In order to achieve highly sensitive, multiplexed bioassays, the number of channels and input solutions in a capillarity-driven device must be increased, which thereby also increases the risk of occurrence of malfunctions. Here, we present a method to prevent unwanted backflow, which is a critical problem that prevents the successful detection of target analytes in a capillarity network. Through the use of a linearized model of the network’s inlet pressures, we show that backflow originates from the difference in time constant ratios of the network’s inlet-channel elements. More importantly, we demonstrate that backflow can be prevented by sequentially increasing the time constants of the network elements where solution is injected. Also, we show that the initial inlet pressures do not influence the generation of backflow and only affect its magnitude. Similarly, by considering the fluidic conductances and time constants to be independent and setting the inlet radii as dependent parameters, we show that the fluidic conductance of each channel also contributes to the magnitude of the backflow without influencing its generation. We believe that the material presented here will be crucial for the control of capillarity in complex fluidic networks and in more sophisticated capillarity-driven bioassays.

为了实现高度灵敏的多重生物测定，必须增加毛细管驱动装置中通道和输入溶液的数量，这也增加了发生故障的风险。在这里，我们提出了一种防止不必要的回流的方法，这是一个严重的问题，阻碍了毛细管网络中目标分析物的成功检测。通过使用网络入口压力的线性化模型，我们表明回流源于网络入口通道元素的时间常数比率的差异。更重要的是，我们证明可以通过顺序增加注入溶液的网络元素的时间常数来防止回流。还，我们表明，初始入口压力不会影响回流的产生，而只会影响其大小。同样，通过将流体电导和时间常数视为独立参数并将入口半径设置为相关参数，我们表明，每个通道的流体电导也有助于回流的大小，而不会影响其产生。我们认为，此处介绍的材料对于控制复杂流体网络中的毛细管现象以及在更复杂的毛细管法驱动的生物测定中至关重要。