npj: 全色锐化算法—搞定AFM-IR相关多模态成像

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原子力显微镜与红外光谱（AFM-IR）的耦合提供了独特的能力，可对各种材料的局部化学和物理组成作纳米分辨的表征。然而，为了充分利用AFM-IR的测量能力，需要取得3D数据集（具有光谱维度的2D图），常规AFM扫描要达到相同空间分辨率会非常耗时。

来自美国橡树岭国家实验室纳米材料科学中心的Olga S. Ovchinnikova教授，应用AFM-IR数据的耦合非负矩阵分解（CNMF）全色锐化（PS）算法，实现了高空间分辨率、高光谱化学成像数据的快速重建。该方法仅需要低空间分辨率光谱和有限数量的高空间分辨率单波数化学图，即可产生高空间分辨率的高光谱图像，可极大地减少数据采集时间。基于这一方法，他们能够获得高分辨率的成分分布图，在光谱范围内的任何波数处生成化学图，并可对样品的物理和化学性质进行相关分析。最后，该研究展示了全色锐化-非负矩阵分解算法在植物细胞壁相关分析中的应用，确定了局部力学性质与化学成分之间的关系。该研究证明了全色锐化算法在恢复全光谱和全空间分辨率AFM-IR数据集中的适用性。使用这些方法即可借助其他光谱成像技术很容易地实现复杂材料的化学成像，有可能更深入地研究纳米尺度的结构-性能关系。

该文近期发表于npj Computational Materials 5: 49 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR

Nikolay Borodinov, Natasha Bilkey, Marcus Foston, Anton V. Ievlev, Alex Belianinov, Stephen Jesse, Rama K. Vasudevan, Sergei V. Kalinin & Olga S. Ovchinnikova 

The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.

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