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

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. Advanced multivariate analysis can dramatically improve the chemical sensing capabilities of tip-enhanced Raman spectroscopy (TERS). A powerful surface analysis technique, TERS is hampered by inherently weak Raman signals, which have not permitted complex molecular architectures to be distinguished at the subnanometer scale. By employing advanced multivariate analysis, Song Jiang and co-workers from the University of Science and Technology of China used TERS to unambiguously identify adjacent molecules on a surface. The scheme’s spatial resolution of just 0.4 nanometers opens up the possibility of gathering information on a submolecular scale, such as mapping local vibrations and structural units within a single molecule. Experiments with a mixture of ZnTPP and H2TBPP molecules adsorbed on the edge of a silver step confirmed the advantages of the multivariate approach over conventional single-peak spectral analysis.