Sum frequency generation imaging microscopy (SFG-IM) is a unique surface-specific technique that can detect the spatial distributions of differing monolayer species based on chemical contrast. The SFG images of microcontact-patterned multi-component self-assembled monolayers derived by the adsorption of alkanethiols on gold were analyzed by factor analysis (FA) utilizing a library consisting of SFG alkanethiol spectra to determine the chemical identity and spatial distribution of the patterned monolayers across the images. The monolayer species were correctly identified using the spectral library as a target, and their spatial distributions mapped for factor analysis. A random-pattern multi-component alkanethiol sample's chemical identity and spatial distribution were determined and mapped. Furthermore, utilizing the alkanethiol library, factor analysis was able to identify an unknown monolayer region, vibrational spectra of which were not present in the target library. The results demonstrate the capability of FA combined with the spectral library to determine the chemical composition and spatial distribution of organic molecules on the surface of multi-component complex chemical systems acquired by SFG-IM. A second approach to solve the chemical identification of the pixel spectra through classification is machine learning. The method shows high accuracy even with extremely noisy spectra. The resulting chemical labels for the pixels are utilized to reliably generate spatial distributions of the SAMs without pixel binning, thus, enabling full-resolution and fast SFG imaging.

Sum frequency generation imaging microscopy: Factor analysis and neural networks

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