Optofluidics Refractometers

Li, Cheng; Bai, Gang; Zhang, Yunxiao; Zhang, Min; Jian, Aoqun

doi:10.3390/mi9030136

Cited by 22 publications

(9 citation statements)

References 62 publications

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“…There is an abundance of optical refractometric sensor techniques based on the interaction between the probe light beam and the analyte material based on a broad range of devices and components, among which optical fibers and waveguides, photonic crystals, planar resonant cavities, microresonators, metallic metamaterials and metasurfaces, and surface plasmon polariton prism and gratings [46,47,48,49]. Although in some of these approaches the sensitivity can exceed by more than one order of magnitude the values achieved by the proposed refractometric sensor, the latter features a series of advantages, which demonstrate its potential, namely: (i) sub-picometric linewidth, which combined with state-of-the-art optical spectrum analyzers and temperature stabilization modules can push the detection limit very low [31], ultimately limited by the absorption properties of the analyte [45]; (ii) almost linear operation in a broad range of RIU; (iii) compact dimensions limited by the size of the probe beam, planar geometry involving only one lithographic step, and free-space operation; (iv) geometrically scalable design to other target wavelengths; (v) all-dielectric configuration, avoiding ohmic losses as in plasmonics-based sensors; and (vi) potential for integration in microfluidic setups by simply confining the analyte volume with a planar superstratum.…”

Section: Resultsmentioning

confidence: 99%

Anapole Modes in Hollow Nanocuboid Dielectric Metasurfaces for Refractometric Sensing

et al. 2018

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This work proposes the use of the refractive index sensitivity of non-radiating anapole modes of high-refractive-index nanoparticles arranged in planar metasurfaces as a novel sensing principle. The spectral position of anapole modes excited in hollow silicon nanocuboids is first investigated as a function of the nanocuboid geometry. Then, nanostructured metasurfaces of periodic arrays of nanocuboids on a glass substrate are designed. The metasurface parameters are properly selected such that a resonance with ultrahigh Q-factor, above one million, is excited at the target infrared wavelength of 1.55 µm. The anapole-induced resonant wavelength depends on the refractive index of the analyte superstratum, exhibiting a sensitivity of up to 180 nm/RIU. Such values, combined with the ultrahigh Q-factor, allow for refractometric sensing with very low detection limits in a broad range of refractive indices. Besides the sensing applications, the proposed device can also open new venues in other research fields, such as non-linear optics, optical switches, and optical communications.

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Section: Resultsmentioning

confidence: 99%

Anapole Modes in Hollow Nanocuboid Dielectric Metasurfaces for Refractometric Sensing

et al. 2018

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“…A flourishing field of application of integrated-optic technologies is optical sensing and biochemical analysis on lab-on-chip optofluidic devices [3][4][5]. In these contexts, requirements on the modulation frequency may be less stringent than in telecommunications.…”

Section: Introductionmentioning

confidence: 99%

Resonant opto-mechanical modulators and switches by femtosecond laser micromachining

et al. 2020

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In this work we demonstrate novel integrated-optics modulators and switches, realized in a glass substrate by femtosecond laser pulses. These devices are based on oscillating microcantilevers, machined by water-assisted laser ablation. Single mode-optical waveguides are laser-inscribed inside the cantilever beam and continue in the substrate beyond the cantilever's tip. By exciting the resonant oscillation of the mechanical structure, coupling between the waveguide segments is varied in time. Operation frequencies are in the range of tens of kilohertz, thus they markedly overcome the response-time limitation of other glass-based modulators, which rely on the thermo-optic effect. These components may be integrated in more complex waveguide circuits or optofluidic lab-on-chips, to provide periodic and high-frequency modulation of the optical signals.

