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Unlocking the potential: analyzing 3D microstructure of small-scale cement samples from space using deep learning

Engineering and Technology

Unlocking the potential: analyzing 3D microstructure of small-scale cement samples from space using deep learning

V. Saseendran, N. Yamamoto, et al.

Discover groundbreaking research by Vishnu Saseendran, Namiko Yamamoto, Peter J. Collins, Aleksandra Radlińska, Sara Mueller, and Enrique M. Jackson on a novel deep learning method that reconstructs the 3D microstructure of cement samples studied in microgravity. The findings reveal unique characteristics of hydrated samples, with potential implications for material science and engineering.

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Playback language: English
Abstract
This paper presents a novel deep learning-based methodology for reconstructing the 3D microstructure of small-scale cement samples hydrated in microgravity on the International Space Station (ISS). Using sparse Scanning Electron Microscopy (SEM) data of tri-calcium silicate (C₃S) samples, the method generates a statistically representative ensemble of microstructures. Space-returned samples showed higher porosity (~70%) and elongated portlandite morphology. Reconstructed volumes, validated against micro-CT data, accurately captured the unique microstructural features, providing Representative Volume Elements (RVEs) for mechanical/transport property characterization.
Publisher
npj Microgravity
Published On
Jan 25, 2024
Authors
Vishnu Saseendran, Namiko Yamamoto, Peter J. Collins, Aleksandra Radlińska, Sara Mueller, Enrique M. Jackson
Tags
deep learning
3D microstructure
microgravity
cement samples
tri-calcium silicate
Scannning Electron Microscopy
porosity

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