Journal of Nondestructive Evaluation最新文献_第9页

Feature Selection of Multi-Channel Acoustic Signals Based on Weighted KL Distance for Boiler Tube Leakage Detection 基于加权KL距离的多通道声信号特征选择锅炉管泄漏检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-28 DOI: 10.1007/s10921-026-01370-6

Chao Wang, Yaran Wang, Qiuyu Wang, Da Liu, Hao Liu

{"title":"Feature Selection of Multi-Channel Acoustic Signals Based on Weighted KL Distance for Boiler Tube Leakage Detection","authors":"Chao Wang, Yaran Wang, Qiuyu Wang, Da Liu, Hao Liu","doi":"10.1007/s10921-026-01370-6","DOIUrl":"10.1007/s10921-026-01370-6","url":null,"abstract":"<div><p>The safe and stable operation of power plants is seriously threatened by boiler tube leakage accidents, making accurate detection of boiler tube leakages essential. However, minor leakages are difficult to detect under the influence of strong hot-state background noise in boilers, and the variety and quantity of available leakage acoustic signals on site are very limited. An equivalent criterion based on sound pressure amplitude is derived, and a method based on the proportional relationship of effective sound pressure is employed to map the hot-state background noise to the experimental environment, thereby constructing a leakage acoustic detection platform that is almost equivalent to the boiler environment. On the platform, various types of leakage acoustic signals with noise are collected, effectively addressing the issue of sample scarcity. To fully utilize the information from multi-channel acoustic sensors and enhance the detection accuracy of minor leakages, a calculation method based on weighted Kullback–Leibler (KL) distance is proposed to select the time-domain and frequency-domain statistical features and wavelet packet energy (WPE) features from four-channel acoustic signals. Experimental results show that the proposed feature selection method combined with a support vector machine (SVM) outperforms the detection method using an acoustic signal energy threshold, with a detection accuracy of 97.00% versus 65.36%. Furthermore, when detecting acoustic data from leakage source locations that were not involved in the training dataset, as well as data collected from the cold-state boiler environment, the detection accuracies of the proposed method are 92.67% and 93.53%, with the narrowest confidence intervals. The results demonstrate that the proposed method exhibits superior generalization capability compared to the feature selection method based on Relief-F and KL distance.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on Directivity and Propagation Path of Laser Ultrasound under Inhomogeneous Temperature in Nickel-based Superalloy 非均匀温度下激光超声在镍基高温合金中的方向性及传播路径研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-28 DOI: 10.1007/s10921-026-01364-4

Yan Tang, Hai Gong, Tao Zhang, Peng Cheng

{"title":"Study on Directivity and Propagation Path of Laser Ultrasound under Inhomogeneous Temperature in Nickel-based Superalloy","authors":"Yan Tang, Hai Gong, Tao Zhang, Peng Cheng","doi":"10.1007/s10921-026-01364-4","DOIUrl":"10.1007/s10921-026-01364-4","url":null,"abstract":"<div><p>Superalloys are widely used in aerospace for their high tensile and yield strength under high temperatures. Laser ultrasonic technology (LUT) is the most promising means to achieve online real-time detection in extreme environments. The directivity and propagation path of laser-generated ultrasound are affected by inhomogeneous temperature fields. To accurately obtain detection signals in high temperatures, it is necessary to study the influence of inhomogeneous temperature fields on the directivity and propagation path of laser ultrasound (LU). In this paper, the directivity and amplitude of LU in the nickel-based superalloy are investigated by theoretical analysis under different temperatures, and the propagation paths of longitudinal (L) waves generated in the thermoelastic regime and shear (S) waves generated in the ablation regime are analyzed in inhomogeneous temperature fields by the developed procedure. It is shown that the amplitude of the L wave decreased by 22%, and the relative position variation <span>({Delta beta }_{TL})</span> exceeded by 17%. Numerical simulations and experiments are conducted to study and verify the directivity, amplitude, and propagation paths of the LU under inhomogeneous temperature. The simulation results show that the amplitude of the L wave reduced by 17% in the thermoelastic regime, and the optimal receiving position decreases as the temperature gradients increase. The experiment results also show that the optimal receiving position decreases with rising temperature gradients, and verify the accuracy of the developed procedure and simulation models. The relationships between the temperature gradient and relative position changes are established in different temperature ranges. This work establishes a reference and theoretical basis for the application of LUT in high-temperature detection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical Investigation on the X-ray Imaging Inspection for Grout Loss Evaluation of Bridges 桥梁灌浆损失评价的x射线成像检测数值研究

