygen vacancy formation and mobility. DFT+U simulations of the surface structure of Na-doped BaCoO3−δ indicate that incorporating Na improves surface stability and facilitates the formation of surface oxygen vacancies. NaxBa1−xCoO3−δ compounds were synthesized using a modified sol–gel method, and their properties were investigated. The experimental results demonstrate that Na doping significantly enhances the redox activity of the material. The heat storage capacity increased by above 50%, with the Na0.0625Ba0.9375CoO3−δ solid solution achieving a heat storage density of up to 341.7 kJ/kg. XPS analysis reveals that Na doping increases the concentration of surface defect oxygen, leading to more active oxygen release sites at high temperatures. This enhancement in redox activity aligns with DFT predictions. During high-temperature cycling, the distribution of Na within the material becomes more uniform, and no performance degradation is observed after 300 cycles. Even after 450 cycles, Na0.0625Ba0.9375CoO3−δ retains over 96% of its initial redox activity, significantly outperforming fresh BaCoO3−δ. These findings elucidate the mechanism by which Na doping enhances the thermochemical heat storage performance of BaCoO3−δ and provide new insights for the design of perovskite-based materials.

A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection

In the manufacturing process of printed circuit boards (PCBs), surface defects have a significant negative impact on product quality. Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated Convolution (SPD-Conv) (YOLOv5_ES) network improved YOLOv5s framework. Firstly, the detection head is optimized by removing medium and large detection layers, fully leveraging the capability of the small detection head to identify minor target defects. This approach not only improves model accuracy but also achieves lightweighting. Secondly, in order to further reduce the number of parameters and computational costs, the SPD-Conv is introduced to improve the feature extraction capability by reducing information loss. Thirdly, a EMA module is introduced to fuse context information of different scales, enhancing the model’s generalization ability. Compared to the YOLOv5s model, there is a 3.1% improvement in mean average precision (mAP0.5), a 55.8% reduction in model parameters, and a 4.8% reduction in giga floating-point operations per second (GFLOPs). These results demonstrate a significant improvement in both accuracy and model parameter efficiency.

Palladium-Functionalized Nanostructured Nickel-Cobalt Oxide as Alternative Catalyst for Hydrogen Sensing Using Pellistors.

To meet today's requirements, new active catalysts with reduced noble metal content are needed for hydrogen sensing. A palladium-functionalized nanostructured Ni0.5Co2.5O4 catalyst with a total Pd content of 4.2 wt% was synthesized by coprecipitation to obtain catalysts with an advantageous sheet-like morphology and surface defects. Due to the synthesis method and the reducible nature of Ni0.5Co2.5O4 enabling strong metal-metal oxide interactions, the palladium was highly distributed over the metal oxide surface, as determined using scanning transmission electron microscopy and energy-dispersive X-ray investigations. The catalyst tested in planar pellistor sensors showed high sensitivity to hydrogen in the concentration range below the lower flammability limit (LFL). At 400 °C and in dry air, a sensor response of 109 mV/10,000 ppm hydrogen (25% of LFL) was achieved. The sensor signal was 4.6-times higher than the signal of pristine Ni0.5Co2.5O4 (24.6 mV/10,000 ppm). Under humid conditions, the sensor responses were reduced by ~10% for Pd-functionalized Ni0.5Co2.5O4 and by ~27% for Ni0.5Co2.5O4. The different cross-sensitivities of both catalysts to water are attributed to different activation mechanisms of hydrogen. The combination of high sensor sensitivity to hydrogen and high signal stability over time, as well as low cross-sensitivity to humidity, make the catalyst promising for further development steps.

In-Situ Anchoring of Co Single-Atom Synergistically with Cd Vacancy of Cadmium Sulfide for Boosting Asymmetric Charge Distribution and Photocatalytic Hydrogen Evolution.

In the context of reshaping the energy pattern, designing and synthesizing high-performance noble metal-free photocatalysts with ultra-high atomic utilization for hydrogen evolution reaction (HER) still remains achallenge. In a streamlined synthesis process, in-situ single atom anchoring is performed in parallel with HER by irradiating a precursory defect-state CdS/Co suspension (Co-DCdS-Ss) system under simulated sunlight and the in-situ synthesizing single-atom Co photocatalyst (Co5:DCdS) exhibits further improved catalytic performance (60.10 mmolg-1h-1) compared with Co-DCdS-Ss (18.09 mmolg-1h-1), reaching an apparent quantum yield of 57.6% at 500nm and a solar-chemical energy conversion efficiency (SCC) of 6.26% at AM 1.5G. In-depth characterization tests and density functional theory (DFT) calculations prove that the anchoring of Co single atom deepens the asymmetric charge distribution of the two-coordination S atom adjacent to the cadmium vacancy (VCd). The synergy between electron delocalization VCd and Co single atom on the catalyst surface is constructed, which bifunctional sites responsible for boosting water adsorption-dissociation and hydrogen evolution. This study advances the understanding of the underlying mechanisms of synergy between surface defects and metal single atoms and opens a new horizon for the development of advanced materials in the field of photocatalysis.

