Nanoscale bond-selective imaging by computational fusion of atomic force microscopy and coherent anti-Stokes Raman scattering microscopy.
Nanoscale bond-selective imaging by computational fusion of atomic force microscopy and coherent anti-Stokes Raman scattering microscopy.
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DOI：
10.1039/d3an00662j
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发表时间：
2023-06
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影响因子：
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通讯作者：
Le Wang;Ji-Xin Cheng
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作者：
Le Wang;Ji-Xin Cheng
基于相干拉曼散射的振动显微镜是高速化学成像的强大工具，但其横向分辨率受到光学衍射极限的限制。另一方面，原子力显微镜（AFM）提供纳米级空间分辨率，但化学特异性较低。在这项研究中，我们利用一种称为全色锐化的计算方法来合并 AFM 形貌图像和相干反斯托克斯拉曼散射 (CARS) 图像。该混合系统结合了两种模式的优点，提供信息丰富的化学图谱，空间分辨率约为 20 nm。 CARS 和 AFM 图像在单个多模态平台上依次采集，这有利于图像共定位。我们的图像融合方法可以识别以前由于衍射极限而看不见的合并相邻特征，并通过 AFM 图像的输入识别微妙的不可观察的结构。与尖端增强的 CARS 测量相比，连续采集 CARS 和 AFM 图像可以使用更高的激光功率，并避免入射激光束造成的任何尖端损坏，从而显着提高 CARS 图像质量。总之，我们的工作提出了通过计算方法实现材料的超分辨率相干拉曼散射成像的新方向。
Vibrational microscopy based on coherent Raman scattering is a powerful tool for high-speed chemical imaging, but its lateral resolution is bound to the optical diffraction limit. On the other hand, atomic force microscopy (AFM) provides nano-scale spatial resolution, yet with lower chemical specificity. In this study, we leverage a computational approach called pan-sharpening to merge AFM topography images and coherent anti-Stokes Raman scattering (CARS) images. The hybrid system combines the advantages of both modalities, providing informative chemical mapping with ∼20 nm spatial resolution. CARS and AFM images were sequentially acquired on a single multimodal platform, which facilitates image co-localization. Our image fusion approach allowed for discerning merged neighboring features previously invisible due to the diffraction limit and identifying subtle unobservable structures with the input from AFM images. Compared to tip-enhanced CARS measurement, sequential acquisition of CARS and AFM images enables higher laser power to be used and avoids any tip damage caused by the incident laser beams, resulting in a significantly improved CARS image quality. Together, our work suggests a new direction for achieving super-resolution coherent Raman scattering imaging of materials through a computational approach.