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Chemical Imaging of Self-Assembled Monolayers on Copper Using Compressive Hyperspectral Sum Frequency Generation

Desheng Zheng1, Liyang Lu2, Kevin F Kelly2

  • 1Department of Chemistry, University of Houston , Houston, Texas 77204-5003, United States.

The Journal of Physical Chemistry. B
|August 11, 2017
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Summary

Compressive sensing hyperspectral sum frequency generation (SFG) microscopy enables label-free chemical imaging of patterned monolayers on copper surfaces. This technique effectively characterizes mixed-molecule domains with high spatial resolution.

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Area of Science:

  • Surface science
  • Chemical imaging
  • Optical microscopy

Background:

  • Sum frequency generation (SFG) microscopy is a label-free technique providing molecular vibrational contrast for surface analysis.
  • Hyperspectral SFG microscopy, enhanced by compressive sensing, offers potential for imaging molecular monolayers on metal surfaces with micrometer resolution.

Purpose of the Study:

  • To demonstrate the chemical imaging capability of compressive sensing broadband hyperspectral SFG microscopy.
  • To analyze spatially patterned monolayers of 1-octadecanethiol (ODT) and 16-methoxy-1-hexadecanethiol (MeOHT) on a copper surface.

Main Methods:

  • Compressive sensing broadband hyperspectral SFG microscopy was employed.
  • Data was reconstructed using a 3-dimensional total variation (3DTV) regularization algorithm.
  • Chemical imaging was performed using a compressive sensing-based endmember unmixing (CEU) scheme.

Main Results:

  • The spatial distributions of ODT and MeOHT monolayers were successfully visualized with vibrational-spectral contrast.
  • Both 3DTV and CEU algorithms reconstructed the hypercube data.
  • The CEU scheme provided superior spatial distribution maps of individual components and characterized mixed-molecule domains.

Conclusions:

  • Compressive sensing hyperspectral SFG microscopy is effective for label-free chemical imaging of molecular monolayers on metal surfaces.
  • The CEU scheme offers direct reconstruction of spatial-chemical distributions, enabling characterization of mixed-molecule domains.