Intelligent Transportation of Heterogeneous droplets in DMFB: A Preferential Deviation Based Technique

In order to enhance the performance of DMFBs, the parallel routing of droplets plays a crucial role. By implementing this approach in a single bio-assay, we can effectively optimize the latest arrival time. However, the various fluidic constraints present on DMFB platform raise different challenges such as collisions and contaminations, especially when dealing with heterogeneous droplet routing. Therefore, it becomes imperative to devise an efficient algorithm that not only satisfies all the constraints but also minimizes issues like stalling, collisions, and contaminations caused by parallel routing. By tackling these obstacles head-on, we can significantly improve the overall efficiency and reliability of DMFBs. In this work, we propose an algorithm for heterogeneous droplet transportation. The proposed intelligent deviation preference scheme serves as a valuable tool in enhancing the overall performance and reliability of the system, allowing for seamless parallel routing and improved cell utilization with latest arrival time.