Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Nat Commun. 2018 Jun 21;9(1):2428. doi: 10.1038/s41467-018-04887-1.
The key objective of scanning probe microscopy (SPM) techniques is the optimal representation of the nanoscale surface structure and functionality inferred from the dynamics of the cantilever. This is particularly pertinent today, as the SPM community has seen a rapidly growing trend towards simultaneous capture of multiple imaging channels and complex modes of operation involving high-dimensional information-rich datasets, bringing forward the challenges of visualization and analysis, particularly for cases where the underlying dynamic model is poorly understood. To meet this challenge, we present a data-driven approach, Graph-Bootstrapping, based on low-dimensional manifold learning of the full SPM spectra and demonstrate its successes for high-veracity mechanical mapping on a mixed polymer thin film and resolving irregular hydration structure of calcite at atomic resolution. Using the proposed methodology, we can efficiently reveal and hierarchically represent salient material features with rich local details, further enabling denoising, classification, and high-resolution functional imaging.
扫描探针显微镜（SPM）技术的主要目标是通过悬臂梁的动力学来最佳地表示纳米级表面结构和功能。如今，这一点尤为重要，因为 SPM 社区看到了一种快速增长的趋势，即同时捕获多个成像通道和涉及高维信息丰富数据集的复杂操作模式，这带来了可视化和分析的挑战，特别是在底层动态模型理解不佳的情况下。为了应对这一挑战，我们提出了一种基于全 SPM 谱的低维流形学习的数据驱动方法，Graph-Bootstrapping，并展示了其在混合聚合物薄膜的高真实性机械映射和解析方解石的不规则水合结构方面的成功。使用所提出的方法，我们可以有效地揭示和分层表示具有丰富局部细节的显著材料特征，进一步实现降噪、分类和高分辨率功能成像。
