Demand-Driven Single- and Multitarget Mixture Preparation Using Digital Microfluidic Biochips

Abstract

Recent studies in algorithmic microfluidics have led to the development of several techniques for automated solution preparation using droplet-based digital microfluidic (DMF) biochips. A major challenge in this direction is to produce a mixture of several reactants with a desired ratio while optimizing reactant cost and preparation time. The sequence of mix-split operations that are to be performed on the droplets is usually represented as a mixing tree (or graph). In this article, we present an efficient mixing algorithm, namely, Mixing Tree with Common Subtrees ( MTCS ), for preparing single-target mixtures. MTCS attempts to best utilize intermediate droplets, which were otherwise wasted, and uses morphing based on permutation of leaf nodes to further reduce the graph size. The technique can be generalized to produce multitarget ratios, and we present another algorithm, namely, Multiple Target Ratios ( MTR ). Additionally, in order to enhance the output load, we also propose an algorithm for droplet streaming called Multitarget Multidemand ( MTMD ). Simulation results on a large set of target ratios show that MTCS can reduce the mean values of the total number of mix-split steps ( T ms ) and waste droplets ( W ) by 16% and 29% over Min-Mix (Thies et al. 2008) and by 22% and 34% over RMA (Roy et al. 2015), respectively. Experimental results also suggest that MTR can reduce the average values of T ms and W by 23% and 44% over the repeated version of Min-Mix , by 30% and 49% over the repeated version of RMA , and by 9% and 22% over the repeated-version of MTCS , respectively. It is observed that MTMD can reduce the mean values of T ms and W by 64% and 85%, respectively, over MTR . Thus, the proposed multitarget techniques MTR and MTMD provide efficient solutions to multidemand, multitarget mixture preparationon a DMF platform.