Automatische Charakterisierung von Strömungsregimen in einem Mikrokanal

Translated title of the contribution: Automatic Characterization of Droplet Flow Regimes in Microfluidic T-Junctions
  • Nico Rathmayr

Research output: ThesisMaster's / Diploma thesis

Abstract

The generation and control of droplets in microfluidic systems is a central topic in modern lab-on-a-chip technologies.
In particular, T-junction structures have proven to be versatile and well-studied geometries, as they enable reproducible
droplet formation under defined conditions. However, precise characterization and classification of the occurring flow regimes
is essential for many applications, such as biomedical diagnostics or chemical reaction studies on the microscale. Existing methods often require complex optical systems for observation, which complicates integration into compact, portable platforms.
The aim of this work is therefore to develop a method for automated, sensor-based analysis of flow regimes in a microfluidic
T-junction, thereby creating a foundation for flexible, cost-efficient, and user-friendly applications.

The automatic characterization of flow regimes is carried out through the use of capacitive sensing in combination with
continuous signal analysis. This approach not only enables continuous measurement and classification of the flow regimes, but also provides a practical user interface that integrates both control and visualization. Three embedded electrode pairs
deliver the raw signals, from which key parameters such as droplet sequence, droplet velocity, and droplet length are extracted.
These parameters form the basis for classification into the prevailing flow regime, whose occurrence depends largely on the
geometry of the T-junction, the absolute flow rates, and their ratio.

To represent and analyze the operating behavior, a so-called regime map is used. Measurement points are plotted on this map in such a way that the entire operating window of the T-junction is captured. The raw signals pass through a series of preprocessing
steps, followed by targeted feature extraction that takes into account parameters such as time intervals, pulse widths, amplitudes, and characteristic shape features. Classification is then performed using a hybrid decision logic that combines fast decisions with more robust methods, ensuring reliable and differentiated discrimination of regimes.

In addition, a cross-platform, intuitive graphical user interface (GUI) has been developed, which combines all essential functions in a compact application. These include pump control, measurement control, visualization of the three electrode channels, as well as automatic edge detection. Furthermore, calculated parameters such as droplet velocity and droplet length are displayed. The flow
regime determined from the measurement data is also visualized graphically, providing users with direct feedback on the current operating state. Accurate prediction, detection, and classification of flow regimes in a given T-junction thus opens up new possibilities for precise and flexible droplet generation, particularly in lab-on-a-chip applications.

Keywords: Microfluidics, Flow regimes, Capacitive sensing, Droplet generation, Visualization
Translated title of the contributionAutomatic Characterization of Droplet Flow Regimes in Microfluidic T-Junctions
Original languageGerman (Austria)
QualificationMaster
Awarding Institution
  • Johannes Kepler University Linz
Supervisors/Reviewers
  • Da Silva, Marco, Supervisor
  • Tröls, Andreas, Supervisor
Award date08 Oct 2025
Publication statusPublished - 08 Oct 2025

Fields of science

  • 202016 Electrical engineering
  • 202015 Electronics
  • 202012 Electrical measurement technology
  • 202027 Mechatronics
  • 202037 Signal processing

JKU Focus areas

  • Digital Transformation

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