| Autor: |
Xianhang Li, Shihao Zhou, Xuhao Liu, Jiadong Zang, Wenhao Fu, Wenlong Lu, Haibo Zhang, Zilin Yan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Heliyon, Vol 10, Iss 20, Pp e39185- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2024.e39185 |
| Popis: |
Accurate assessment of the three-dimensional (3D) pore characteristics within porous materials and devices holds significant importance. Compared to high-cost experimental approaches, this study introduces an alternative method: utilizing a generative adversarial network (GAN) to reconstruct a 3D pore microstructure. Unlike some existing GAN models that require 3D images as training data, the proposed model only requires a single cross-sectional image for 3D reconstruction. Using porous ceramic electrode materials as a case study, a comparison between the GAN-generated microstructures and those reconstructed through focused ion beam-scanning electron microscopy (FIB-SEM) reveals promising consistency. The GAN-based reconstruction technique demonstrates its effectiveness by successfully characterizing pore attributes in porous ceramics, with measurements of porosity, pore size, and tortuosity factor exhibiting notable agreement with the results obtained from mercury intrusion porosimetry. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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