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Abstract: Microfluidic channels are at micrometer scales; thus, their fluid flows are laminar, resulting in the linear dependence of pressure drop on flow rate in the length of the channel. The ratio of the pressure drop to flow rate, referred to as resistance, depends on channel size and dynamic viscosity. Usually, a microfluidic chip is analogous to an electric circuit in design, but the design is adjusted to optimize channel size. However, whereas voltage loss is negligible at the nodes of an electric circuit, hydraulic pressure drops at the nodes of microfluidic chips by a magnitude are comparable to the pressure drops in the straight channels. Here, we prove by experiment that one must fully consider the pressure drops at nodes so as to accurately design a precise microfluidic chip. In the process, we numerically calculated the pressure drops at hydraulic nodes and list their resistances in the range of flows as concerned. We resorted to machine learning to fit the calculated results for complex junctions. Finally, we obtained a library of node resistances for common junctions and used them to design three established chips that work for single-cell analysis and for precision allocation of solutes (in gradient and averaging concentration microfluidic networks). Endothelial cells were stimulated by generating concentrations of adriamycin hydrochloride from the last two microfluidic networks, and we analyzed the response of endothelial cells. The results indicate that consideration of junction resistances in design calculation brings experimental results closer to the design values than usual. This approach may therefore contribute to providing a platform for the precise design of organ chips.

中国科学技术大学何立群等 | 基于微流控输运网络节点阻力特征的微流控芯片精准设计方法