{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:01:15Z","timestamp":1763542875821,"version":"3.45.0"},"reference-count":58,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IDUB AGH","award":["IDUB 15135","15137"],"award-info":[{"award-number":["IDUB 15135","15137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>The aim of this research is to develop and implement artificial intelligence models for the automatic detection of defects in the microstructures of austempered ductile iron (ADI). Our research used three different approaches, representing various categories of machine learning tasks: image classification (ResNet), pixel-wise segmentation (UNet), and object detection (YOLO). Each of the models were adapted to the specific characteristics of the dataset and tested on a collection of microstructural images prepared within the scope of the research. The data preparation process included clustering using the k-means method, morphological operations, generation of binary masks, conversion of labels into formats required by each architecture, and data augmentation to increase the diversity of training samples. The results demonstrated that ResNet achieved very high classification accuracy but did not provide spatial information about defect localization. UNet produced precise segmentation masks of martensitic regions, allowing for quantitative analysis of samples, although it required significantly higher computational resources and struggled with detecting very small defects. YOLO, in turn, enabled fast detection of defects in the form of bounding boxes. In summary, each model proved effective in a different context: ResNet for preliminary classification, UNet for detailed laboratory analysis, and YOLO for industrial detection tasks.<\/jats:p>","DOI":"10.3390\/ma18225207","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T08:50:07Z","timestamp":1763542207000},"page":"5207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9564-3001","authenticated-orcid":false,"given":"Sandra","family":"Gajoch","sequence":"first","affiliation":[{"name":"Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0477","authenticated-orcid":false,"given":"Dorota","family":"Wilk-Ko\u0142odziejczyk","sequence":"additional","affiliation":[{"name":"Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Krakow, Poland"},{"name":"Lukasiewicz Research Network-Krakow Institute of Technology, Zakopia\u0144ska 73, 30-418 Krak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7469-3057","authenticated-orcid":false,"given":"\u0141ukasz","family":"Marcjan","sequence":"additional","affiliation":[{"name":"Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8366-6059","authenticated-orcid":false,"given":"Roberto","family":"Corizzo","sequence":"additional","affiliation":[{"name":"Department of Computer Science (CAS), American University, Don Myers Technology & Innovation Building, 3501 Nebraska Avenue NW, Washington, DC 20016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2342-5023","authenticated-orcid":false,"given":"Adam","family":"Bitka","sequence":"additional","affiliation":[{"name":"Lukasiewicz Research Network-Krakow Institute of Technology, Zakopia\u0144ska 73, 30-418 Krak\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7484-1393","authenticated-orcid":false,"given":"Marcin","family":"Ma\u0142ysza","sequence":"additional","affiliation":[{"name":"Department of Applied Computer Science and Modelling, Faculty of Metals Engineering and Computer Science, AGH University of Krakow, al. 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Materials, 16.","DOI":"10.3390\/ma16114107"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Polishetty, A., Pan, B., Pasang, T., and Littlefair, G. (2010, January 12\u201315). Microstructural Study on Strain Induced Transformation in Austempered Ductile Iron Using Heat Tinting. Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference, Erie, PA, USA.","DOI":"10.1115\/MSEC2010-34085"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s11665-012-0233-0","article-title":"Effect of cooling rate on microstructure and mechanical properties of thin-walled ductile iron castings","volume":"22","year":"2013","journal-title":"J. Mater. Eng. Perform."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Silva, M.I., Malitckii, E., Santos, T.G., and Vila\u00e7a, P. (2023). Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects. Prog. Mater. Sci., 138.","DOI":"10.1016\/j.pmatsci.2023.101155"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jia, Y., Chen, G., and Zhao, L. (2024). Defect detection of photovoltaic modules based on improved Varifocal Net. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-66234-3"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhang, C., Ge, L., Chen, Z., Lu, K., and Wu, C. (2024). Joining Spatial Deformable Convolution and a Dense Feature Pyramid for Surface Defect Detection. IEEE Trans. Instrum. Meas., 73.","DOI":"10.1109\/TIM.2024.3370962"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1007\/s40747-024-01362-x","article-title":"TD-Net:tiny defect detection network for industrial products","volume":"10","author":"Shao","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dehaerne, E., Dey, B., Blanco, V., and Davis, J. (2025). Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: A systematic review. J. Micro\/Nanopatterning Mater. Metrol., 24.","DOI":"10.1117\/1.JMM.24.2.020901"},{"key":"ref_9","unstructured":"AI Image Evaluation, vs. (2025, October 23). Manual Microstructure Analysis\u2014Advantages and Disadvantages. Available online: https:\/\/mivia.ai\/en\/2024\/01\/ki-bildauswertung-vs-manuelle-mikrostrukturanalyse-vor-und-nachteile\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.matdes.2017.12.049","article-title":"New methods for automatic quantification of microstructural features using digital image processing","volume":"141","author":"Campbell","year":"2018","journal-title":"Mater. Des."