Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes
This work addresses a fundamental problem in materials science for researchers and engineers by providing a generalizable framework to understand and control point defects in 2D materials, which is incremental as it builds on existing electron microscopy and AI methods.
The researchers tackled the challenge of resolving the three-dimensional arrangement of point defects in multi-layer two-dimensional materials, using an AI-guided electron microscopy workflow to map atomic vacancies in Ti3C2TX MXene, reconstructing 3D coordinates across hundreds of thousands of lattice sites and revealing defect structures from isolated vacancies to nanopores. This enabled classification of defect hierarchies and correlation with synthesis pathways, supported by molecular dynamics simulations.
Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti$_3$C$_2$T$_X$ MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures--from isolated vacancies to nanopores--revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.