Monitoring the evaporation of fluids from fiber-optic micro-cell cavities

Optical Fiber Fabry-Pérot Microfluidic Sensor Based on Capillary Fiber and Side Illumination Method

In this paper, an optical fiber Fabry-Pérot (FP) microfluidic sensor based on the capillary fiber (CF) and side illumination method is designed. The hybrid FP cavity (HFP) is naturally formed by the inner air hole and silica wall of CF which is side illuminated by another single mode fiber (SMF). The CF acts as a naturally microfluidic channel, which can be served as a potential microfluidic solution concentration sensor. Moreover, the FP cavity formed by silica wall is insensitive to ambient solution refractive index but sensitive to the temperature. Thus, the HFP sensor can simultaneously measure microfluidic refractive index (RI) and temperature by cross-sensitivity matrix method. Three sensors with different inner air hole diameters were selected to fabricate and characterize the sensing performance. The interference spectra corresponding to each cavity length can be separated from each amplitude peak in the FFT spectra with a proper bandpass filter. Experimental results indicate that the proposed sensor with excellent sensing performance of temperature compensation is low-cost and easy to build, which is suitable for in situ monitoring and high-precision sensing of drug concentration and the optical constants of micro-specimens in the biomedical and biochemical fields.

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

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.

Optical Fiber Sensor for Monitoring the Evaporation of Ethanol–Water Mixtures

An inline optical fiber sensor is proposed to monitor in real time the evaporation process of ethanol–water binary mixtures. The sensor presents two interferometers, a cladding modal interferometer (CMI) and a Mach–Zehnder interferometer (MZI). The CMI is used to acquire the variations in the external medium refractive index, presenting a maximum sensitivity of 387 nm/RIU, and to attain the variation in the sample concentration profile, while the MZI monitors temperature fluctuations. For comparison purposes, an image analysis is also conducted to obtain the droplet profile. The sensor proposed in this work is a promising alternative in applications where a rigorous measurement of volatile organic compound concentrations is required, and in the study of chemical and physical properties related to the evaporation process.

Machine learning identifies liquids employing a simple fiber-optic tip sensor

We proposed an extremely simple fiber-optic tip sensor system to identify liquids by combining their corresponding droplet evaporation events with analyses using machine learning techniques. Pendant liquid droplets were suspended from the cleaved endface of a single-mode fiber during the experiment. The optical fiber-droplet interface and the droplet-air interface served as two partial reflectors of an extrinsic Fabry-Perot interferometer (EFPI) with a liquid droplet cavity. As the liquid pendant droplet evaporated, its length diminished. A light source can be used to observe the effective change in the net reflectivity of the optical fiber sensor system by observing the resulting optical interference phenomenon of the reflected waves. Using a single-wavelength probing light source, the entire evaporation event of the liquid droplet was precisely captured. The measured time transient response from the fiber-optic tip sensor to an evaporation event of a liquid droplet of interest was then transformed into image data using a continuous wavelet transform. The obtained image data was used to fine-tune pre-trained convolution neural networks (CNNs) for the given task. The results demonstrated that machine learning-based classification methods achieved greater than 98% accuracy in classifying different liquids based on their corresponding droplet evaporation processes, measured by the fiber-optic tip sensor.

Optomechanical time-domain reflectometry

Optical fibres constitute an exceptional sensing platform. However, standard fibres present an inherent sensing challenge: they confine light to an inner core. Consequently, distributed fibre sensors are restricted to the measurement of conditions that prevail within the core. This work presents distributed analysis of media outside unmodified, standard fibre. Measurements are based on stimulated scattering by guided acoustic modes, which allow us to listen where we cannot look. The protocol overcomes a major difficulty: guided acoustic waves induce forward scattering, which cannot be mapped using time-of-flight. The solution relies on mapping the Rayleigh backscatter contributions of two optical tones, which are coupled by the acoustic wave. Analysis is demonstrated over 3 km of fibre with 100 m resolution. Measurements distinguish between air, ethanol and water outside the cladding, and between air and water outside polyimide-coated fibres. The results establish a new sensor configuration: optomechanical time-domain reflectometry, with several potential applications.

Dynamic sessile micro-droplet evaporation monitored by electric impedance sensing

Studies of liquid evaporation on a solid surface are useful for wettability phenomena-related research, and can be applied in a series of scientific and industrial areas. However, traditional methods are not easy to be intergrated into small size to monitor evaporation process of a micro-droplet. In this paper, a micro-electrode array was used to measure the impedance of an electrolyte droplet, indicating the dynamic process of evaporation. This method uses the relationship between concentration and conductivity of the water solution to dynamically monitor the evaporation process. The dynamic impedance results were compared to weight and imaging data of droplet evaporation and demonstrate high correlation coefficient of the earlier 90% part of the sodium chloride droplet evaporation process (R ² = 0.99). Our study proved that the height of the droplet will affect the impedance sensing result, and the solution used for droplet evaporation can be expanded to mixture of strong electrolyte solution such as phosphate buffered solution. Then the "impedance imaging" of the array monitored the evaporating speed differences of different sites of a sessile droplet. As the electrode array can be integrated into small size, this method is compatible for many other experimental systems and can be further used for evaporation studies and corresponding application areas.

Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

Evaporation of volatile compounds in suspended-core fibers

A sensing configuration for fluid evaporation monitoring using a suspended-core fiber tip is proposed. Strong differences between the evaporation processes of acetone and isopropyl alcohol were observed, both in terms of the signal's intensity fluctuations and total duration. In each fluid, the main signal variations were due to changes in reflectivity inside a collapsed region of the suspended-core fiber near the spliced interface with a standard single-mode fiber. After further analysis with a wider array of substances, this configuration could, in the future, be used to detect and study the evaporation of different volatile organic compounds.