Evaporation rate sensor of liquids using a simple fiber optic configuration

Lim, Ok-Rak; Cho, Hee-Taek; Seo, Gyeong-Seo; Kwon, Min‐Ki; Ahn, Tae-Jung

doi:10.1364/cleopr.2018.w3a.81

Cited by 2 publications

(2 citation statements)

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“…FOSs have been used for measuring various parameters such as displacement, strain, temperature, and pressure. − Currently, FOSs are fabricated following different designs, such as the fiber-optic gyroscopes, the fiber grating sensors, the interferometer sensors, and so forth. Fiber-optic interferometers are widely utilized in the analysis of liquid droplets, − especially, the extrinsic Fabry–Perot interferometer (EFPI)-based sensor, which has demonstrated tremendous potential because it is simple and highly sensitive. , Using the EFPI approach to monitor liquid droplet evaporation events has been previously reported in the literature. − However, the previous studies focused on evaporation dynamics, but they did not use the experiment data to identify liquids. Moreover, the model employed in the present study provides a substantial improvement over the classification models used in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

“… 14 , 22 Using the EFPI approach to monitor liquid droplet evaporation events has been previously reported in the literature. 23 − 25 However, the previous studies focused on evaporation dynamics, but they did not use the experiment data to identify liquids. Moreover, the model employed in the present study provides a substantial improvement over the classification models used in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

et al. 2023

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In this paper, we report an array of fiber-optic sensors based on the Fabry–Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.

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