Journal of Nondestructive Evaluation最新文献

{"title":"Hyperspectral Imaging Combined With a Hybrid Deep Learning Framework for Simultaneous Monitoring of Multiple Quality Indicators in Frozen-thawed Beef","authors":"Kunshan Yao,&nbsp;Yibo Zhang,&nbsp;Jun Sun,&nbsp;Bing Zhang,&nbsp;Lina Wang,&nbsp;Xiaojiao Du,&nbsp;Xingyu Ji,&nbsp;Xin Zhou","doi":"10.1007/s10921-026-01443-6","DOIUrl":"10.1007/s10921-026-01443-6","url":null,"abstract":"<div><p>Monitoring the quality of frozen meat is crucial for ensuring food safety. This study aimed to evaluate multiple quality indicators of frozen-thawed beef using hyperspectral imaging (HSI), including pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and carbonyl content. Conventional hyperspectral analysis methods rely on complex manual feature engineering and require separate model training for different attributes, which is cumbersome and inefficient. To address these limitations, this study proposed a hybrid deep learning framework integrating computer vision processing, multi-task learning and hyperparameter adaptive optimization, enabling simultaneous assessment of multiple quality indicators. Comparative experiments demonstrated that the proposed method achieved optimal predictive performance for pH, TVB-N, TBARS and carbonyl content, with all coefficient of determination for prediction (R²_P) values exceeding 0.91 and ratio of performance deviation (RPD) values greater than 3.3. By applying the established model to each pixel of the hyperspectral images, chemical distribution maps for the four indicators were generated, providing a comprehensive and visual assessment of beef quality. The results indicated the great potential of integrating HSI with deep learning for simultaneously evaluating multiple quality indicators in frozen-thawed beef.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189002","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}

{"title":"The Utilization of Ultrasonic Characteristics for Identifying Precursors of Low-Cycle Fatigue Crack Formation in AA5083-H112 Aluminum","authors":"Hsuan-Han Lai,&nbsp;En Hong Zhou,&nbsp;Chi-Ming Lin,&nbsp;Chao Hui Ou,&nbsp;Yun-His Chao,&nbsp;Weite Wu","doi":"10.1007/s10921-026-01442-7","DOIUrl":"10.1007/s10921-026-01442-7","url":null,"abstract":"<div><p>This study investigates the evolution of fatigue-induced damage in AA5083-H112 aluminum alloy under low-cycle fatigue using linear and nonlinear ultrasonic techniques in conjunction with X-ray diffraction (XRD) and optical microscopy. Three independent tensile specimens and three independent fatigue specimens were tested to verify the repeatability of the experimental results. Residual stress and full width at half maximum (FWHM) gradually increased during the early fatigue stage because of dislocation accumulation, followed by a decrease as fatigue damage progressed. Acoustic velocity exhibited significant changes during the early fatigue stage, whereas the root mean square (RMS) amplitude and the time of flight (TOF) varied predominantly during the later stages of fatigue. Optical microscopy revealed potential microcrack initiation sites at approximately 2200 fatigue cycles, followed by clearly identifiable microcracks at 2700 cycles. During this transition, the nonlinear ultrasonic SPC-I exhibited a pronounced increase during the transition from potential microcrack initiation to visible microcrack formation, indicating high sensitivity to fatigue damage evolution prior to the development of visible surface cracks. The combined results demonstrate that the integration of SPC-I with acoustic velocity, TOF, residual stress, FWHM, and optical microscopy provides a reliable and complementary approach for monitoring fatigue damage evolution, particularly during the transition from microstructural damage accumulation to microcrack formation.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189100","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}

{"title":"Economic Feasibility of Acoustic Emission Monitoring in CO2 Pipelines: An Experimental Study","authors":"Vignesh. V. Shanbhag,&nbsp;Nabil Belbachir,&nbsp;Rune Schlanbusch","doi":"10.1007/s10921-026-01436-5","DOIUrl":"10.1007/s10921-026-01436-5","url":null,"abstract":"<div><p>CO₂ pipelines are critical for carbon capture, utilisation, and storage (CCUS) and industrial applications. Gas leaks from these pipelines can pose a significant safety hazard, impact the environment, and cause economic losses. When leakage occurs from the high-pressure pipeline, it creates stress waves due to localised energy release, which can be detected by using acoustic emission (AE) sensors. There have been considerable attempts made in literature to monitor leaks and perform source localisation of leaks in CO₂ pipelines using AE, as this technique is non-invasive and can be monitored in real-time. Most of these studies were performed using a straight CO₂ pipeline. However, most of the pipelines using CO₂ transportation will have bends, joints, and valves, which may impact the strength of the AE signal. Also, the economic viability of using the AE for commercial implementation for pipeline monitoring in industries is not well understood. Therefore, to address these knowledge gaps, this paper focuses on understanding the economic viability through an experimental study. Experiments were conducted on a six-meter pipeline with joints, bends and valves. Static test experiments were conducted under different flow coefficients. Also, attempts were made in the study to identify the source localisation of the leak by adjusting the sensor positions. From the experiments conducted, the minimum leak that could be identified from the valve was 0.5 L/min and from the pipeline was 42 L/min. It was also possible to identify the leak location with good accuracy. From the study conducted and based on international prices of CO₂ gas in December 2025, we observed that the industry could save at least $225,482 USD annually by monitoring leaks from a single valve and approximately 18.9 million USD by monitoring leaks from pipelines using acoustic emission. This study lays a strong basis for industries to further investigate the commercial implementation of AE sensors for CO₂ pipelines.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01436-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189003","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}

