[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123134-en":3,"doc-seo-123134-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123134,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning","Structural disclosure of biological materials supports understanding of natural design principles and enables progress toward artificial materials. Synchrotron microfocus X-ray diffraction is a key tool for characterizing hierarchically structured biological systems, especially the 3D orientation distribution of interpenetrating nanofiber networks. Current iterative parametric fitting is slow, expert-dependent, and sensitive to initial parameters, limiting real-time analysis for high-throughput experiments. A machine-learning workflow predicts fast fiber-orientation metrics from micro-focused WAXD data, using corrupted simulated training and label transformation to address discontinuities in angle outputs.","research papers  \n\n| IUCrJ\u003Cbr>ISSN 2052-2525\u003Cbr>NEUTRON j SYNCHROTRON\u003Cbr>Received 27 May 2022\u003Cbr>Accepted 3 March 2023\u003Cbr>Edited by I. Robinson, UCL, United Kingdom\u003Cbr>‡ These authors contributed equally to this work.\u003Cbr>Keywords: machine learning; synchrotron microfocus X-ray diffraction; biological materials; nanofiber networks.\u003Cbr>Supporting information: this article has\u003Cbr>supporting information [at www.iucrj.org](at www.iucrj.org)\u003Cbr>\u003Cbr>Published under a CC BY 4.0 licence | Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning\u003Cbr>Minghui Sun,a,b‡ Zheng Dong,a,b‡ Liyuan Wu,a Haodong Yao,a Wenchao Niu,a Deting Xu,a,b Ping Chen,a,b Himadri S. Gupta,c Yi Zhang,a,b* Yuhui Dong,a,b Chunying Chenb,d and Lina Zhaoa,b*\u003Cbr>aMultidisciplinary Initiative Center, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People’s Republic of China, bUniversity of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China, cSchool of Engineering and Material Science, Queen Mary University of London, London E1 4NS, United Kingdom, and dNational Center for Nanoscience and Technology of China, Beijing 10084, People’s Republic of China .\u003Cbr>*Correspondence e-mail: [zhangyi88@ihep.ac.cn](zhangyi88@ihep.ac.cn), [linazhao@ihep.ac.cn](linazhao@ihep.ac.cn)\u003Cbr>Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials.\u003Cbr>1. Introduction\u003Cbr>Many biological, bioinspired and synthetic materials exhibit 3D networks of textured nanofibers, especially for highstrength and multifunctional materials containing nanofibrillar constituents (Ma et al., 2020; Zhang et al., 2014, 2021;\u003Cbr>Kargarzadeh et al., 2017; Peng et al., 2020; Meyers et al., 2008) . Their key functionality and properties are closely related with nanofiber orientation (Mittal et al., 2018; Meyers et al., 2008) . The accurate and fast characterization of nanofiber orientation will help to reveal important structural information, elucidate the relationship between structure and property, and thereby provide a way for material modification (Li et al., 2015) and bioinspired material design (Amorim et al., 2021) . Synchrotron small-angle X-ray scattering (SAXS) and wide-angle X-ray diffraction (WAXD) methods are widely employed to rapidly and non-destructively extract the orientation distribution information of nanofiber-based composites. Though the texture information of 2D-layered nanocomposites can be directly acquired by fitting the SAXS/WAXD peaks, the determination of the orie","cbCaivboadfFzGYA","https://ap.wps.com/l/cbCaivboadfFzGYA","pdf",2022748,1,12,"English","en",105,"# 1. Introduction\n## Motivation and limitations of iterative fitting\n## Need for real-time analysis in high-throughput experiments\n## Role of SAXS/WAXD and tomography in multiscale characterization\n## Overview of the proposed machine-learning approach","[{\"question\":\"Why is extracting 3D nanofiber orientation from WAXD patterns challenging?\",\"answer\":\"Extraction is commonly done via iterative parametric fitting, which is time-consuming, requires expertise and good initial parameter estimates, and can vary across analyzers.\"},{\"question\":\"What is the main idea of the proposed method?\",\"answer\":\"The method uses machine learning to predict three-dimensional fiber orientation metrics directly from synchrotron micro-focused diffraction data under a two-group nanofiber assumption for the illuminated volume.\"},{\"question\":\"How does the work improve robustness for real experimental data and angle prediction?\",\"answer\":\"Training uses corrupted simulated data to better match real-world experimental conditions, and label transformation is applied to resolve jump discontinuities when predicting angle parameters.\"}]","Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning | 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is extracting 3D nanofiber orientation from WAXD patterns challenging?","Question",{"text":75,"@type":76},"Extraction is commonly done via iterative parametric fitting, which is time-consuming, requires expertise and good initial parameter estimates, and can vary across analyzers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main idea of the proposed method?",{"text":80,"@type":76},"The method uses machine learning to predict three-dimensional fiber orientation metrics directly from synchrotron micro-focused diffraction data under a two-group nanofiber assumption for the illuminated volume.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the work improve robustness for real experimental data and angle prediction?",{"text":84,"@type":76},"Training uses corrupted simulated data to better match real-world experimental conditions, and label transformation is applied to resolve jump discontinuities when predicting angle 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