论文标题
通过神经网络辅助原子层析成像来确定3维表面原子结构的单原子水平
Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography
论文作者
论文摘要
纳米材料的功能特性在很大程度上取决于其表面原子结构，但它们通常与其散装结构有很大不同，表现出表面重建和弛豫。但是，大多数表面表征方法要么限制为二维测量值，要么没有达到真正的3D原子尺度分辨率，并且对于一般3D纳米材料的3D表面原子结构的单原子水平测定仍然难以捉摸。在这里，我们在15 pm的精度下显示了PT纳米颗粒的3D原子结构的测量，并在基于深度学习的数据检索的帮助下进行了帮助。表面原子结构可靠地测量，我们发现<100>和<111>方面对表面应变有不同的贡献，从而导致各向异性应变分布以及压缩支撑边界效应。单原子水平表面表征的能力不仅会加深我们对纳米材料功能性能的理解，而且还为其性能的精细剪裁打开了新的门。
Functional properties of nanomaterials strongly depend on their surface atomic structure, but they often become largely different from their bulk structure, exhibiting surface reconstructions and relaxations. However, most of the surface characterization methods are either limited to 2-dimensional measurements or not reaching to true 3D atomic-scale resolution, and single-atom level determination of the 3D surface atomic structure for general 3D nanomaterials still remains elusive. Here we show the measurement of 3D atomic structure of a Pt nanoparticle at 15 pm precision, aided by a deep learning-based missing data retrieval. The surface atomic structure was reliably measured, and we find that <100> and <111> facets contribute differently to the surface strain, resulting in anisotropic strain distribution as well as compressive support boundary effect. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance.
