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Subnanometer-resolved chemical imaging via multivariate analysis of tip-enhanced Raman maps.

Song JiangXianbiao ZhangYao ZhangChunrui HuRui ZhangYang ZhangYuan LiaoZachary J SmithZhenchao DongJ G Hou
Published in: Light, science & applications (2017)
Tip-enhanced Raman spectroscopy (TERS) is a powerful surface analysis technique that can provide subnanometer-resolved images of nanostructures with site-specific chemical fingerprints. However, due to the limitation of weak Raman signals and the resultant difficulty in achieving TERS imaging with good signal-to-noise ratios (SNRs), the conventional single-peak analysis is unsuitable for distinguishing complex molecular architectures at the subnanometer scale. Here we demonstrate that the combination of subnanometer-resolved TERS imaging and advanced multivariate analysis can provide an unbiased panoramic view of the chemical identity and spatial distribution of different molecules on surfaces, yielding high-quality chemical images despite limited SNRs in individual pixel-level spectra. This methodology allows us to exploit the full power of TERS imaging and unambiguously distinguish between adjacent molecules with a resolution of ~0.4 nm, as well as to resolve submolecular features and the differences in molecular adsorption configurations. Our results provide a promising methodology that promotes TERS imaging as a routine analytical technique for the analysis of complex nanostructures on surfaces.
Keyphrases
  • high resolution
  • raman spectroscopy
  • deep learning
  • machine learning
  • escherichia coli
  • staphylococcus aureus
  • convolutional neural network
  • mass spectrometry
  • photodynamic therapy
  • clinical practice