A reverse predictive model towards design automation of microfluidic droplet generators

This work has been presented in the 10th IWBDA workshop.Droplet-based microfluidic devices in comparison to test tubes can reduce reaction volumes 10^9 times and more due to the encapsulation of reactions in micro-scale droplets [4]. This volume reduction, alongside higher accuracy, higher sensitivity and faster reaction time made droplet microfluidics a superior platform particularly in biology, biomedical, and chemical engineering. However, a high barrier of entry prevents most of life science laboratories to exploit the advantages of microfluidics. There are two main obstacles to the widespread adoption of microfluidics, high fabrication costs, and lack of design automation tools. Recently, low-cost fabrication methods have reduced the cost of fabrication significantly [7]. Still, even with a low-cost fabrication method, due to lack of automation tools, life science research groups are still reliant on a microfluidic expert to develop any new microfluidic device [3, 5]. In this work, we report a framework to develop reverse predictive models that can accurately automate the design process of microfluidic droplet generators. This model takes prescribed performance metrics of droplet generators as the input and provides the geometry of the microfluidic device and the fluid and flow settings that result in the desired performance. We hope this automation tool makes droplet-based microfluidics more accessible, by reducing the time, cost, and knowledge needed for developing a microfluidic droplet generator that meets certain performance requirement

From the Hicksites to the Progressive Friends: The Rural Roots of Perfectionism and Social Reform among North American Friends

In the 1840s and 1850s, North American Friends endured a series of localized separations. This paper examines the Progressive Friends separations in Genesee Yearly Meeting in 1848, centered in the \u27burned-over district\u27 of New York State, and in Western Quarterly Meeting of Philadelphia Yearly Meeting (Hicksite) in 1852-53. Both separations had roots in the controversy among Friends over appropriate anti-slavery activities and both challenged the existing structures of the Religious Society of Friends. These separations were both radical and rural, and mark a distinct change from the earlier deference of Friends towards the leadership of London and Philadelphia Yearly Meetings

The Underground Railroad: South to North

Christopher Densmore's presentation notes from the MARAC Spring 2014 conference. His presentation is titled "The Underground Railroad: South to North", S12 - April 25, 2014, Rochester, N

Time Synchronization Server

Report presents the results of IST\u27s installation and integration of Network Time Protocol (NTP) and a Global Positioning System (GPS) receiver into the Distributed Interactive Stimulation (DIS) TESTBED located in IST\u27s laboratories

Modular microfluidic design automation using machine learning

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