Regulation of the Buried Interface to Achieve Efficient HTL-Free All-Inorganic CsPbI2Br-Based Perovskite Solar Cells.

The large voltage loss (Vloss) mainly stems from the mismatch between the perovskite film and electron transport layer in CsPbI2Br-based all-inorganic perovskite solar cells (I-PSCs), which restricts the power conversion efficiency (PCE) of devices. To address this issue, potassium benzoate (BAP) is first introduced as a bifunctional passivation material to regulate the TiO2/CsPbI2Br interface, reduce the Vloss, and improve the photovoltaic performance of CsPbI2Br-based I-PSCs. Eventually, the champion PCE of CsPbI2Br-based I-PSCs without a hole transport layer modified by BAP (Target-PSCs) improves to 14.90% from the 12.14% of reference PSCs. The open-circuit voltage (Voc) increases to 1.27 V from the initial 1.14 V after BAP modification. A series of characterizations show that BAP modification can not only optimize the energy level alignment of I-PSCs but also passivize the surface defects caused by uncoordinated Cs+/Pb2+. Moreover, the Target-PSCs without encapsulation demonstrate better thermal stability, which can maintain 107.6% of the original PCE after annealing at 160 °C for 140 min in humid air. While the reference PSCs only maintain 76.5% of their initial PCE after annealing at the same process. This work provides a simple strategy to modify the buried interface and improve the performance of CsPbI2Br-based I-PSCs.

An Improved YOLOv8n Algorithm for Steel Surface Defect Detection

Due to the manufacturing process and other aspects, steel surface defects vary in shape and size, which is a difficult point for defect detection. For this reason, this paper proposes a steel surface defect detection algorithm based on YOLOv8n. The algorithm improves the C2f module of Backbone in this network by adding Context Guided Block (CG block), which can effectively extract the local features, surrounding context and global context, and integrate this information to improve the precision and accuracy of detection. Meanwhile, we also change the detection header of YOLOv8 to Detect_ASFF by introducing the Adaptive Spatial Feature Fusion (ASFF) technique. This improvement effectively filters out the conflicting information, which can allow the model to have a better adaptability at different scales. In this paper, Shape-IoU is adopted as the bounding box loss function to make up for the traditional IoU loss that ignores the intrinsic properties of the bounding box in the calculation process. After optimisation, mAP@0.5 are improved to 67.9% and mAP@0.5~0.95 are improved to 35.1%, which is 2.8% and 1.1% respectively. The improved algorithm in this paper has improved in detection accuracy, which proves the practicality and effectiveness of the algorithm in this paper.

Novel framework-confined Cu@ 13X catalyst with excellent resistance to toxic SO2 as an eco-friendly substitute for hazardous V-based NH3-SCR catalyst

V2O5-WO3/TiO2 (VWTi) catalyst has long been utilized in fixed source flue gases to purify harmful NO gas. However, VWTi is classified as a hazardous material because of its harm to human health and environment. To address this issue, a low-cost green Cu-based zeolite catalyst (Cu@13X) with a wide temperature range was synthesized using an in-situ hydrothermal method. This method is intended to control the adsorption capacity of toxic SO2 by regulating the location of Cu species. UV-Vis DRS and EXAFS analyses revealed that a significant amount of Cu was encapsulated within the 12-membered ring pores of the 13X molecular sieve. SO2-TPD and DFT calculations further indicated that Cu@ 13X exhibits reduced SO2 chemical adsorption capacity. Consequently, this green catalyst demonstrated superior catalytic performance, maintaining superior NOx conversion for over 70 h in the mixture gas containing 250 ppm SO2; while the Cu/13X catalyst lacked NH3-SCR catalytic activity under the same conditions. Moreover, Cu@ 13X catalyst also has excellent NH3-SCR catalytic performance in low temperature flue gas below 250 °C, which is significantly better than the hazardous VWTi catalyst. Characterization techniques such as NH3-TPD, H2-TPR, and XPS confirmed that the Cu@ 13X catalyst possesses excellent acidity, robust redox capabilities, and abundant surface defects. In-situ DRIFT spectra further illustrated that the NH3-SCR reaction on the catalyst surface adheres to the Eley-Rideal mechanism both before and after the introduction of toxic SO2. This study offers a fresh perspective on the development of framework-confined NH3-SCR catalysts and elucidates the mechanism behind the sulfur resistance of these catalysts. And the green Cu@ 13X catalyst is anticipated to serve as an effective alternative to the hazardous VWTi catalyst.