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.

A π-conjugated inorganic polymer constructed from boron difluoride formazanates and platinum(ii) diynes.

The first example of a π-conjugated polymer incorporating boron difluoride (BF2) formazanates is introduced. The film-forming properties, controllable reduction chemistry, and low optical band gap (ca. 1.4 eV) of the polymer make it an excellent candidate for use as a light-harvesting n-type semiconductor in organic electronics. Comparison of the polymer to model compounds confirmed that its unique optoelectronic properties can be directly attributed to the presence of the BF2 formazanate repeat unit and that the [Pt(PBu3)2]2+ unit must also be present to achieve the narrow band gaps observed.