Sample preparation is an essential process in biochemical reactions, and many techniques have been proposed to address this issue recently. However, only one of them, IDSA, focuses on the multi-target sample preparation problem. In this thesis, we propose a new algorithm, WARA, which concentrates on reactant and waste minimization in multi-target sample preparation. WARA first generates a set of mixing trees for all required target concentrations as an initial solution, and then recycles waste droplets through droplet sharing and droplet replacement. For the droplet sharing, WARA uses number of nonzero bits (nzb) in the binary representation of CVs to speed up sharing process. During the droplet replacement, the replacement candidate pair (RCP) is proposed to guarantee that the count of waste droplets decreases monotonously. Two factors, gain and uniqueness, are used to determine the replacement order for better reactant usage and waste count. The experimental results demonstrate that all the three phases of WARA have their own contributions during optimization. Furthermore, WARA outperforms the existing state-of-the-art algorithm IDSA in terms of waste amount and operation count. WARA reduces the waste and operation count by 48% and 37% respectively when the number of target concentrations is ten. The reduction is increased to 97% and 73% when the number of target concentrations goes to hundred.
Moreover, WARA is also very efficient in runtime. As a consequence, it is conclusive that WARA is currently the best method for multi-target sample preparation on digital microfluidic biochips.
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