Design of microfluidic chromatographs through reinforcement learning

Abstract

Chromatography is one of the most valuable techniques chemists possess at their disposal, conducive to everything from developing vaccines, food, beverage, and drug testing to catching criminals. The diverse applications allow it to be used for analytical and preparative purposes. On the other hand, droplet microfluidics has significantly evolved from simple droplet generators to complex and integrated tasks through specially designed networks. Microfluidics finds itself at the center of various Lab-on-Chip studies, enabling single-cell analysis, biochemical synthesis, etc. We demonstrate a microfluidic chromatograph machine that can produce an ordered droplet arrangement for a large number of drops. The droplets are sent into the device using a novel methodology where the conventional droplet train is made into smaller batches. The study describes the use of droplet batch methodology and compares it with the traditional droplet train approach. Using this platform, different droplet sequences are sent through the chromatograph, which preferably allows some droplets to exit first while others take a longer time to flow across the chromatograph based on the droplet properties and device design. The droplet sequences contain various drops; however, the type of drops in these sequences is limited to 2. The chromatograph can handle any number of drops in a single machine is enough for handling diverse droplet sequences. The stability of the microfluidic chromatography is also studied by varying the droplet properties and the droplet batch size.

Keywords

• Droplet microfluidics
• Optimization
• Reinforcement learning