PAPER Making General Dilution Graphs Robust to Unbalanced-Split Errors on Digital Microfluidic Biochips

Digital Microfluidic Biochips (DMFBs) can execute bio-chemical experiments very efficiently, and thus they are drawing attention recently. In biochemical experiments on a DMFB, sample preparation is an important task to generate a sample droplet with the desired con-centration value. We merge/split droplets in a DMFB to perform sample preparation. When we split a droplet into two droplets, the split cannot be done evenly in some cases. By some unbalanced splits, the generated concentration value may have unacceptable errors. This paper shows that we can decrease the impact of errors caused by unbalanced splits if we duplicate some mixing nodes in a given dilution graph for most cases. We then propose an efficient method to transform a dilution graph in order to decrease the impact of errors caused by unbalanced splits. We also present a preliminary experimental result to show the potential of our method.

Automated summary

Digital Microfluidic Biochips (DMFBs) have gained attention for their efficient execution of bio-chemical experiments. Sample preparation is crucial for generating sample droplets with desired concentration values in a DMFB. Uneven splitting of droplets can lead to unacceptable errors in concentration values. This paper proposes duplicating mixing nodes in a dilution graph to decrease the impact of errors caused by unbalanced splits. The presented experimental results demonstrate the potential of the proposed method.