Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks

Recent release of open-source machine learning libraries presents opportunities to unify machine learning with nanoscale research, thus improving effectiveness of research methods and characterization protocols. This paper outlines and demonstrates the effectiveness of such a synergy with artificial neural networks to provide for an accelerated and enhanced characterization of individual carbon nanotubes deposited over a surface. Our algorithms provide a rapid diagnosis and analysis of tip-enhanced Raman spectroscopy mappings and the results show an improved spectral assignment of spectral features and spatial contrast of the collected images. Using several examples, we demonstrate the robustness and versatility of our deep learning neural network models. We highlight the use of machine learning and data science in tandem with tip-enhanced Raman spectroscopy technique enables a fast and accurate understanding of experimental data, thus leading to a powerful and comprehensive imaging analysis applied to spectroscopic measurements.

Tip-enhanced Raman spectroscopy of graphene-like and graphitic platelets on ultraflat gold nanoplates

TERS was used to investigate the graphene-like platelets in gap mode geometry using radially and linearly polarized excitation.