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bai, J., Wu, D., Shelley, T., Schubel, P., Twine, D., Russell, J., Zeng, X., and Zhang, J. (2025). A Comprehensive Survey on Machine Learning Driven Material Defect Detection. ACM Comput. Surv., 57.","DOI":"10.1145\/3730576"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, Q., Liu, M., Wang, C., and Wu, Q.M.J. (2024). An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images. Sol. Energy, 267.","DOI":"10.1016\/j.solener.2023.112245"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, S., Xuan, J., and Shi, T. (2025). Biologically inspired compound defect detection using a spiking neural network with continuous time\u2013frequency gradients. Adv. Eng. Inform., 65.","DOI":"10.1016\/j.aei.2025.103132"},{"key":"ref_14","first-page":"137","article-title":"Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning. Smart Sustain","volume":"2","author":"Ferguson","year":"2018","journal-title":"Manuf. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Xuan, J., and Shi, T. (2024). An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment. Mech. Syst. Signal Process., 219.","DOI":"10.1016\/j.ymssp.2024.111596"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Peng, C., Peng, J., Wang, Z., Wang, Z., Chen, J., Xuan, J., and Shi, T. (2025). Adaptive fault diagnosis of railway vehicle on-board controller with large language models. Appl. Soft Comput., 16.","DOI":"10.1016\/j.asoc.2025.113919"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nowroth, C., Gu, T., Grajczak, J., Nothdurft, S., Twiefel, J., Hermsdorf, J., Kaierle, S., and Wallaschek, J. (2022). Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products. Appl. Sci., 12.","DOI":"10.3390\/app12094645"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, J., Shen, C., Huang, M., Qi, Y., Chai, Y., and Zheng, S. (2025). Deep learning accelerated micrograph-based porosity defect quantification in additively manufactured steels for uncovering a generic process-defect-properties relation. Mater. Charact., 225.","DOI":"10.1016\/j.matchar.2025.115094"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nithin, A.M., Perumal, M., Davidson, M.J., Srinivas, M., Rao, C.S.P., Harikrishna, K., Jagtap, J., Bhowmik, A., and Santhosh, A.J. (2025). Segmentation Studies on Al-Si-Mg Metallographic Images Using Various Different Deep Learning Algorithms and Loss Functions. Eng. Rep., 7.","DOI":"10.1002\/eng2.70119"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wei, X., Wang, C., and Xu, W. (2025). Physical Metallurgy-Guided Machine Learning for Strength-Plasticity Optimization in Aluminum Alloys. Mater. Today Commun., 49.","DOI":"10.1016\/j.mtcomm.2025.113970"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M., Stiefel, M., Bachmann, B.-I., Britz, D., and M\u00fccklich, F. (2024). Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures. Metals, 14.","DOI":"10.3390\/met14050553"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1017\/S1431927618015635","article-title":"High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel","volume":"25","author":"DeCost","year":"2019","journal-title":"Microsc. Microanal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, Y., Guo, X., Ling, X., and Geng, Q. (2025). Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-94936-9"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Biswas, M., Pramanik, R., Sen, S., Sinitca, A., Kaplun, D., and Sarkar, R. (2023). Microstructural segmentation using a union of attention guided U-Net models with different color transformed images. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-32318-9"},{"key":"ref_26","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_27","unstructured":"(2025, October 23). The Impact of AI on the Engineering Field|JHU EP. Available online: https:\/\/ep.jhu.edu\/news\/the-impact-of-ai-on-the-engineering-field\/."},{"key":"ref_28","unstructured":"(2025, October 23). What Is Machine Learning? Definition, Types, Tools & More|DataCamp. Available online: https:\/\/www.datacamp.com\/blog\/what-is-machine-learning."},{"key":"ref_29","unstructured":"(2025, October 23). An Overview of Deep Learning for Image Processing\u2014Christian Garbin\u2019s Personal Blog. Available online: https:\/\/cgarbin.github.io\/deep-learning-for-image-processing-overview\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ge, M., Su, F., Zhao, Z., and Su, D. (2020). Deep learning analysis on microscopic imaging in materials science. Mater Today Nano, 11.","DOI":"10.1016\/j.mtnano.2020.100087"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1007\/s11666-024-01736-0","article-title":"Microstructural Feature Extraction by a Convolutional Neural Network for Cold Spray of Aluminum Alloys","volume":"33","author":"Tu","year":"2024","journal-title":"J. Therm. Spray Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"87","DOI":"10.15408\/jti.v18i1.44147","article-title":"Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification","volume":"18","author":"Gea","year":"2025","journal-title":"J. Tek. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, Y., Li, R., Yang, S., Chen, Q., Wang, B., and Liu, Y. (2022). Center-environment feature models for materials image segmentation based on machine learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-16824-w"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wankhade, N., Sale, V., Yadav, R., Jikar, P., Gadgekar, S., and Dhokey, N. (2024). Metallurgical microstructure classification using CNN: A comprehensive study on heat treatment analysis for steel. Mater. Today Proc., in press.","DOI":"10.1016\/j.matpr.2024.05.066"},{"key":"ref_35","unstructured":"(2025, October 23). Convolutional Neural Networks: A Complete Guide|Medium. Available online: https:\/\/medium.com\/@alejandro.itoaramendia\/convolutional-neural-networks-cnns-a-complete-guide-a803534a1930."