| Title: | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
| Author(s): | Frazao J; Palma S; Costa HMA; Alves C; Roque ACA; Silveira M; |
| Address: | "Institute for Systems and Robotics (ISR), Instituto Superior Tecnico (IST), University of Lisbon, 1049-001 Lisbon, Portugal. UCIBIO, Chemistry Department, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| 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 results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9-4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets" |
| Keywords: | Cnn Lstm Yolo gas sensing liquid crystal volatile organic compound; |
| Notes: | "PubMed-not-MEDLINEFrazao, Jose Palma, Susana I C J Costa, Henrique M A Alves, Claudia Roque, Ana C A Silveira, Margarida eng 639123/ERC_/European Research Council/International UCIBIO (UIDB/ 04378/2020)/Applied Molecular Biosciences Unit/ EU Horizon 2020 research and innovation programme (grant agreement No. SCENT-ERC-2014-STG-639123, 2015-2022)/ERC_/European Research Council/International LARSyS - FCT Project UIDB/50009/2020/Fundacao para a Ciencia e a Tecnologia/ Switzerland 2021/05/01 Sensors (Basel). 2021 Apr 18; 21(8):2854. doi: 10.3390/s21082854" |
