ForecastBased Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixingthensplitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probabilitybased sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor onchip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient.

Experimental Study on Microfluidic Mixing with Trapezoidal Obstacles in a 1000Fold Span of Reynolds Number Mixing is important for microfluidic systems and placing obstacles in a flow path for generating advection, or splittingrecombination flows, is a very popular method for enhancing the mixing. In this paper, we present experimental investigations on such microfluidic mixing with four different shapes of trapezoidal obstacles and tested them in a 1000fold span of Reynolds number (Re). The obstacles have four different base ratios 1, 3, 6 and 9, which indicate the ratios between the upper and lower bases of a trapezoid. Two different dyes, yellow and blue, were used for visualizing and evaluating the performance of mixing. The results show that, when the base ratio is 1, the mixing surprisingly becomes worse with the increase of the flowrate. When the ratio is 3 or 6, the mixing performance is first reduced with the increase of Re and then went through a transition around Re of 10 before it rapidly increases. When the ratio is 9, two transition points were found around Re of 10 and 100, respectively. The work provides useful information for realizing shapedependent mixing performance, as well as for developing an intelligent labonachip system.