Determining particle depth positions and evaluating dispersion using astigmatism PTV with a neural network

Area of Science:

• Fluid dynamics
• Microfluidics
• Biophysics

Background:

• Accurate particle tracking is crucial for understanding microfluidic phenomena.
• Traditional methods for determining particle depth positions can be complex and less precise.
• Astigmatism particle tracking velocimetry (APTV) offers potential for 3D particle localization but requires robust calibration.

Purpose of the Study:

• To develop and validate a novel calibration procedure for determining particle depth positions in microfluidic channels using APTV.
• To utilize a neural network model for calibrating APTV based on particle image geometry.
• To assess the efficiency of the developed calibration in analyzing microfluidic flows and particle behavior.

Main Methods:

• Development of a neural network model trained on geometrical parameters of distorted particle images.
• Implementation of astigmatism particle tracking velocimetry (APTV) for particle localization.
• Application of the calibrated APTV to study Poiseuille flow and particle dispersions in a microchannel.

Main Results:

• The calibration procedure successfully determined particle depth positions with an uncertainty of ±1µm.
• Analysis of Poiseuille flow demonstrated the method's capability in microfluidic studies.
• Particle position dispersion was correlated with the degree of particle image deformation and its deviation.

Conclusions:

• The developed neural network-based APTV calibration provides a precise method for measuring particle depth in microfluidics.
• The findings highlight the link between particle image deformation and position dispersion, offering insights into micro-scale particle dynamics.
• This technique enhances the study of particle behavior in microfluidic devices.