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.
Highlights
We demonstrated that the 1D signal corresponding to the intensity of polarised light transmitted through hybrid gels contains volatile organic compounds (VOC) distinctive features [5,19,20]
Taking in consideration the number of sequences available for each droplet diameter range, the results indicate VOC recognition task is optimised by droplets with diameter larger than 24 μm and using the CNN2D+LSTM
In this work we explored a VOC pattern recognition approach which is fully automated and takes advantage of the full extent of information carried by Liquid crystal (LC) optical textures, namely morphological and colour changes along time
Summary
The study of liquid crystals as platforms for gas sensing attracts increasing interest from the scientific community due to their fast and reversible optical responses, low energydemand, operation at room temperature and tunable selectivity [1]. These are advantages when compared to the conventional semiconductor metal oxide [2] and polymeric [3] gas sensors, that require high operating temperatures and lack selectivity. These LC properties are valuable to develop new portable and low-cost devices for real-time detection of odours and volatile organic compounds (VOC), which can find applications in fields like industrial manufacture [4] and food quality control [5], air quality monitoring [6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
