Abstract

Herein, a calibration procedure to determine the depth positions of particles in a microfluidic channel via astigmatism particle tracking velocimetry (APTV) has been described. A neural network model focusing on the geometrical parameters of distorted particle images was developed to calibrate APTV. To demonstrate the efficiency of this procedure, the Poiseuille flow and depth of the particles, and dispersions in the microchannel were studied. The depth positions were determined with an uncertainty of ${{\pm 1}}\;\unicode{x00B5}{\rm m}$. The present results suggest that the particle position dispersion could be a result of the degree of particle image deformation and its deviation.

© 2021 Optical Society of America

Scanning defocusing particle tracking for the experimental characterization of flows in demanding microfluidic systems

Quentin Galand, David Blinder, Pierre Gelin, Dominique Maes, and Wim De Malsche
Appl. Opt. 63(10) 2636-2642 (2024)

Distortion correction for particle image velocimetry using multiple-input deep convolutional neural network and Hartmann-Shack sensing

Zeyu Gao, Hannes Radner, Lars Büttner, Hongwei Ye, Xinyang Li, and Jürgen Czarske
Opt. Express 29(12) 18669-18687 (2021)

High-accuracy measurement of depth-displacement using a focus function and its cross- correlation in holographic PTV

Kyung Won Seo and Sang Joon Lee
Opt. Express 22(13) 15542-15553 (2014)

Macroscopic three-dimensional particle location using stereoscopic imaging and astigmatic aberrations

Thomas Fuchs, Rainer Hain, and Christian J. Kähler
Opt. Lett. 39(24) 6863-6866 (2014)

Holographic 3D particle reconstruction using a one-stage network

Yunping Zhang, Yanmin Zhu, and Edmund Y. Lam
Appl. Opt. 61(5) B111-B120 (2022)