{"title":"X-Ray-Based Lightweight High-Performance Non-Destructive Defect Detection Method for High-Voltage Cables","authors":"Junyu Chen,&nbsp;Beiping Hou,&nbsp;Jianwei Dong,&nbsp;Wen Zhu,&nbsp;Fangkun Shao,&nbsp;Jing Jie","doi":"10.1007/s10921-026-01434-7","DOIUrl":"10.1007/s10921-026-01434-7","url":null,"abstract":"<div><p>High-voltage cable defect detection is an important part of urban power system maintenance. Due to variations in cable thickness, large weight, and challenging operability, existing signal-processing and artificial-intelligence-based methods may face trade-offs among detection accuracy, cost, efficiency, and applicability. Moreover, some existing methods are invasive in nature. Therefore, this paper proposes an improved multiscale attention-based YOLOv8 model, designs a portable X-ray non-destructive testing (NDT) system, and creates a larger dataset based on the CycleGAN-based (Cycle Consistent Adversarial Networks-based) Pix2Pix (Pixel-to-Pixel Translation) model. First, the improved YOLOv8 model introduces EfficientNet to replace CSPNet (Cross Stage Partial Network) as the backbone network and incorporates a Squeeze-and-Excitation (SE) module after each convolutional block. Second, a bottom-up feature pyramid PAN (Path Aggregation Network) is integrated with the FPN (Feature Pyramid Network) to construct a hybrid multi-scale feature fusion structure, enabling multi-level and multi-directional information transfer. Moreover, a task-specific box refinement strategy combining K-means initialization with IoU-guided matching is employed to improve localization accuracy. The experimental results show that, compared with the other detection models, EMA-YOLOv8 improves the average precision and mAP50 by 4.68 and 3.9 percentage points, respectively, while reducing the average number of parameters and GFLOPs by 35.37% and 27.76%, respectively. Therefore, the proposed model has great potential.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189102","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}

{"title":"Research on Rotor Fault Identification Method Based on Multi-sensor AE Signal Fusion and SVD Orthogonal Mode Frequency Domain Fractal","authors":"Fangyu Chen,&nbsp;Zixuan Liu,&nbsp;Hongke Hu,&nbsp;Ming Li,&nbsp;Xinjun Zhu,&nbsp;Dan Jin,&nbsp;Dong Liu,&nbsp;Hangfeng Qiu,&nbsp;Shishang Dong","doi":"10.1007/s10921-026-01435-6","DOIUrl":"10.1007/s10921-026-01435-6","url":null,"abstract":"<div><p>This study addresses the dynamic identification of typical faults, such as rotor cracks and reversed installation of blades, in automotive power steering pumps by proposing a method for fault feature extraction and classification using multi-sensor acoustic emission (AE) signals. Initially, two AE sensors are positioned near the pump's inlet port and return port to synchronously capture AE signal time-domain waveforms under various working conditions, using a sampling frequency of 1 MHz. AE sub-signals for a single period are then extracted. After whitening, signals from the four sensors were arranged as column vectors to form a matrix. The singular value decomposition (SVD) was applied to extract four orthogonal mode vectors, which were then decomposed via three-layer wavelet packet decomposition (WPD). The frequency-domain fractal dimension (FFD) of the first four nodes is calculated. The random forest was used to assess feature importance and select the top six parameters. These features were fed into a support vector machine (SVM), and the performance of different kernel functions was compared. Experiments were conducted under pump body pressures of 4 MPa, 7.5 MPa, and 13 MPa, with a rotational speed of 1000 r/min. Results showed minimal differences among kernel functions, though radial basis function (RBF) performed best for the normal state. By incorporating SVD for multi-sensor information fusion and feature extraction, the classification accuracy of rotor states including normal, cracked, and reversed blade installation is improved from 87.77% to 97.55%, demonstrating the method's effectiveness and practicality.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132293","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}

