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首页> 外文期刊>Lab on a chip >Finding the optimal design of a passive microfluidic mixer
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Finding the optimal design of a passive microfluidic mixer

机译：找到无源微流体混合器的最佳设计

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摘要

The ability to thoroughly mix two fluids is a fundamental need in microfluidics. While a variety of different microfluidic mixers have been designed by researchers, it remains unknown which (if any) of these mixers are optimal (that is, which designs provide the most thorough mixing with the smallest possible fluidic resistance across the mixer). In this work, we automatically designed and rationally optimized a microfluidic mixer. We accomplished this by first generating a library of thousands of different randomly designed mixers, then using the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the random chips in order to achieve Pareto efficiency. Pareto efficiency is a state of allocation of resources (e.g. driving force) from which it is impossible to reallocate so as to make any one individual criterion better off (e.g. pressure drop) without making at least one individual criterion (e.g. mixing performance) worse off. After 200 generations of evolution, Pareto efficiency was achieved and the Pareto-optimal front was found. We examined designs at the Pareto-optimal front and found several design criteria that enhance the mixing performance of a mixer while minimizing its fluidic resistance; these observations provide new criteria on how to design optimal microfluidic mixers. Additionally, we compared the designs from NSGA-II with some popular microfluidic mixer designs from the literature and found that designs from NSGA-II have lower fluidic resistance with similar mixing performance. As a proof of concept, we fabricated three mixer designs from 200 generations of evolution and one conventional popular mixer design and tested the performance of these four mixers. Using this approach, an optimal design of a passive microfluidic mixer is found and the criteria of designing a passive microfluidic mixer are established.
机译：彻底混合两种流体的能力是微流体的基本需求。虽然由研究人员设计了各种不同的微流体混合器，但它仍然未知，这些混合器中的哪种（如果有的话）是最佳的（即，哪些设计提供最彻底的混合在混合器上的最小可能的流体阻力）。在这项工作中，我们自动设计和合理优化了微流体搅拌机。我们通过首先使用非主导的分类遗传算法II（NSGA-II）来完成数千个不同随机设计的混合器的库来实现这一目标，以优化随机芯片以实现静脉效率。帕累托效率是资源分配的状态（例如驱动力），从中无法重新分配，以便使任何一个单独的标准更好地关闭（例如压降）而不使至少一个单独的标准（例如混合性能）更糟糕。经过200代的演化后，达到帕累托效率，发现了帕累托最佳的前锋。我们在帕累托 - 最佳前部检查了设计，发现了几种设计标准，可增强混合器的混合性能，同时最小化其流体抗性;这些观察结果提供了如何设计最佳的微流体混合器的新标准。此外，我们将NSGA-II的设计与来自文献的一些流行的微流体混合器设计进行了比较，发现来自NSGA-II的设计具有较低的流体抗性，具有类似的混合性能。作为概念的证据，我们制造了三种搅拌机​​设计，从200代演化和传统的流行搅拌机设计中进行了测试，并测试了这四种混合器的性能。使用这种方法，发现了无源微流体混合器的最佳设计，建立了设计被动微流体混合器的标准。

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