Quantitative characterization of micromixing simulation

Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study

Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors. To account for these coupled effects, many microfluidic device designs and operating conditions must be considered and optimized in a time- and labour-intensive trial-and-error process. Computational modelling facilitates a systematic exploration of a large design parameter space via in silico simulations, but the majority of in silico models apply only a small set of conditions or parametric levels. Novel approaches to computational modelling are needed to explore large parameter spaces and accelerate the optimization of spheroid-on-a-chip and other organ-on-a-chip designs. Here, we report an efficient computational approach for simulating fluid flow and transport of drugs in a high-throughput arrayed cancer spheroid-on-a-chip platform. Our strategy combines four key factors: i) governing physical equations; ii) parametric sweeping; iii) parallel computing; and iv) extensive dataset analysis, thereby enabling a complete “full-factorial” exploration of the design parameter space in combinatorial fashion. The simulations were conducted in a time-efficient manner without requiring massive computational time. As a case study, we simulated >15,000 microfluidic device designs and flow conditions for a representative multicellular spheroids-on-a-chip arrayed device, thus acquiring a single dataset consisting of ∼10 billion datapoints in ∼95 GBs. To validate our computational model, we performed physical experiments in a representative spheroid-on-a-chip device that showed excellent agreement between experimental and simulated data. This study offers a computational strategy to accelerate the optimization of microfluidic device designs and provide insight on the flow and drug transport in spheroid-on-a-chip and other biomicrofluidic platforms.

Mixing in microfluidic devices and enhancement methods

Mixing in microfluidic devices presents a challenge due to laminar flows in microchannels, which result from low Reynolds numbers determined by the channel's hydraulic diameter, flow velocity, and solution's kinetic viscosity. To address this challenge, novel methods of mixing enhancement within microfluidic devices have been explored for a variety of applications. Passive mixing methods have been created, including those using ridges or slanted wells within the microchannels, as well as their variations with improved performance by varying geometry and patterns, by changing the properties of channel surfaces, and by optimization via simulations. In addition, active mixing methods including microstirrers, acoustic mixers, and flow pulsation have been investigated and integrated into microfluidic devices to enhance mixing in a more controllable manner. In general, passive mixers are easy to integrate, but difficult to control externally by users after fabrication. Active mixers usually take efforts to integrate within a device and they require external components (e.g. power sources) to operate. However, they can be controlled by users to a certain degree for tuned mixing. In this article, we provide a general overview of a number of passive and active mixers, discuss their advantages and disadvantages, and make suggestions on choosing a mixing method for a specific need as well as advocate possible integration of key elements of passive and active mixers to harness the advantages of both types.

Microfluidic mixing: a review

The aim of microfluidic mixing is to achieve a thorough and rapid mixing of multiple samples in microscale devices. In such devices, sample mixing is essentially achieved by enhancing the diffusion effect between the different species flows. Broadly speaking, microfluidic mixing schemes can be categorized as either “active”, where an external energy force is applied to perturb the sample species, or “passive”, where the contact area and contact time of the species samples are increased through specially-designed microchannel configurations. Many mixers have been proposed to facilitate this task over the past 10 years. Accordingly, this paper commences by providing a high level overview of the field of microfluidic mixing devices before describing some of the more significant proposals for active and passive mixers.

A simplified design of the staggered herringbone micromixer for practical applications

We demonstrated a simple method for the device design of a staggered herringbone micromixer (SHM) using numerical simulation. By correlating the simulated concentrations with channel length, we obtained a series of concentration versus channel length profiles, and used mixing completion length L(m) as the only parameter to evaluate the performance of device structure on mixing. Fluorescence quenching experiments were subsequently conducted to verify the optimized SHM structure for a specific application. Good agreement was found between the optimization and the experimental data. Since L(m) is straightforward, easily defined and calculated parameter for characterization of mixing performance, this method for designing micromixers is simple and effective for practical applications.