Metasurface-Incorporated Optofluidic Refractive Index Sensing for Identification of Liquid Chemicals through Vision Intelligence

Conventional approaches for the identification of liquid chemicals are bulky and harmful to the environment, detect a limited number of chemical species, produce high false alarm rates, or rely on complex/expensive spectrometers. In this study, a spectrometer-free, accurate metasurface-mediated liquid identification scheme was demonstrated based on optofluidic refractive index (RI) sensing in conjunction with vision intelligence algorithms. A metasurface device integrated into an optofluidic channel provides a polarization-independent focused vortex beam at a single wavelength of 1550 nm, which is highly sensitive to liquid chemicals. The beam patterns respond to the RI and transmission of chemicals, and thus effectively serve as their unique optical "fingerprints". To realize vision intelligence, two deep-learning architectures -a convolutional neural network and a vision transformer -were adopted and trained to classify the beam patterns. A variety of liquid chemicals were successfully identified in situ with over 99% accuracy, requiring no spectrometers. The proposed approach is expected to corroborate the feasibility of artificial intelligence-powered detection schemes that can classify at single wavelengths, unlike conventional instrument-intensive techniques that are attentive to entire spectral responses.