(300h) Very Large Scale Droplet Microfluidic Integration (VLDMI) Study Using Genetic Algorithm

Authors: 
Kasule, J., West Virginia University
Maddala, J., Texas Tech University
Mobed, P., Texas Tech University
Rengaswamy, R., Indian Institute of Technology Madras

Significant research is being directed in the field of droplet microfluidics as it is anticipated to play a central role in the development of lab-on-a-chip technologies. This field enables the manipulation of discrete fluid packetsin the form of micro-droplets that provide numerous benefits for conducting biological and chemical assays. Such benefits include large reduction in sample size and volume of reagent required for assays, the size of equipmentand the enhanced speed at which these biological and chemical assays are performed. Understanding the spatio-temporal dynamics of discrete droplets inside microfluidic devices and the design of microfluidic devices for specific tasks are some of the dominant research topics. These works have since resulted in the development of simple microfluidic devices with functionalities such as sorting, merging, synchronization, storing etc. However, the anticipated application of microfluidic devices to more complex problems will require more integrated devices that can incorporate the above functionalities on a single chip.In the current work, we present a genetic algorithm optimization baseddesign tool for discovering very large scale integration of discrete microfluidic networks for a given objective function. The application of the algorithm is demonstrated through a combinatorial sequencing problem, where the objective is to achieve three different droplet combinatorial sequences for three different droplet types. Multiple fascinating, but non-obvious designs were discovered for this application. It is difficult to imagine such devices being designed using trial and error experimental procedure, which has been the main route for obtaining microfluidic device designs.With advances in technologies for fabrication of microfluidic devices, the current tool can be a significant step towards drastically cutting down on the laborious trial-and-error design process and help in developing droplet microfluidics based lab-on-a-chip platforms cheaper and faster.

Keywords:Droplet microfluidics, lab-on-a-chip technology, large scale integration, Genetic algorithm, optimization, combinatorial sequences.