Presentation + Paper
19 February 2018 Identification of hydrodynamic forces around 3D surrogates using particle image velocimetry in a microfluidic channel
Author Affiliations +
Abstract
Previous studies have demonstrated that flow-induced shear stress induces a motile and aggressive tumor phenotype in a microfluidic model of 3D ovarian cancer. However, the magnitude and distribution of the hydrodynamic forces that influence this biological modulation on the 3D cancer nodules are not known. We have developed a series of numerical and experimental tools to identify these forces within a 3D microchannel. In this work, we used particle image velocimetry (PIV) to find the velocity profile using fluorescent micro-spheres as surrogates and nano-particles as tracers, from which hydrodynamic forces can be derived. The fluid velocity is obtained by imaging the trajectory of a range of florescence nano-particles (500–800 μm) via confocal microscopy. Imaging was done at different horizontal planes and with a 50 μm bead as the surrogate. For an inlet current rate of 2 μl/s, the maximum velocity at the center of the channel was 51 μm/s. The velocity profile around the sphere was symmetric which is expected since the flow is dominated by viscous forces as opposed to inertial forces. The confocal PIV was successfully employed in finding the velocity profile in a microchannel with a nodule surrogate; therefore, it seems feasible to use PIV to investigate the hydrodynamic forces around 3D biological models.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sepideh Afshar, Shubhankar Nath, Utkan Demirci, Tayyaba Hasan, Giuliano Scarcelli, Imran Rizvi, and Walfre Franco "Identification of hydrodynamic forces around 3D surrogates using particle image velocimetry in a microfluidic channel", Proc. SPIE 10491, Microfluidics, BioMEMS, and Medical Microsystems XVI, 1049103 (19 February 2018); https://doi.org/10.1117/12.2290372
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KEYWORDS
Tumors

Microfluidics

3D modeling

Particles

Fluid dynamics

Confocal microscopy

Particle image velocimetry

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