High-throughput defect detection and coverage quantification of graphene grids via a dual-stage deep learning framework

Today's article comes from the NPJ journal of Computational Materials . The authors are Mao et al., from Zhejiang University, in China. In this paper they're showcasing a dual-stage deep learning framework that takes in raw images of graphene grids, identifies defects, and quantifies graphene coverage.

DOI: 10.1038/s41524-025-01870-5