},{"key":"ref_36","unstructured":"(2025, October 23). Convolutional Neural Networks (CNNs) and Layer Types\u2014PyImageSearch. Available online: https:\/\/pyimagesearch.com\/2021\/05\/14\/convolutional-neural-networks-cnns-and-layer-types\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4421","DOI":"10.1140\/epjs\/s11734-025-01698-6","article-title":"Towards advanced brain tumor segmentation: A novel hybrid architecture integrating UNet, FCN, and YOLO models on the newly introduced BTS-DS 2024 dataset","volume":"234","author":"Eker","year":"2025","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"943","DOI":"10.22214\/ijraset.2022.47789","article-title":"An Introduction to Convolutional Neural Networks","volume":"10","author":"Saxena","year":"2022","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C.W., Choudhary, A., Agrawal, A., and Billinge, S.J.L. (2022). Recent advances and applications of deep learning methods in materials science. NPJ Comput. Mater., 8.","DOI":"10.1038\/s41524-022-00734-6"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity mappings in deep residual networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands. 9908 LNCS.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gebhardt, C., Trimborn, T., Weber, F., Bezold, A., Broeckmann, C., and Herty, M. (2020). Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship. Mech. Mater., 151.","DOI":"10.1016\/j.mechmat.2020.103625"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Medghalchi, S., Kortmann, J., Lee, S.-H., Karimi, E., Kerzel, U., and Korte-Kerzel, S. (2024). Automated segmentation of large image datasets using artificial intelligence for microstructure characterisation and damage analysis. Mater. Des., 243.","DOI":"10.1016\/j.matdes.2024.113031"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Mach","volume":"5","author":"Terven","year":"2023","journal-title":"Learn. Knowl. Extr."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Salvi, M., Acharya, U.R., Molinari, F., and Meiburger, K.M. (2021). The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Comput. Biol. Med., 128.","DOI":"10.1016\/j.compbiomed.2020.104129"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.inffus.2021.09.018","article-title":"A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges","volume":"78","author":"Luengo","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","article-title":"Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm","volume":"54","author":"Dhanachandra","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., and Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP J. Wirel. Commun. Netw., 2021.","DOI":"10.1186\/s13638-021-01910-w"},{"key":"ref_49","first-page":"161","article-title":"Morphological image processing","volume":"2","author":"Goyal","year":"2011","journal-title":"Int. J. Comput. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.matchar.2018.08.009","article-title":"A new analysis approach based on Haralick texture features for the characterization of microstructure on the example of low-alloy steels","volume":"144","author":"Webel","year":"2018","journal-title":"Mater. Charact."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L. (2021). A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel. Mathematics, 9.","DOI":"10.3390\/math9192359"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1080\/08839514.2021.1975391","article-title":"Deep Learning Based Steel Pipe Weld Defect Detection","volume":"35","author":"Yang","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ma, B., Wei, X., Liu, C., Ban, X., Huang, H., Wang, H., Xue, W., Wu, S., Gao, M., and Shen, Q. (2020). Data augmentation in microscopic images for material data mining. NPJ Comput. Mater., 6.","DOI":"10.1038\/s41524-020-00392-6"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ma, J., Hu, C., Zhou, P., Jin, F., Wang, X., and Huang, H. (2023). Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation. Appl. Sci., 13.","DOI":"10.3390\/app13116478"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, W., Yang, L., Peng, G., Pang, G., Yu, Z., and Zhu, X. (2025). An effective microscopic image augmentation approach. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-93954-x"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54569\/aair.1016544","article-title":"Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network","volume":"2","author":"Isik","year":"2022","journal-title":"Adv. Artif. Intell. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1007\/s10462-021-10082-4","article-title":"A comprehensive review of image analysis methods for microorganism counting: From classical image processing to deep learning approaches","volume":"55","author":"Zhang","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Kato, S., and Hotta, K. (2021). Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation. arXiv.","DOI":"10.1109\/SMC52423.2021.9658801"}],"container-title":["Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1944\/18\/22\/5207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T08:58:39Z","timestamp":1763542719000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1944\/18\/22\/5207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"references-count":58,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ma18225207"],"URL":"https:\/\/doi.org\/10.3390\/ma18225207","relation":{},"ISSN":["1996-1944"],"issn-type":[{"value":"1996-1944","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]}}}