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-20 DOI: 10.1007/s10921-026-01357-3

Zhenjun Li, Hengxi Chen, Akio Sugita, Masahiro Abe, Shuichi Hasegawa

{"title":"Numerical Investigation on the X-ray Imaging Inspection for Grout Loss Evaluation of Bridges","authors":"Zhenjun Li, Hengxi Chen, Akio Sugita, Masahiro Abe, Shuichi Hasegawa","doi":"10.1007/s10921-026-01357-3","DOIUrl":"10.1007/s10921-026-01357-3","url":null,"abstract":"<div><p>The X-ray imaging technique has evolved into one of the most effective inspection methods for evaluating internal degradation of bridges. For the inspection of grout loss in bridge evaluation, a numerical model was established to analyze its detectability in the X-ray non-destructive inspection. Different cases were developed by adjusting the rod radius and grout fraction inside the sheath in terms of the rod-to-sheath structure. The simulated projection revealed that the gray value difference of the healthy side Z<sub>G</sub> is lower than that of the air mixed side Z<sub>A+G</sub>, which agrees well with the lower attenuation coefficient of air compared with grout. A relative ratio R<sub>G</sub> was further defined to evaluate the detectability of each structure. Besides, small rod radius and large loss ratio led to significant differences between healthy and air mixed parts, indicating a better detectability of the structure. Additionally, the hypothesis testing was used to determine whether the structure was successfully detected based on the given p-value. The failures of the detections demonstrate the limitation of the X-ray source in inspecting tiny structures. Overall, the study is helpful in guiding the on-site bridge inspection and evaluation using X-rays theoretically.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Deep Anomaly Detection using Thermograpy based Convolution Autoencoder Framework 基于热像仪的卷积自编码器框架自动深度异常检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-17 DOI: 10.1007/s10921-026-01360-8

Naga Prasanthi Yerneni, L. Sainath, V. S. Ghali, Sk. Aashik, G. T. Vesala, V. Dhanunjana Chari, Fei Wang

{"title":"Automatic Deep Anomaly Detection using Thermograpy based Convolution Autoencoder Framework","authors":"Naga Prasanthi Yerneni, L. Sainath, V. S. Ghali, Sk. Aashik, G. T. Vesala, V. Dhanunjana Chari, Fei Wang","doi":"10.1007/s10921-026-01360-8","DOIUrl":"10.1007/s10921-026-01360-8","url":null,"abstract":"<div><p>Metals and composite structures are widely used in various industries due to their high mechanical strength and durability. However, defects generated during the manufacturing and operating phase limit their future usefulness and are recommended through non-invasive inspection. Quadratic frequency-modulated thermography (QFMT) is a non-destructive testing technique applicable to many materials due to its high-energy deposition at lower frequencies for enhanced defect signatures. The recent past in QFMT is advanced with machine learning and deep learning-based techniques. In contrast, the highly class-imbalanced and scarce nature of thermal profiles has recently gained interest in anomaly detection models. A stacked denoising convolution autoencoder (SDCAE) driven Local Outlier Factor (LOF) is proposed in the present article to identify defects in mild steel and carbon fiber reinforced polymer specimens inspected by QFMT. Deep features extracted from the temporal thermal profiles using pre-trained SDCAE are further fed to LOF for automatic defect detection. A quantitative comparison with recently introduced deep anomaly detection models and other autoencoder models for performance analysis strengthens the suitability of proposed method for automatic defect detection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Modal Source Separation for Electrical Impedance Tomography 电阻抗断层成像的多模态源分离

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-17 DOI: 10.1007/s10921-026-01363-5

Laura Homa, Mathew Schey, John Wertz

引用次数: 0

Unsupervised Domain Adaptation for Visual Anomaly Detection in Nondestructive Evaluation 无损评价中视觉异常检测的无监督域自适应

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-15 DOI: 10.1007/s10921-026-01354-6

