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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08350 |
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| _version_ | 1866917399320592384 |
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| author | Guinan, Grace Smeaton, Michelle A. Wyatt, Brian C. Goldy, Steven Egan, Hilary Glaws, Andrew Tucker, Garritt J. Anasori, Babak Spurgeon, Steven R. |
| author_facet | Guinan, Grace Smeaton, Michelle A. Wyatt, Brian C. Goldy, Steven Egan, Hilary Glaws, Andrew Tucker, Garritt J. Anasori, Babak Spurgeon, Steven R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes Guinan, Grace Smeaton, Michelle A. Wyatt, Brian C. Goldy, Steven Egan, Hilary Glaws, Andrew Tucker, Garritt J. Anasori, Babak Spurgeon, Steven R. Materials Science Machine Learning 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. |
| title | Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2511.08350 |