More Effective Randomly-Designed Microfluidics

Abstract

Random design of microfluidics is gaining significant attention by creating functional microfluidic chips. Notable merit of random design is that the error-prone design stage is avoided by a library of random chips, which are simulated beforehand using finite element analysis. This paper proposes a methodology for more effective random chip designs, which further optimizes the random chip library to significantly reduce sample consumption. The random design optimization method can be separately loaded as a stand-alone tool and applied to the original chips from the library. Computational simulation results show that the proposed method greatly reduces sample consumption by more than 20% on average in terms of redundant channels. Moreover, the induced deviations in concentrations are mostly less than 0.002, which are negligible in real biomedical applications.