Numerical and artificial neural network analysis of an axisymmetric co-flow-focusing microfluidic droplet generator using active and passive control

Abstract

Droplet generation in microscale has gained enormous attention in recent years especially in the pharmaceutical industry due to their application in targeted drug delivery into droplets. In most of these applications, monodispersity and uniformity of droplets are essential. Microfluidic devices can generate droplets at high throughput, enabling thousands of droplet compound encapsulation per second. The monodispersity of the droplets is ensured hydrodynamically through the dripping regime and their uniformity is controlled by active and passive microflow control methods. Here, we study numerically a microfluidic chip that uses a non-embedded co-flow-focusing geometry, so that the droplet generation throughput can take advantage of the flow-focusing devices while the non-embedded co-flow geometry forecloses the surfactant addition necessity. The continuous and dispersed phases were light mineral oil and water, respectively. We investigated the formation of droplets and studied how changing the external diameter of the chip affects the transition between the dripping regime (which corresponds to monodispersity) and the jetting regime. The number of parameters to be taken into account for the optimization of the device is enormous; therefore, in order to account for the effect of many geometrical and hydrodynamical parameters, we trained an artificial neural network based on our simulation data. Using this neural network, we evaluated droplet formation in 3240 different cases. This approach resulted in a remarkable reduction of computation time, from months to seconds. Examining numerous cases in such a short period lets us choose the optimum geometry and flow rate based on the application. The optimization was able to find the best geometry to extend the region of dripping regime in the flow rate map. Finally, to harness the droplet generation frequency, we also simulated a periodically switched laser and we were able to predict the generation of droplets with the same frequency as the switching frequency. Therefore, altering and controlling the frequency and dimensions of the droplets for a given flow rate ratio could be achieved with this technique, even without satellite droplets.