A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips

Abstract

Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. However, for complex bioassays, finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task. In this study, we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips. The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets. The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer, and the learner fetches the experiences and updates the routing paths. The proposed algorithm was applied to benchmark suites I and III as two different test benches, and it achieved significant improvements over state-of-the-art techniques.

Pinch-off droplet generator using microscale gigahertz acoustics

“…Microdroplet technology, known as droplet microfluidics, is an emerging microfluidic strategy, which can generate, manipulate and analyze discrete droplets with a volume of nanoliters or below. 1–3 Droplet microfluidics has been widely used in various fields, such as drug delivery, 4,5 chemical synthesis, 6,7 microreactors, 8,9 bioassays 10,11 and cell analysis, 12–14 in which the controllable generation of microdroplets is of vital importance for the following application process. 15 Thus, on-demand droplet generation over a wide size range with a miniaturized and versatile platform will facilitate the wide use of droplet microfluidics in various research and application fields.…”