Hien Vu Pham, Phan Xuan Tan, Minhhuy Le, Ngoc-Tam Bui

{"title":"Unsupervised Domain Adaptation for Visual Anomaly Detection in Nondestructive Evaluation","authors":"Hien Vu Pham, Phan Xuan Tan, Minhhuy Le, Ngoc-Tam Bui","doi":"10.1007/s10921-026-01354-6","DOIUrl":"10.1007/s10921-026-01354-6","url":null,"abstract":"<div><p>Nondestructive Evaluation (NDE) is essential for ensuring the reliability of industrial products, but deep learning–based visual inspection systems often fail when applied to new production conditions. These failures arise from domain shifts, such as changes in lighting, sensors, or materials, and correcting them usually requires costly re-annotation of data. To address this challenge, we present an unsupervised domain adaptation (UDA) framework tailored for one-class anomaly detection in Automated Visual Inspection (AVI). Our approach combines adversarial feature alignment with statistical distribution matching, allowing the model to learn domain-invariant representations while maintaining high sensitivity to defects. Evaluation on the MVTec Anomaly Detection benchmark shows that the proposed method substantially reduces performance loss under domain shifts, achieving AUROC above 0.95 and PRO scores above 0.97 across multiple cross-domain scenarios. These findings demonstrate the practical value of our framework and establish a foundation for deploying reliable, scalable, and expert-level anomaly detection solutions in dynamic industrial environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time Series Analysis of Acoustic Emission Measurements from an Experimental Avalanche Slope at Patsio in Great Himalaya 大喜马拉雅帕齐奥实验雪崩坡声发射测量的时间序列分析

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-07 DOI: 10.1007/s10921-026-01347-5

Prem Datt, Rahul Sheoran, Praveen K. Srivastava

{"title":"Time Series Analysis of Acoustic Emission Measurements from an Experimental Avalanche Slope at Patsio in Great Himalaya","authors":"Prem Datt, Rahul Sheoran, Praveen K. Srivastava","doi":"10.1007/s10921-026-01347-5","DOIUrl":"10.1007/s10921-026-01347-5","url":null,"abstract":"<div><p>Snow avalanches are formed due to the failure of a weak layer buried below a cohesive snow slab. Microscopic failures due to progressive damage within the snow release elastic waves known as ‘acoustic emissions (AEs)’. Attempts were made to monitor AEs prior to snow slope failure to obtain a potential tool for non-invasive snowpack stability assessment. We present acoustic emission measurements from a natural snowpack present on a test slope near Patsio field observatory located in Indian Himalaya. Time series analyses of AE parameters such as amplitude, counts and energy were carried out for the observation period. Snow related additional data such as snowpack evolution, snowstorms and pit profiles were also recorded to interpret the AE response of natural snowpack. The snowpack on the test slope was monitored continuously to observe any fracture or slope failure. We found an increased AE activity with loading during snowfall. For some of the events, distinct energy peaks were recorded simultaneously by all AE sensors. These high energy events may be attributed to the formation of large cracks within the snowpack. The amplitude-frequency distribution data of AE events were further analyzed, and the temporal variation of the b-value was estimated. We then tried to relate the temporal evolution of the b-value with the occurrence of cracks observed on the surface of snowpack. However, no clear pattern emerged. The b-value decreased toward the fracture event but overall showed strong fluctuations during the snowstorm.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01347-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Probability of Detection Evaluation for Acoustic Emission and Ultrasonic Testing in Composite Structures: Context-Dependent Reliability for Inspection Planning 复合材料结构声发射和超声检测的检测概率评估综述：检测计划的上下文依赖可靠性

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-06 DOI: 10.1007/s10921-026-01356-4

Jan Lean Tai

{"title":"A Review of Probability of Detection Evaluation for Acoustic Emission and Ultrasonic Testing in Composite Structures: Context-Dependent Reliability for Inspection Planning","authors":"Jan Lean Tai","doi":"10.1007/s10921-026-01356-4","DOIUrl":"10.1007/s10921-026-01356-4","url":null,"abstract":"<div><p>The probability of detection (POD) is widely adopted as a central reliability metric in non-destructive evaluation (NDE). However, existing review literature often treats POD as a comparable performance indicator across inspection modalities without sufficiently accounting for the contextual variability. This review advances a reliability-oriented synthesis by integrating detection physics, statistical modeling assumptions, and contextual drivers into a unified framework for interpreting POD in Acoustic Emission Testing (AET) and Ultrasonic Testing (UT). A fundamental distinction is established between the event-driven “Monitoring POD” characteristic of passive AET and the stimulus–response “Inspection POD” associated with active UT. These modalities differ in both sensing mechanisms and statistical formulations, such as hit–miss and â versus a approaches. They also exhibit varying sensitivities to contextual factors, including loading regimes, defect morphology, and environmental conditions. From this perspective, context is defined as a combined set of physical, operational, and human variables that condition the detection probability. Rather than ranking techniques by nominal a₉₀/₉₅ values, this review proposes a Context-Dependent POD (CDPOD) framework that clarifies how similar numerical metrics may arise from fundamentally different detection environments. A comparative synthesis of representative studies demonstrates that apparent performance differences frequently reflect contextual misalignment rather than intrinsic technique superiority, thereby limiting direct cross-modality comparison and transferability of laboratory-derived POD values to field applications. By reframing the POD as a context-sensitive reliability construct rather than a standalone scalar metric, this review provides a structured basis for inspection planning, uncertainty interpretation, and engineering decision-making in composite systems.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental Demonstration of 450kVp MesoFocus X-Ray Radiography for Rail Weld Inspection 450kVp中焦x射线检测钢轨焊缝的实验演示

