(372x) Spacing Design for Active Sorting of Drops in Millifluidic Network: A Genetic Algorithm-Based Approach

Droplet microfluidics or
millifluidics has been used for reaction kinetic studies, encapsulation of
cells in drops and their analysis etc. For these applications, sorting of drops
based on their properties or contents is very important in the pre-processing
or post-processing phases. Different methods for active and passive sorting are
available in literature^1,2. In this work, we experimentally
demonstrate an active sorting technique where different types of drops are
allowed to interact in a 1D millifluidic network in such a way that they get
sorted at the exit.

When the drops move in a 1D network,
they interact hydrodynamically. These interactions are dictated by the network design,
material and size of the drop and position and velocity of other drops in the
network.  The movement of drops in 1D networks can be simulated using network
model³. By appropriately changing the time
at which each drop enters the network or the distance between the drops at the
inlet to the network, one can manipulate the movement of the drops in the
network.

We use a GA (genetic algorithm) based
optimization⁴ framework that incorporate
simulations⁴based on network model to identify the
desired inter-droplet spacing at the inlet that results in the sorting of
different types of drops.  The detection of drops and measurement of
inter-droplet spacing is carried out using a pair of LDR (Light dependent
resistor) sensors. The desired spacing profile (obtained from the GA based
optimization) at the inlet is implemented by introducing or withdrawing
continuous phase fluid between the drops.

The schematic of the experimental
setup is shown below.

Schematic of experimental set-up

References

1.        Xi H-D, Zheng H, Guo W, et
al. Active droplet sorting in microfluidics: a review. Lab Chip.
2017;17(5):751-771. doi:10.1039/C6LC01435F.

2.        Hatch
AC, Patel A, Beer NR, Lee AP. Passive droplet sorting using viscoelastic flow
focusing. Lab Chip. 2013;13(7):1308-1315. doi:10.1039/C2LC41160A.

3.        Schindler
M, Ajdari A. Droplet Traffic in Microfluidic Networks: A Simple Model for
Understanding and Designing. Phys Rev Lett. 2008;100(4):44501.
doi:10.1103/PhysRevLett.100.044501.

4.        Kasule
JS, Maddala J, Mobed P, Rengaswamy R. Very large scale droplet microfluidic
integration (VLDMI) using genetic algorithm. Comput Chem Eng.
2016;85:94-104. doi:https://doi.org/10.1016/j.compchemeng.2015.10.018.