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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/2512.24220 |
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| _version_ | 1866918266202488832 |
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| author | Shen, Li-Qun Wang, Hao-Jin Sun, Mengzhao Xiang, Yang Tian, Xin-Ning Chai, Yue Yang, Yue Ding, Feng Kong, Xiao Willinger, Marc-Georg Wang, Zhu-Jun |
| author_facet | Shen, Li-Qun Wang, Hao-Jin Sun, Mengzhao Xiang, Yang Tian, Xin-Ning Chai, Yue Yang, Yue Ding, Feng Kong, Xiao Willinger, Marc-Georg Wang, Zhu-Jun |
| contents | Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions Shen, Li-Qun Wang, Hao-Jin Sun, Mengzhao Xiang, Yang Tian, Xin-Ning Chai, Yue Yang, Yue Ding, Feng Kong, Xiao Willinger, Marc-Georg Wang, Zhu-Jun Mesoscale and Nanoscale Physics Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems. |
| title | Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2512.24220 |