Microfluidic flowmeter based on micro “hot-wire” sandwiched Fabry-Perot interferometer

Li, Ying; Yan, Guofeng; Zhang, Liang; He, Sailing

doi:10.1364/oe.23.009483

Cited by 56 publications

(30 citation statements)

References 22 publications

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“…1(b). It can be observed that even though at different fluidic conditions, the temperature increase follows a reasonable linear relationship with respect to the pumping power, which is similar to the heating process of hot fibers 2,8,14 . On the same amount of pumping power, the temperature difference between the flow sensor and oil is larger than water because the heat capacity of the former fluid is ~1.88 J .…”

Section: Introductionmentioning

confidence: 59%

Miniature FBG-based fluidic flowmeter to measure hot oil and water

et al. 2017

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In this paper, we present a miniature fluidic flowmeter based on a packaged FBG and laser-heated fibers. The flow rates of water and hydraulic oil were measured by utilizing the proposed flowmeter. The measured results exhibited good sensitivity of 0.339 nm/(m/s) for water and 0.578 nm/(m/s) for oil flow. Experimental results showed that the sensitivity of the fluidic flow sensor is depending on the heat capacity of the fluids, where the fluid with higher heat capacity has higher sensitivity and lower detection limit at the same measurement condition. The real-time flow rates measured by the proposed sensor and a commercial flowmeter installed in the test rig were also compared, demonstrating good agreement with correlation coefficient of 0.9974.

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Section: Introductionmentioning

confidence: 59%

Miniature FBG-based fluidic flowmeter to measure hot oil and water

et al. 2017

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“…The sensor is easy to fabricate with a simple and reliable structure. Besides, it shows an ultra-high sensitivity, which is almost one order of magnitude higher than previous reports when the flow rate is 1 μl/s [7,8]. The minimum detectable change of the flow rate is 9 nl/s at v 1 μl∕s.…”

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confidence: 61%

“…While at v 1 μl∕s, the sensitivities are approximately 1.092, 1.637, and 2.183 nm/(μl/s), when the incident power is also set to 100, 150, and 197 mW. At v 1 μl∕s, there is almost one order of magnitude improvement in the sensitivity than for previous reports [7,8]. Because the resolution of the OSA is 0.02 nm, the minimum detectable change of the flow rate is approximately 48 nl/s at v 3 μl∕s and 9 nl/s at v 1 μl∕s when the incident power is 197 mW.…”

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confidence: 67%

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“Hot-wire” microfluidic flowmeter based on a microfiber coupler

Yan

Liu

et al. 2016

Opt. Lett.

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Using an optical microfiber coupler (MC), we present a microfluidic platform for strong direct or indirect light-liquid interaction by wrapping a MC around a functionalized capillary. The light propagating in the MC and the liquid flowing in the capillary can be combined and divorced smoothly, keeping a long-distance interaction without the conflict of input and output coupling. Using this approach, we experimentally demonstrate a "hot-wire" microfluidic flowmeter based on a gold-integrated helical MC device. The microfluid inside the glass channel takes away the heat, then cools the MC and shifts the resonant wavelength. Due to the long-distance interaction and high temperature sensitivity, the proposed microfluidic flowmeter shows an ultrahigh flow rate sensitivity of 2.183 nm/(μl/s) at a flow rate of 1 μl/s. The minimum detectable change of the flow rate is around 9 nl/s at 1 μl/s.

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“…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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Microfluidic flowmeter based on micro “hot-wire” sandwiched Fabry-Perot interferometer

Cited by 56 publications

References 22 publications

Miniature FBG-based fluidic flowmeter to measure hot oil and water

Miniature FBG-based fluidic flowmeter to measure hot oil and water

“Hot-wire” microfluidic flowmeter based on a microfiber coupler

Learning from droplet flows in microfluidic channels using deep neural networks

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