Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks.

ABSTRACT: Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the r

SUBMITTER: Frazao J 

PROVIDER: S-EPMC8073403 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature