Machine-learning methods are increasingly prevalent in plasmonics to predict optical properties of nanomaterials, to model plasmon modes and to process plasmonic data. For example, surface enhanced Raman scattering (SERS) and Raman spectroscopy yields large data sets with information-rich spectra. Classical linear methods are limited, especially for SERS spectra of single molecules, where the spectra are highly dependent on the orientation of molecules on surfaces and for large data sets. Methods from data sciences are increasingly used to classify spectra into categories and predict SERS spectra for new data based on trained algorithms. For example, we recently introduced the concept of SERS optophysiology, which combines a SERS nanosensor on the tip of a pulled fiber to provide spatially and temporally specific molecular information near or inside biological material. To accomplish this, a SERS nanofiber decorated with a dense and well dispersed array of Au NP has been developed for the measurement of neurotransmitters and other metabolites in proximity of cells. The nanosensors are thus highly compatible with current physiology experiments also relying on similar nanosensors based on electrochemistry and electrophysiology. Specifically, we will show that the SERS optophysiology nanosensor can measure a panel of metabolites near cells in a single experiment. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method, processed with TensorFlow using a convolutional neural network architecture. This machine-learning driven data processing significantly improved the positive assignment rates for a series of metabolites and allows for complex measurements of the cell’s biochemistry. In addition to these untargeted SERS nanosensors, we also designed molecularly specific sensors to measure pH, H<sub>2</sub>O<sub>2</sub> and heavy metals inside cells using the same nanosensor architecture, and COVID antibodies sensors using a different sensing platform. In those cases, the machine-learning algorithm serves to triage spectra, to classify them and to cluster categories of samples. Different examples will be shown to illustrate the use of machine learning in SERS.

Machine-learning tools for plasmonic sensors

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ACS Fall 2022

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