Search for: All records

We describe an exciting new application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB consists of a two-dimensional electrode array, and it manipulates droplets of liquid to automatically execute biochemical protocols for clinical chemistry. However, a major problem with DMFBs is that electrodes can degrade over time. The transportation of droplet transportation over these degraded electrodes can fail, thereby adversely impacting the integrity of the bioassay outcome. We demonstrated that the formulation of droplet transportation as an RL problem enables the training of deep neural network policies that can adapt to the underlying health conditions of electrodes and ensure reliable fluidic operations. We describe an RL-based droplet routing solution that can be used for various sizes of DMFBs. We highlight the reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. We show that the use of the RL approach on a simple micro-computer (Raspberry Pi 4) leads to acceptable performance for time-critical bioassays. We present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet routing problems on DMFBs. We present results on our study of electrode degradation using fabricated DMFBs. The study supports the degradation model used in the simulator. 

Advances in lab-on-a-chip technologies are driven by the pursuit of programmable microscale bioreactors or fluidic processors that mimic electronic functionality, scalability, and convenience. However, few fluidic mechanisms allow for basic logic operations on rewritable fluidic paths due to cross-contamination, which leads to random interference between “fluidic bits” or droplets. Here, we introduce a mechanism that allows for contact-free gating of individual droplets based on the scalable features of acoustic streaming vortices (ASVs). By shifting the hydrodynamic equilibrium positions inside interconnected ASVs with multitonal electrical signals, different functions such as controlling the routing and gating of droplets on rewritable fluidic paths are demonstrated with minimal biochemical cross-contamination. Electrical control of this ASV-based mechanism allows for unidirectional routing and active gating behaviors, which can potentially be scaled to functional fluidic processors that can regulate the flow of droplets in a manner similar to the current in transistor arrays. 

Contact-free manipulation of small objects ( e.g. , cells, tissues, and droplets) using acoustic waves eliminates physical contact with structures and undesired surface adsorption. Pioneering acoustic-based, contact-free manipulation techniques ( e.g. , acoustic levitation) enable programmable manipulation but are limited by evaporation, bulky transducers, and inefficient acoustic coupling in air. Herein, we report an acoustofluidic mechanism for the contactless manipulation of small objects on water. A hollow-square-shaped interdigital transducer (IDT) is fabricated on lithium niobate (LiNbO 3 ), immersed in water and used as a sound source to generate acoustic waves and as a micropump to pump fluid in the ± x and ± y orthogonal directions. As a result, objects which float adjacent to the excited IDT can be pushed unidirectionally (horizontally) in ± x and ± y following the directed acoustic wave propagation. A fluidic processor was developed by patterning IDT units in a 6-by-6 array. We demonstrate contactless, programmable manipulation on water of oil droplets and zebrafish larvae. This acoustofluidic-based manipulation opens avenues for the contactless, programmable processing of materials and small biosamples.