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Automatic Droplet Sequence Generation for Microfluidic Networks With Passive Droplet Routing

机译:具有无源液滴路由的微流体网络的自动液滴序列生成

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Droplet-based microfluidic devices are a well-established and highly potential Labs-on-Chip technology as droplets are especially suited to encapsulate biological samples like cells, proteins, or DNA. These droplets are injected in a continuous phase and flow through closed microchannels to modules executing operations on the droplets-eventually realizing a (bio-)chemical experiment. Moreover, this technology even allows for the realization of multiple experiments on a single device by letting droplets take different paths through the microfluidic network. This requires, however, a mechanism to route the droplets along these paths. To this end, the concept of passive droplet routing has been suggested which entirely avoids complex valves or switches and, instead, realizes the routing by exploiting the hydrodynamic effect that a droplet will always flow along the path with the highest volumetric flow rate. Since droplets themselves affect the volumetric flow rate, a dedicated sequence of droplets can define what path is taken and, hence, what experiment is executed. However, determining such a droplet sequence is a nontrivial task, as it is nonobvious how much droplets are needed, when to inject them, and how they are interacting. In this paper, we are addressing this issue by providing, for the first time, an automatic method for the generation of droplet sequences realizing the desired experiments on a given network. Evaluations confirm the practicability of the proposed solution. Moreover, the suitability of the obtained droplet sequences is additionally validated through simulations on the 1D analysis model.
机译:基于液滴的微流体装置是一种良好的良好且高潜在的水库技术,因为液滴特别适合于封装细胞,蛋白质或DNA等生物样品。这些液滴在连续相位中注入并通过封闭的微通道流过于执行在液滴上的操作的模块 - 最终实现(生物)化学实验。此外,该技术甚至允许通过让液滴通过微流体网络进行不同的路径来实现单个设备上的多个实验。然而,这需要一种机制来沿着这些路径路由液滴。为此,已经提出了被动液滴路由的概念,其完全避免了复杂的阀门或开关,而是通过利用流体动力学效果来实现路由,使得液滴始终沿着具有最高体积流量的路径流动的流体动力学效果。由于液滴本身影响体积流量,因此专用的液滴序列可以定义所拍摄的路径,因此执行实验。然而,确定这样的液滴序列是非活动任务,因为它是不可吸取的,何时注射它们的液滴,以及它们是如何交互的。在本文中,我们首次提供了用于生成实现给定网络上所需实验的液滴序列的自动方法来解决这个问题。评估证实了所提出的解决方案的实用性。此外,通过对1D分析模型的模拟还通过模拟验证所得液滴序列的适用性。

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