Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning

DOI: 10.1107/S205225252300204X

Authors: Minghui Sun (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Zheng Dong (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Liyuan Wu (nstitute of High Energy Physics, Chinese Academy of Sciences) , Haodong Yao (nstitute of High Energy Physics, Chinese Academy of Sciences) , Wenchao Niu (nstitute of High Energy Physics, Chinese Academy of Sciences) , Deting Xu (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Ping Chen (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Himadri S. Gupta (Queen Mary University of London) , Yi Zhang (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Yuhui Dong (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences) , Chunying Chen (University of Chinese Academy of Sciences; National Center for Nanoscience and Technology of China) , Lina Zhao (Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Iucrj , VOL 10

State: Published (Approved)
Published: May 2023

Abstract: 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.

Journal Keywords: machine learning; synchrotron microfocus X-ray diffraction; biological materials; nanofiber networks

Subject Areas: Materials, Information and Communication Technology


Instruments: I22-Small angle scattering & Diffraction

Other Facilities: BL10U1, BL19U2 at SSRF

Added On: 29/03/2023 15:20

Documents:
ro5032.pdf

Discipline Tags:

Biomaterials Artificial Intelligence Information & Communication Technologies Materials Science Data processing

Technical Tags:

Scattering Wide Angle X-ray Scattering (WAXS)