Chaotic mixing in liquid microdroplets

液体微滴的混沌混合

• 批准号: 0400370
• 负责人: Roman Grigoriev
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0400370&HistoricalAwards=false
• 关键词: Chaotic; mixing; liquid; microdroplets

This project explores basic mechanisms of introducing chaotic advection to enhance mixing in a hydrodynamic model of a nanoliter-scale batch reactor. A combined analytical, numerical and experimental approach will be used, which brings together two novel features: (1) a non-contact experimental method of precisely moving, merging, and mixing nanoliter-sized droplets using temperature-induced surface tension gradients, and (2) a convenient theoretical model based on the Stokes equation for the fluid coupled with the advection equation for the solute concentration that is suitable for making quantitative predictions of mixing behaviors observed in the experiments. The central focus of this research is on fundamental understanding of nonperturbative effects in time-dependent flow, which are most likely to lead to effective mixing in practical applications. The main idea of our approach is based on using the invariants, or functions preserved along the streamlines of the flow, which arise due to a high symmetry of typical flows in microdroplets. As streamlines cannot cross the invariant surfaces, destruction of all invariants is the key to achieving good mixing. The nonperturbative corrections that arise naturally from non-ideal aspects of any experimental implementation are therefore essential ingredients in the mixing process as they change the symmetry of the flow and thus affect the existence and number of invariants. The obtained results will be used to design an effective procedure for controlling mixing that should be applicable to a broad class of flows involving the mixing of discrete quantities of fluid.Miniaturization holds great promise for novel applications, such as chemical and biological sensors, drug discovery, and clinical tests, bringing radical improvements in speed, throughput, and sensitivity. This project focuses on the fundamental problem of fluid mixing, which plays a crucial role in most microfluidic technologies, yet becomes increasingly difficult at small scales. The study of chaotic mixing in liquid microdroplets is an integral part of a broader study of opto-microfluidics, a novel approach for handling liquids at the microscale using optical methods. This approach will allow construction of a new generation of microfluidic devices free of many drawbacks of conventional microchannel-based technology. Such highly integrated dynamically reprogrammable reusable devices could be used to design complete "labs-on-a-chip" with a potential to revolutionize the way chemical and biological assays are done.

本计画探讨在奈升规模间歇式反应器之流体力学模型中，引入混沌平流以增进混合之基本机制。 将使用分析、数值和实验相结合的方法，它汇集了两个新特征：（1）使用温度引起的表面张力梯度精确移动、合并和混合纳升尺寸的液滴的非接触实验方法，以及（2）一个方便的理论模型的基础上斯托克斯方程的流体耦合对流方程的溶质浓度，这是适合于使实验中观察到的混合行为的定量预测。 这项研究的中心重点是对时间相关流中的非微扰效应的基本理解，这最有可能导致在实际应用中的有效混合。 我们的方法的主要思想是基于使用的不变量，或功能保存沿着流线的流量，这是由于一个典型的微滴流的高度对称性。 由于流线不能穿过不变曲面，因此破坏所有不变量是实现良好混合的关键。 因此，从任何实验实施的非理想方面自然产生的非微扰修正是混合过程中的重要成分，因为它们改变了流动的对称性，从而影响了不变量的存在和数量。 所获得的结果将被用来设计一个有效的程序控制混合，应适用于广泛的一类涉及离散量的流体的混合流动，小型化具有很大的希望，为新的应用，如化学和生物传感器，药物发现，和临床试验，带来根本性的改善速度，吞吐量和灵敏度。 该项目的重点是流体混合的基本问题，这在大多数微流体技术中起着至关重要的作用，但在小尺度上变得越来越困难。 微液滴中混沌混合的研究是光微流体学更广泛研究的一个组成部分，光微流体学是一种使用光学方法在微尺度上处理液体的新方法。 这种方法将允许构建新一代的微流体装置，而没有传统的基于微通道的技术的许多缺点。 这种高度集成的动态可重编程可重复使用的设备可用于设计完整的“芯片实验室”，有可能彻底改变化学和生物测定的方式。