Quick Response Auto‐Coding and Recognition of Mixed Vapors Via Microlaser Array Sensor

Abstract

AbstractThe superior stimuli‐responsiveness, narrow linewidth, and high spectral multiplexing capacity of microlasers have led to their use as photonic tags for molecular labeling, encryption, and anticounterfeiting. However, the requirement of consistent lasing features for repeated measurements and the need for lasing features to change regularly with varying analytes pose a challenge to the efficient and convenient authentication of laser‐encoded photonic tags for practical applications. To address this challenge, an optical microsphere array is proposed that provides a set of real‐time typical lasing spectra collected from microspheres coated with specific recognition surface films of different sizes capable of recognizing one analyte or a mixture of analytes. These lasing spectra were transformed into 2D grayscale barcodes. Additionally, a gray value‐quick response code (GV‐QR code) is developed using deep learning methods, which enables the real‐time monitoring and identification of molecular concentration changes through GV‐QR autocoding, resulting in more precise, wide‐ranging, and reliable molecular detection.