Monitoring of random microvessel network formation by in-line sensing of flow rates: A numerical and in vitro investigation

• Published in: Sensors and actuators. A. Physical. 2021-11, Vol.331, p.112970, Article 112970
• Main Authors: Pozdin, Vladimir A., Erb, Patrick D., Downey, McKenna, Rivera, Kristina R., Daniele, Michael
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Construction engineering; Cross-sections; Equivalent circuit; Equivalent circuits; Flow velocity; Fluid dynamics; Fluid flow; Fluid mechanics; Fluid networks; Heat transfer; Microfluidics; Microphysiological systems; Microvessels; Monitoring; Network formation; Neural networks; Physiological effects; Physiology; Porosity; Vascular mechanics
• ISSN: 0924-4247; 1873-3069
• DOI: 10.1016/j.sna.2021.112970

Non-invasive, non-destructive characterization microscale fluid networks is achieved via in-line sensing of fluid flow rate, quantifying the hydrodynamic resistance of the network, and correlating this measurement to model the progression of microvessel formation and connectivity. [Display omitted] •The design and strategy for correlating the fluid flow across a microfluidic network to network geometry is described.•Integrated low rate sensors correlate the hydrodynamic resistance to microfluidic network geometry.•Numerical model is compared to custom microdevices were designed and fabricated to demonstrate this principle. The directed or de novo formation of microvasculature in engineered tissue constructs is essential for accurately replicating physiological function. A limiting factor of a system relying on spontaneous microvessel formation is the inability to precisely quantify the development of the microvascular network and control fluid moving through formed vessels. Herein, we report a strategy to monitor the dynamic formation of microscale fluid networks, which can be translated to the monitoring of microvasculature development in engineered tissue constructs. The non-invasive, non-destructive monitoring and characterization of the fluid network is achieved via in-line sensing of fluid flow rate and correlating this measurement to the hydrodynamic resistance of the fluid network to model the progression of microvessel formation and connectivity. Computational fluid dynamics, equivalent circuit, and experimental models were compared, which simulated multi-generational branching or splitting microvessel networks. The networks simulated vessels with varying cross-sectional area, up to 16 branching vessels, and microvessel network volume ranging from ˜20−30 mm3. In all models, the increasing degree of network complexity and volume corresponded to a decrease in jumper flow-rate measured; however, vessel cross-section also impacted the measured jumper flow rate, i.e. at low vessel height (200 μm) the response was dominated by resistance of narrow channels. An approximately 2% error was exhibited between the models, which was attributed to variation in the geometry of the fabricated models and illustrates the potential to precisely and non-destructively monitor microvessel network development and volumetric changes.