Journal article
Porosity-Modulus Mapping enhanced nanomechanical analysis of heterogeneous materials
Journal of materials science, Vol.58(24), pp.10058-10072
06/2023
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Abstract
Nanomechanical characterisation of heterogeneous material with nanoscale interfaces is challenging due to scale limitations. Here, we introduce a characterisation technique, called porosity-modulus mapping (PMM), to characterise the nanomechanical properties of cement composites with improved characterisation of the interfaces. The technique is developed and calibrated based on a cement mortar sample containing fly ash. The mapping process has three steps: deep learning (DL)-based segmentation, nanoporosity transformation and nanomodulus mapping. To establish the link between microstructure and mechanical properties, a transforming agent is introduced, called equivalent porosity, which transforms the microstructural signals [i.e. backscattered electrons (BSE)] into porosities. The relationship between the equivalent porosity and BSE signals is determined by performing a Monte Carlo simulation, and that between the porosity and the mechanical properties is defined by a theoretical relationship. Analysis showed that the method can characterise the material not only at the nanoscale but also on a large surface area with excellent characterisation of the interfacial transition zones (ITZs). Furthermore, the mapping results can predict the engineering modulus. Finally, the PMM method was applied to analyse the nanomodification of nano-reinforced cement composites developed in a literature. The proposed technique can open a pathway for developing a microstructure-based material design.
Details
- Title
- Porosity-Modulus Mapping enhanced nanomechanical analysis of heterogeneous materials
- Creators
- Hoan Nguyen - Monash UniversityWei Wang - Monash UniversityXupei Yao - Monash UniversityKwesi Sagoe-Crentsil - Monash UniversityWenhui Duan - Monash University
- Publication Details
- Journal of materials science, Vol.58(24), pp.10058-10072
- Publisher
- Springer Nature
- Identifiers
- 991013171713702368
- Copyright
- (c) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article