Microfluidic mixing is a key process in miniaturized analysis system. Achieving adequate mixing performance is considerably difficult in microfluidic micromixer, as the flow is always associated with unfavorable laminar flow and dominated by molecular diffusion. The mixing performance of these micromixers are generally characterized as a function of mixing index based on dispersion (homogeneity) information, leading to either an overestimated or underestimated mixing index. This paper presents a new method for determining the mixing index of micromixers based on RGB color model by decoding mixing images to their respective red, green, and blue pixel intensities. Several digital composite images were used to perform initial benchmarking, and the proposed method accurately quantified the mixing index significantly better than previously adopted methods. The practicality of the method was further demonstrated by characterizing mixing of well‐known micromixers, namely T‐ and Y‐ micromixers, at varying Reynolds numbers of 5 ≤ Re ≤ 100. The results show that the mixing index decreases with the increase in Reynolds number, whereas the mixing index of the T‐micromixer was superior to that of Y‐micromixer, both agreeing well with the literature. The mixing index of these micromixers at varying Reynolds numbers of 5 ≤ Re ≤ 100 calculated using other methods were also compared and discussed. The proposed method is foreseen handy and robust in characterizing mixing in real time for gradient mixing in networked microchannels and multivortex mixing for the manipulation of fluids, particles, and biological substances.

中文翻译：

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RGB颜色模型确定微流体混合指数的方法

微流体混合是小型化分析系统中的关键过程。在微流体微混合器中，要获得足够的混合性能非常困难，因为流动总是与不利的层流相关联，并以分子扩散为主。这些微混合器的混合性能通常被表征为基于分散度（均质性）信息的混合指数的函数，导致混合指数被高估或低估。本文提出了一种通过将混合图像解码为各自的红色，绿色和蓝色像素强度来确定基于RGB颜色模型的微型混合器混合指数的新方法。几个数字合成图像用于执行初始基准测试，与以前采用的方法相比，所提出的方法对混合指数的准确定量显着更好。通过在5≤Re≤100的不同雷诺数下表征著名的微型混合器（即T和Y微型混合器）的混合特性，进一步证明了该方法的实用性。结果表明，混合指数随雷诺数的增加而降低，而T型微混合器的混合指数优于Y型微混合器，两者均与文献一致。还比较并讨论了使用其他方法计算的5≤Re≤100的雷诺数变化时这些微混合器的混合指数。预见到该方法既方便又健壮，可实时表征混合特性，用于在网络微通道中进行梯度混合，以及用于处理流体，颗粒，