A high flow rate thermal bubble-driven micropump with induction heating

Liu, Bendong; Sun, Jianchuang; Li, Desheng; Zhe, Jiang; Oh, Kwang W.

doi:10.1007/s10404-016-1822-2

Cited by 26 publications

(10 citation statements)

References 27 publications

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“…Using DNNs is a suitable approach to measure the gas flow rate online. Moreover, DNNs measure the flow rate in the cases where the exact flow rate inside the microfluidic chip is unknown 37,38 . Furthermore, this method can be used to measure other fluid parameters.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Learning from droplet flows in microfluidic channels using deep neural networks

Hadikhani

Borhani

Hashemi

2019

Sci Rep

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“…With origins in inkjet printing, thermal bubble micropumping uses an integrated microheater to drive the expansion of an attached bubble. This has been used to impose an oscillatory flow with frequencies up to 300 Hz and can be performed using either Joule or induction heating to influence bubble expansion. Piezoelectric diaphragms have also been used to induce fluid motion over a wide frequency range …”

Section: Pulsatile Flows In Microfluidic Processesmentioning

confidence: 99%

Pulsatile Flow in Microfluidic Systems

Dincau

Dressaire

Sauret

2019

Small

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This review describes the current knowledge and applications of pulsatile flow in microfluidic systems. Elements of fluid dynamics at low Reynolds number are first described in the context of pulsatile flow. Then the practical applications in microfluidic processes are presented: the methods to generate a pulsatile flow, the generation of emulsion droplets through harmonic flow rate perturbation, the applications in mixing and particle separation, and the benefits of pulsatile flow for clog mitigation. The second part of the review is devoted to pulsatile flow in biological applications. Pulsatile flows can be used for mimicking physiological systems, to alter or enhance cell cultures, and for bioassay automation. Pulsatile flows offer unique advantages over a steady flow, especially in microfluidic systems, but also require some new physical insights and more rigorous investigation to fully benefit future applications.

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“…36 The semipermeable shell of Au-coated liposomes could be advantageous for liquid cell TEM, since the electron-dose-rate-dependent bubble expansion (0-40 nm 3 s -1 , Figure S11b) could be used to finely control the flow rate of water pumped out of the nanocapsules (40 yL s -1 ). Such nanopumps would be analogous to the microfluidic pumps driven by expansion and collapse of thermally 73 or electrochemically 74 generated microbubbles. (Figure 4b), bubble velocities occasionally spiked to 200 nm s -1 (Figure 2b, frames 2 and 3; Figure 4ab).…”

Section: H2o → Eh - H • Oh • Oh - H2 Ho2 • H2o2 H3o +mentioning

confidence: 99%