{"title":"Comparative Study of Non-Destructive Techniques for Wood Quality Assessment: Acoustic Tomography versus CT Scanning","authors":"Miloš Gejdoš,&nbsp;Katarína Michajlová,&nbsp;Tomáš Gergeľ","doi":"10.1007/s10921-026-01432-9","DOIUrl":"10.1007/s10921-026-01432-9","url":null,"abstract":"<div><p>This study evaluates and compares advanced non‑destructive techniques for wood quality assessment, focusing on acoustic stress‑wave tomography and X‑ray computed tomography (CT), complemented by digital image analysis and manual measurements according to EN 1309‑3. Acoustic tomography was applied to a dataset of 165 standing trees (oak, lime, pine, fir) to identify spatial patterns of internal degradation, while CT scanning of a subset of 20 harvested logs was used for direct quantitative validation. Statistical analyses included Kruskal–Wallis tests, correlation analysis, and multiple regression models. Significant differences in defect proportion were found among species (H = 34.64; <i>p</i> &lt; 0.0001), together with a strong negative association between stem diameter and relative defect area (r ≈ − 0.67; p ≪ 0.001). In the CT‑validated subset, acoustic tomography showed a systematic but modest positive bias relative to CT (mean difference ≈ 4.1 percentage points), whereas digital photography consistently underestimated internal damage. Measurements according to EN 1309‑3 yielded generally conservative estimates, with occasional overestimation arising from geometric assumptions. Although the CT‑based validation is limited by the size of the harvested subset, the results confirm that CT provides the most accurate quantitative reference, while acoustic tomography is well suited for rapid in‑field screening and defect localization, particularly when supported by targeted verification methods (e.g. resistance drilling). The findings highlight the complementary roles of these techniques and support integrated workflows for practical wood quality assessment and decision‑making in forestry and timber processing.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132753","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}

{"title":"AI-Driven Bridge Crack Detection: A Review of Deep Learning Applications for Durability Assessment","authors":"Fangyuan Li,&nbsp;Md Sohel Rana","doi":"10.1007/s10921-026-01437-4","DOIUrl":"10.1007/s10921-026-01437-4","url":null,"abstract":"<div><p>Bridge crack detection is paramount for structural health monitoring and durability assessment, yet traditional methods remain labor-intensive, subjective, and limited in accuracy. This review comprehensively examines the paradigm shift toward artificial intelligence (AI), particularly deep learning (DL), in automating both crack detection and quantification for bridge infrastructure. We systematically analyze state-of-the-art DL methodologies, categorizing convolutional neural networks (CNNs) as the foundational backbone for specialized architectures, including ‘you only look once’ (YOLO) variants for detection, U-Net architectures for segmentation, and generative adversarial networks (GANs) for data augmentation. The review highlights their superior performance in accuracy (&gt; 95%), robustness to environmental variability, and real-time processing capabilities. Critical challenges such as data scarcity, annotation inconsistencies regarding crack severity, poor cross-environment generalization, computational constraints for edge deployment, and model interpretability gaps are rigorously evaluated. The paper also identifies key limitations in existing datasets, benchmarking practices, and regulatory integration. Finally, future directions are outlined with an emphasis on data-centric innovations, lightweight model optimization, explainable AI, and multimodal systems capable of predictive durability analysis. This synthesis underscores DL's transformative potential in enhancing bridge safety and calls for standardized frameworks to bridge the gap between research and real-world deployment.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132663","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}

{"title":"Investigation of Acoustic-emission-based Method for Weld Damage Monitoring in High Temperature Coke Drum","authors":"Zhihai Hu,&nbsp;Keqin Ding,&nbsp;Jianfang Zhou,&nbsp;Wei Xin,&nbsp;Li Chen,&nbsp;Song Liu,&nbsp;Guosong Wu","doi":"10.1007/s10921-026-01438-3","DOIUrl":"10.1007/s10921-026-01438-3","url":null,"abstract":"<div><p>The safety monitoring of welds on high-temperature equipment has long been a persistent challenge within the petrochemical industry. The coke drum is a critical component in coke production, enduring prolonged cyclic exposure to high temperatures and mechanical loads. This makes its weld zone particularly susceptible to cracking, posing a major threat to safe and stable operation. To address this, this paper introduces, for the first time, an acoustic emission (AE) based method for real-time online monitoring of weld damage in in-service high-temperature coke drums. Given the large data volume and low processing efficiency of conventional waveform analysis, a practical three-step feature value screening method rooted in AE feature analysis is proposed. This method, using amplitude as a representative single feature parameter, not only effectively identifies the presence of weld cracks but also enables approximate localization of longer cracks; however, its capability in detecting small cracks remains somewhat limited. Nevertheless, the method compresses the data volume to merely 1.1% of its original size, thereby significantly improving monitoring efficiency. The effectiveness of this AE-based monitoring approach was subsequently validated on the inner wall of a coke drum. Overall, this study provides a simple and efficient engineering solution for identifying weld damage in coke drums under high-temperature conditions, which holds considerable practical significance for advancing equipment safety monitoring.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132662","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}

