Locating, characterizing, and understanding
the microstructural
nanomodifications of random heterogeneous materials are critical to
improve their performance, but current methods are limited by the
material’s highly disordered microstructure. Here, we proposed
a scheme to reveal the hidden microstructural modifications by coupling
large-scale nanoporosity mappings with deep learning and demonstrated
its effectiveness on nanomaterial (graphene oxide, GO)-modified cement.
This deep learning-based approach showed superior abilities in distinguishing
the nanomodified samples. The deep learning-approach shows a classification
accuracy of 88.8% using nanoporosity mapping with micro-scale features.
When nano-scale features were included, the classification accuracy
further increased to 98%, indicating the GO modifications in the nano-scale.
The microstructural changes in nano and micro scales were also located,
providing the first direct evidence of a localized modification effect
of GO where a small proportion (3.6–5.4%) of characteristic
regions contribute up to 70% of total relevance. The effect of GO
on spatial heterogeneity was also revealed by the changes in characteristic
lengths. Besides, the reduced fraction of shared pore structural patterns
from 44.6 to 40.9% under a high dosage of GO (0.02 wt %) also indicated
the nanomodification effect of GO in improving structural topological
efficiency. This study not only provides insights into the GO-modified
cement for structural applications but also promotes the development
of other random heterogeneous materials for a wide range of applications
such as load-bearing, thermal and electrical insulation, catalyst
support, and so on.