Robust Multi-Target Sample Preparation on MEDA Biochips Obviating Waste Production

Article Type

Research Article

Publication Title

ACM Transactions on Design Automation of Electronic Systems

Abstract

Digital microfluidic biochips have fueled a paradigm shift in implementing bench-top laboratory experiments on a single tiny chip, thus replacing costly and bulky equipment. However, because of imprecise fluidic functions, several volumetric split errors may occur during the execution of bioassays. Earlier approaches to error-correcting sample preparation addressed this problem by using a cyberphysical system yielding several drawbacks such as increased sample preparation cost and time, and uncertainty in assay completion time. In addition, error correction for only a single-target sample has been considered so far, although many assays require the production of multi-target samples. In this work, we present an error-free dilution technique that guarantees the correctness of the resulting concentration factor of a sample without performing any additional roll-back or roll-forward action. To the best of our knowledge, we are the first to present a solution strategy for tackling dispensing errors during sample preparation. We use micro-electrode-dot-array biochips that offer the advantages of manipulating fractional volumes of droplets (aliquots) for navigation, as well as mix-split operations. Instead of performing traditional mix-and-split steps with integral-volume droplets, we execute only an aliquoting-and-mix sequence using differential-size aliquots. Thus, all split operations, which are the main source of errors in conventional digital microfluidic biochips, are completely eliminated, and hence neither sensing nor any correcting action is needed, and further, no management of intermediate waste droplets is needed. Additionally, the procedure can be fully parallelized for accurately producing multiple dilutions of a sample. Experimental results corroborate the superiority of the proposed method in terms of error management, as well as sample preparation cost and time.

DOI

10.1145/3414061

Publication Date

1-1-2021

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