{"title":"A Quantum Tunneling-Driven Enhancement Framework for Unifying Computed Radiography and Digital Radiography in Nondestructive Testing","authors":"Guancheng Lu,&nbsp;Guiqing Li,&nbsp;Jihua Guan,&nbsp;Jin Wei,&nbsp;Zhen Dong,&nbsp;Wei Wei","doi":"10.1007/s10921-026-01420-z","DOIUrl":"10.1007/s10921-026-01420-z","url":null,"abstract":"<div><p>Robust cross-modal enhancement between computed radiography (CR) and digital radiography (DR) is a persistent challenge in industrial nondestructive testing (NDT) because conventional methods inevitably sacrifice information during dynamic range adaptation, thereby compromising detection reliability. To overcome this limitation, a rigorous quantum–mechanical framework is established by drawing a formal analogy between image detail enhancement and quantum tunneling. Pixel intensity distributions are mapped onto quantum probability densities, and quantum potential barriers are systematically derived from normalized gradient tensor fields. This physically grounded methodology yields an enhancement operator derived entirely from first principles without relying on training data, seamlessly unifying CR and DR processing within a single paradigm. Experimental evaluation on 60 industrial X-ray images demonstrates that the proposed framework achieves a structural similarity index (SSIM) of 0.92 (CR) and 0.96 (DR), a peak signal-to-noise ratio (PSNR) of 28.39 dB (CR) and 38.80 dB (DR), and average relative improvements in spatial frequency (SF) of 32.3% and 18.4% for CR and DR cases, respectively. This study establishes a novel quantum–mechanical enhancement paradigm and provides a unified mathematical foundation for cross-modal radiographic processing. The demonstrated gains in defect detection sensitivity and cross-modal consistency underscore the potential of the proposed framework to transform computational imaging practices in NDT. The source code accompanying this study is available at the following persistent identifier: https://doi.org/10.5281/zenodo.21493535.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034940","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}

{"title":"Edge AI for Embedded Acoustic Emission Clustering: A Globally Optimal Sequential Mixture Model for Non-Destructive Structural Health Monitoring","authors":"Lina-Nadjeh Labar,&nbsp;Ikram Remadna,&nbsp;Oualid Laiadi,&nbsp;Noureddine Zerhouni","doi":"10.1007/s10921-026-01431-w","DOIUrl":"10.1007/s10921-026-01431-w","url":null,"abstract":"<div><p>Structural Health Monitoring (SHM) using Acoustic Emission (AE) as a Non-Destructive Testing modality requires continuous online classification of transient hits into damage regimes that appear sequentially as a structure degrades. The state-of-the-art GMMSEQ model couples a Gaussian mixture with time-varying sigmoid mixing weights, but its Expectation–Maximization (EM) procedure is initialized by a time-blind GMM, making it prone to local optima. We propose Dynamic-Programming GMMSEQ <b>DP-GMMSEQ</b>, a two-stage algorithm that replaces this initialization with a globally optimal hard-boundary segmentation obtained by dynamic programming (global optimality applies to this initialization stage), then refines it with standard GMMSEQ EM. On the public ORION-AE bolt-loosening benchmark, DP-GMMSEQ raises the Adjusted Rand Index (ARI) over the original GMMSEQ on four of five campaigns, with absolute gains up to <span>(14.9%)</span>. We further present the first end-to-end embedded AE pipeline on an STM32 microcontroller as a proof-of-concept foundational architecture, covering high-pass filtering, db45 wavelet denoising, hit detection, 32-feature extraction and on-device inference. The DP-GMMSEQ inference stage achieves a per-hit latency of 450 µs with an average performance drop of only <span>(sim !13,%)</span> across precision, recall, entropy and ARI compared to the desktop baseline. The dominant remaining bottleneck is the wavelet pre-processing front-end, not the statistical clustering itself, a finding that identifies the signal-processing stage as the key target for future real-time optimization.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034521","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}