Whispering gallery mode sensing through the lens of quantum optics, artificial intelligence, and nanoscale catalysis

Abstract

Ultra-sensitive sensors based on the resonant properties of whispering gallery modes (WGMs) can detect fractional changes in nanoscale environments down to the length and time scales of single molecules. However, it is challenging to isolate single-molecule signals from competing noise sources in experiments, such as thermal and mechanical sources of noise, and—at the most fundamental level—the shot noise limit of classical light. Additionally, in contrast to traditional bulk refractive index measurements, analyzing single-molecule signals is complicated by the localized nature of their interactions with nanoscale field gradients. This perspective discusses multifaceted solutions to these challenges, including the use of quantum light sources to boost the signal-to-noise ratio in experiments and leveraging the power of supercomputers to predict the electronic response of molecules to WGM optoplasmonic fields. We further discuss the role of machine learning in WGM sensing, including several advanced models that can predict molecular polarizability and solvent effects. These advancements in WGM spectroscopy and computational modeling can help to decipher the molecular mechanics of enzymes, enable studies of catalysis on the nanoscale, and probe the quantum nature of molecules.