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-04-06 DOI: 10.1007/s10921-026-01352-8

Abdulrasheed Sado, Jason P. Hayward

{"title":"Experimental Demonstration of 450kVp MesoFocus X-Ray Radiography for Rail Weld Inspection","authors":"Abdulrasheed Sado, Jason P. Hayward","doi":"10.1007/s10921-026-01352-8","DOIUrl":"10.1007/s10921-026-01352-8","url":null,"abstract":"<div><p>Rail welds are critical points of potential failure in railway infrastructure, yet conventional inspection methods such as ultrasonic testing and Ir-192 gamma radiography exhibit limited sensitivity to small internal flaws in thick rail sections. This study investigates the application of a 450 kVp MesoFocus X-ray source coupled with a Varex 4343HE flat-panel detector for non-destructive evaluation of rail welds. Experimental radiographs were acquired from a reference rail containing artificial flaws ranging from 0.5 mm to 3.0 mm in diameter. Image quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and basic spatial resolution, were analysed to quantify image performance. SNR measurements were performed using multiple lead filters of varying thicknesses; the 1 mm filter provided the optimal SNR. Frame averaging enhanced image quality up to approximately 200 frames, beyond which SNR plateaued, while both SNR and CNR decreased with increasing material thickness. Flaws as small as 0.5 mm were resolvable through 42 mm of steel, whereas the detectability of 1–3 mm flaws decreased in the 74-mm -thick rail head. These results establish the detection limits of 450 kV X-ray rail-weld radiography and provide quantitative guidance for optimizing deployable in-track inspection systems aimed at improving railway safety.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01352-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Enhanced Unsupervised Clustering for FRP-Concrete Debonding Using FEM–BEM Simulation-Guided Percussion Sound Reconstruction 基于FEM-BEM模拟的无监督聚类frp -混凝土去粘接

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-03-29 DOI: 10.1007/s10921-026-01353-7

Keyan Ji, Cheng Yuan, Qingzhao Kong, Lin Chen

{"title":"Physics-Enhanced Unsupervised Clustering for FRP-Concrete Debonding Using FEM–BEM Simulation-Guided Percussion Sound Reconstruction","authors":"Keyan Ji, Cheng Yuan, Qingzhao Kong, Lin Chen","doi":"10.1007/s10921-026-01353-7","DOIUrl":"10.1007/s10921-026-01353-7","url":null,"abstract":"<div><p>Percussion sound analysis is a promising non-destructive evaluation technique for detecting debonding between fiber-reinforced polymer (FRP) and concrete. However, conventional supervised learning approaches are constrained by their reliance on labeled training datasets and their limited capacity to reconcile the inherent variability and non-stationary characteristics of percussion sound signals, ultimately compromising model generalizability. To address these challenges, this study proposes an unsupervised clustering–based detection method guided by finite element–boundary element (FEM–BEM) simulations. First, FEM–BEM models simulate percussion sound signals under varying debonding conditions and percussion patterns, revealing common latent acoustic features strongly correlated with debonding across diverse percussion patterns. Guided by physics-derived insights, original percussion signals are reconstructed using wavelet packet decomposition, emphasizing debonding-related features while suppressing noise. Subsequently, unsupervised clustering is applied to the reconstructed signals to group percussion sounds without using labeled training data. The resulting clusters are then assigned semantic labels using a small set of reference signals from well-bonded regions. Experimental validation demonstrates that the method achieves an accuracy of 87.80%, outperforming supervised methods by 15.44%. The proposed method reduces the reliance on large amounts of labeled experimental data by using simulations to identify features sensitive to debonding, thereby guiding the detection algorithm to place greater emphasis on relevant frequency bands or features.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0