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    • ENG: Electrical and Computer Engineering: Scholarly Papers
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    •   OpenBU
    • College of Engineering
    • Electrical and Computer Engineering
    • ENG: Electrical and Computer Engineering: Scholarly Papers
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    Modular microfluidic design automation using machine learning

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    License
    Attribution-NonCommercial 4.0 United States
    Date Issued
    2019-07-10
    Author(s)
    Lashkaripour, Ali
    Rodriguez, Christopher
    Mehdipour, Noushin
    McIntyre, David
    Densmore, Douglas
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    Permanent Link
    https://hdl.handle.net/2144/36567
    Citation (published version)
    Lashkaripour, Ali, Christopher Rodriguez, Noushin Mehdipour, David McIntyre, and Douglas Densmore. "Modular microfluidic design automation using machine learning." 11th International Workshop on Bio-Design Automation (IWBDA-19), 2019.
    Abstract
    Microfluidics is the science of handling liquids inside sub-millimeter microchannels at nano-liter and pico-liter scales. This volume reduction enables increased resolution, sensitivity, and throughput, while, reducing the reagent cost significantly. These advantages make microfluidic devices to be ideal substitutes for bench-top and robotic liquid handling in numerous life science applications, specifically, synthetic biology where there is a need for low-cost, automated, and high-throughput platforms. Despite the need, implementing microfluidic platforms in the life science research work-flow is an exception rather than being the norm. This can be attributed to the high cost of fabricating microfluidic devices and a lack of microfluidic design automation tools that can design a microfluidic geometry based on the desired performance. As a result, designing a microfluidic device that delivers an expected performance is an iterative, time-consuming, and costly process. To address this, we previously described a low-cost and accessible micro-milling technique to fabricate microfluidic devices in less than an hour while costing less than $10. However, still designing a microfluidic device that performs as expected is an iterative and in-efficient process. Therefore, microfluidic design automation tools that are able to design a microfluidic geometry and provide the necessary flow conditions and fluid properties that would deliver a user-specified performance is with great importance. We propose a modular design automation tool, called DAFD, that is able to design a microfluidic device based on user-specified performance and constraints. DAFD uses machine learning to generate accurate predictive models, and then exploits these models to provide a design automation platform. DAFD can be implemented in many microfluidic applications such as droplet generation, high-throughput sorting, and micro-mixing.
    Rights
    Attribution-NonCommercial 4.0 United States
    Collections
    • ENG: Biomedical Engineering: Scholarly Papers [268]
    • BU Open Access Articles [3664]
    • ENG: Electrical and Computer Engineering: Scholarly Papers [252]


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