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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.15772 |
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| _version_ | 1866914206330126336 |
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| author | Tang, Binze Lo, Chon-Hei Liang, Tiancheng Hong, Jiani Qin, Mian Song, Yizhi Cao, Duanyun Jiang, Ying Xu, Limei |
| author_facet | Tang, Binze Lo, Chon-Hei Liang, Tiancheng Hong, Jiani Qin, Mian Song, Yizhi Cao, Duanyun Jiang, Ying Xu, Limei |
| contents | Premelting plays a key role across physics, chemistry, materials and biology sciences but remains poorly understood at the atomic level due to surface characterization limitations. We report the discovery of a novel amorphous ice layer (AIL) preceding the quasi-liquid layer (QLL) during ice premelting, enabled by a machine learning framework integrating atomic force microscopy (AFM) with molecular dynamics simulations. This approach overcomes AFM's depth and signal limitations, allowing for three-dimensional surface structure reconstruction from AFM images. It further enables structural exploration of premelting interfaces across a wide temperature range that are experimentally inaccessible. We identify the AIL, present between 121-180K, displaying disordered two-dimensional hydrogen-bond network with solid-like dynamics. Our findings refine the ice premelting phase diagram and offering new insights into the surface growth dynamic, dissolution and interfacial chemical reactivity. Methodologically, this work establishes a novel framework for AFM-based 3D structural discovery, marking a significant leap in our ability to probe complex disordered interfaces with unprecedented precision and paving the way for future disciplinary research, including surface reconstruction, crystallization, ion solvation, and biomolecular recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling the amorphous ice layer during premelting using AFM integrating machine learning Tang, Binze Lo, Chon-Hei Liang, Tiancheng Hong, Jiani Qin, Mian Song, Yizhi Cao, Duanyun Jiang, Ying Xu, Limei Materials Science Mesoscale and Nanoscale Physics Chemical Physics Computational Physics Premelting plays a key role across physics, chemistry, materials and biology sciences but remains poorly understood at the atomic level due to surface characterization limitations. We report the discovery of a novel amorphous ice layer (AIL) preceding the quasi-liquid layer (QLL) during ice premelting, enabled by a machine learning framework integrating atomic force microscopy (AFM) with molecular dynamics simulations. This approach overcomes AFM's depth and signal limitations, allowing for three-dimensional surface structure reconstruction from AFM images. It further enables structural exploration of premelting interfaces across a wide temperature range that are experimentally inaccessible. We identify the AIL, present between 121-180K, displaying disordered two-dimensional hydrogen-bond network with solid-like dynamics. Our findings refine the ice premelting phase diagram and offering new insights into the surface growth dynamic, dissolution and interfacial chemical reactivity. Methodologically, this work establishes a novel framework for AFM-based 3D structural discovery, marking a significant leap in our ability to probe complex disordered interfaces with unprecedented precision and paving the way for future disciplinary research, including surface reconstruction, crystallization, ion solvation, and biomolecular recognition. |
| title | Unveiling the amorphous ice layer during premelting using AFM integrating machine learning |
| topic | Materials Science Mesoscale and Nanoscale Physics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2512.15772 |