From micro- to nano- and time-resolved x-ray computed tomography: Bio-based applications, synchrotron capabilities, and data-driven processing

Author(s)

Claro, Pedro I. C. ; Borges, Egon P. B. S. ; Schleder, Gabriel R. R. ; Archilha, Nathaly L. L. ; Pinto, Allan ; Carvalho, Murilo ; Driemeier, Carlos E. E. ; Fazzio, Adalberto ; Gouveia, Rubia F. F.

• Document type: Journal article
• Source: APPLIED PHYSICS REVIEWS; v. 10, n. 2, p. 19-pg., 2023-06-01.

Abstract

X-ray computed microtomography (mu CT) is an innovative and nondestructive versatile technique that has been used extensively to investigate bio-based systems in multiple application areas. Emerging progress in this field has brought countless studies using lCT characterization, revealing three-dimensional (3D) material structures and quantifying features such as defects, pores, secondary phases, filler dispersions, and internal interfaces. Recently, x-ray computed tomography (CT) beamlines coupled to synchrotron light sources have also enabled computed nanotomography (nCT) and four-dimensional (4D) characterization, allowing in situ, in vivo, and in operando characterization from the micro- to nanostructure. This increase in temporal and spatial resolutions produces a deluge of data to be processed, including real-time processing, to provide feedback during experiments. To overcome this issue, deep learning techniques have risen as a powerful tool that permits the automation of large amounts of data processing, availing the maximum beamline capabilities. In this context, this review outlines applications, synchrotron capabilities, and data-driven processing, focusing on the urgency of combining computational tools with experimental data. We bring a recent overview on this topic to researchers and professionals working not only in this and related areas but also to readers starting their contact with x-ray CT techniques and deep learning. (C) 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). (AU)

• FAPESP's process: 21/03097-1 - Morphological and morphometric investigation of porous nanocomposites using computational and segmentation tools
• Grantee: Égon Piragibe Barros Silva Borges
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 17/18139-6 - Machine learning for Materials Science: 2D materials discovery and design
• Grantee: Gabriel Ravanhani Schleder
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties
• Grantee: Adalberto Fazzio
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/50884-5 - INCT 2014: National Institute of Science and Technology of Bioethanol
• Grantee: Marcos Silveira Buckeridge
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/16453-8 - Morphogenetic analysis of the infection mechanisms of the USUV and its effects on neurogenesis in murine model
• Grantee: Murilo de Carvalho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/08651-4 - Morphological and physico-chemical properties nanocellulose-based porous nanocomposites: an advanced in situ investigation by 4D X-ray tomography using synchrotron beamline at Sirius
• Grantee: Rubia Figueredo Gouveia
• Support Opportunities: Program for Research on Bioenergy (BIOEN) - Regular Program Grants