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“…For the measurement of concentration, the Coriolis device can also be used 17 with up to 0.25% accuracy. Optical methods (refractometry) 21 have been demonstrated to have excellent accuracy (as low as 0.01% concentration of IPA versus 0.5% for our demonstration); however, fine resolution optical refractometers generally rely on resonant mechanisms which are difficult to integrate into microfluidic chips. Optical probes and DNNs can be implemented to measure compounds concentrations for applications such as ammonium concentration measurement in oceanography 25 , hydrocarbon concentration measurement in reacting flows 26 , concentration measurement in microfluidic mixers 27,28 , and measurement of contaminant concentrations in water 29 .…”

mentioning

confidence: 96%

“…Such effects can also be recognized by the DNN to measure the concentration. There are alternative methods for measuring the flow rate [14][15][16][17] or the dilution ratio [18][19][20][21] in a microfluidic chip. The two prominent methods for on-chip flow measurements are the Coriolis method 17 , based on mechanical oscillations that depend on the flow rate, and thermal measurement 16 , in which a heater and thermometer are used to measure temperature changes in the flow.…”

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confidence: 99%

Learning from droplet flows in microfluidic channels using deep neural networks

Hadikhani

Borhani

Hashemi

2019

Sci Rep

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A non-intrusive method is presented for measuring different fluidic properties in a microfluidic chip by optically monitoring the flow of droplets. A neural network is used to extract the desired information from the images of the droplets. We demonstrate the method in two applications: measurement of the concentration of each component of a water/alcohol mixture, and measurement of the flow rate of the same mixture. A large number of droplet images are recorded and used to train deep neural networks (DNN) to predict the flow rate or the concentration. It is shown that this method can be used to quantify the concentrations of each component with a 0.5% accuracy and the flow rate with a resolution of 0.05 ml/h. The proposed method can in principle be used to measure other properties of the fluid such as surface tension and viscosity.Machine learning is a framework that learns from the data without being programmed. Deep leaning is a specific approach of machine learning 1 which has emerged in recent years as a powerful technique for a broad range of applications. Deep learning consists of several representation layers where each layer is obtained by the non-linear transformation of the previous layer 2 . Deep neural networks use the combination of these transformations to learn complex functions. In fluid mechanics, neural networks have been reported recently 3-5 as tools that can help computational fluid mechanics simulations by mapping the estimates of low-resolution simulations to those with higher fidelity. In microfluidics, neural networks have been used to estimate various quantities for different applications 6-9 . Mahdi and Daoud 10 used neural networks to predict the size of the droplets in an emulsion while Khor et al. 11 . used them to predict the stability of droplets in an emulsion. In our work, we employ neural networks to estimate fluid and flow parameters by observing the droplet formation process in a passive microfluidic chip. We extract this information by monitoring the flow of droplets with an optical microscope and training a neural network to obtain useful information from the recorded images. In one experiment, we trained a network to accurately measure the flow velocity at the inlet of the channel from droplet images. In a separate demonstration, a network was trained to identify the concentration of isopropanol in water in the droplet-forming solution. Our experiments demonstrate that DNNs can capture the complex nonlinear phenomena that result in the flow patterns and droplet shapes.The flow rate is a parameter that affects droplet formation 12 and can change the size, generation frequency, and pattern of droplets 13 . Similarly, the dilution ratio of isopropanol (IPA) in water affects the generation frequency and flow pattern of the droplets. Such effects can also be recognized by the DNN to measure the concentration. There are alternative methods for measuring the flow rate 14-17 or the dilution ratio 18-21 in a microfluidic chip. The two prominent methods for on-chip flow measurement...

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Optofluidics Refractometers

Cited by 22 publications

References 62 publications

Anapole Modes in Hollow Nanocuboid Dielectric Metasurfaces for Refractometric Sensing

Anapole Modes in Hollow Nanocuboid Dielectric Metasurfaces for Refractometric Sensing

Resonant opto-mechanical modulators and switches by femtosecond laser micromachining

Learning from droplet flows in microfluidic channels using deep neural networks

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