Microfluidic concentration gradient generators are essential in research as they create spatially resolved concentration values, allowing the screening of a wide range of concentrations at the same time. Tree-shaped gradient generators are commonly used due to their flexibility in concentration values and ability to maintain the gradient profile indefinitely. However, their design methods are not fully developed. Design automation is not widely used for microfluidic devices, and design tools are lacking. This is concerning as the design of gradient generators requires considering various parameters such as channel geometries, concentration values, fluid properties, and flow rates, which all influence each other. As a result, the design of the gradient generator is mainly done by hand, which is a very time-consuming task. In this work, Fink and colleagues addressed this problem by introducing a publicly available online tool capable of automatically designing the layout of concentration gradient generators that satisfy user-defined requirements.
The researchers suggest an approach using a tree-shaped structure with two inlets and multiple outlets. User-defined fluids are injected at the inlets, undergoing multiple mixing stages within the structure. The design involves layered channels directing fluids to meander channels, where they are mixed, creating intermediate concentration values. The final layer combines these intermediates to achieve the desired concentrations at the outlets. Unlike tree-shaped structures with uniform meander lengths, this model allows for varied concentration profiles, even with equal flow rates.
To design the gradient generator and achieve the desired concentration values, the researchers used the model proposed by Oh et al., suitable for laminar flow in microfluidic devices. The process involved four steps: defining basic parameters, computing flow rates within each layer, computing the required resistances for the meander channels, and determining channel lengths.
The method described above is available as an online tool. Users can generate the design of a gradient generator by inputting basic parameters like the channels' geometry, the fluids' viscosity, concentration values, flow rates, and number of outlets into an intuitive interface. This automated process helps eliminate manual errors and simplify the design step.
Quality of the results
Validation through simulation
The researchers employed OpenFOAM, a well-established Computational Fluid Dynamics (CFD) simulation tool, to validate the designs. This tool enables precise simulation of flow distributions and mixing processes within channels, allowing the verification of concentration values at the outlets. After completing the simulations, the researchers compared the desired concentration values with CFD results. They also calculated the minimal and maximal absolute errors.
Chip fabrication and concentration measurement
Besides running CFD simulations, the researchers generated designs with three and five outlets and measured their outcomes to determine the tool’s accuracy. This involved fabricating microfluidic devices made from poly(methyl methacrylate) (PMMA). The channel dimensions were set at 300µm width and 200µm height; these parameters were used as inputs in the design tool. The researchers then assessed the concentrations at device outlets through a colorimetric method. Following that, they injected two coloured test liquids at the inlets using ExiGo syringe pump at a flow rate of 5µL/min (also used as inputs to the proposed design tool). They took optical images and used hue values to map concentrations based on the calibration curve, enabling the correlation of measured hues to concentrations.
The main findings of these experiments were:
The measured concentrations were in agreement with the desired values that the proposed design tool was supposed to generate.
The maximum deviation of the fabricated devices from the desired value was 4.24%.
Overall, the evaluation using fabricated devices and measurements confirms the quality of the results obtained by the proposed approach.
How to get started?
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Fink, Gerold, et al. "Automatic design of microfluidic gradient generators." IEEE Access 10 (2022): 28155-28164.