Cargando…
Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073403/ https://www.ncbi.nlm.nih.gov/pubmed/33919620 http://dx.doi.org/10.3390/s21082854 |
_version_ | 1783684122107969536 |
---|---|
author | Frazão, José Palma, Susana I. C. J. Costa, Henrique M. A. Alves, Cláudia Roque, Ana C. A. Silveira, Margarida |
author_facet | Frazão, José Palma, Susana I. C. J. Costa, Henrique M. A. Alves, Cláudia Roque, Ana C. A. Silveira, Margarida |
author_sort | Frazão, José |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8073403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80734032021-04-27 Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks Frazão, José Palma, Susana I. C. J. Costa, Henrique M. A. Alves, Cláudia Roque, Ana C. A. Silveira, Margarida Sensors (Basel) Article 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. MDPI 2021-04-18 /pmc/articles/PMC8073403/ /pubmed/33919620 http://dx.doi.org/10.3390/s21082854 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Frazão, José Palma, Susana I. C. J. Costa, Henrique M. A. Alves, Cláudia Roque, Ana C. A. Silveira, Margarida Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title_full | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title_fullStr | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title_full_unstemmed | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title_short | Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks |
title_sort | optical gas sensing with liquid crystal droplets and convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073403/ https://www.ncbi.nlm.nih.gov/pubmed/33919620 http://dx.doi.org/10.3390/s21082854 |
work_keys_str_mv | AT frazaojose opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks AT palmasusanaicj opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks AT costahenriquema opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks AT alvesclaudia opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks AT roqueanaca opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks AT silveiramargarida opticalgassensingwithliquidcrystaldropletsandconvolutionalneuralnetworks |