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Published on: August 28, 2015
Quantitative Analysis of Composite Polymeric Membrane Structure Using Contour Morphological Heterogeneity Functions
Georgy Rytikov1, Fedor Doronin1,2, Yuriy Rudyak1
1Faculty of Printing Industry, Moscow Polytechnic University, 107023 Moscow, Russia.
Polymers
|August 13, 2026
Summary
A new quantitative method uses computer analysis of scanning electron microscopy (SEM) images to characterize multilayer polymer membranes. This technique enables standardized analysis and automated metrological assessment of membrane architecture.
Area of Science:
- Materials Science
- Polymer Science
- Analytical Chemistry
Background:
- Membrane architecture significantly impacts separation performance.
- Accurate characterization of multilayer polymer membranes is crucial for optimizing their design and application.
- Current methods for analyzing membrane microstructure can be subjective and time-consuming.
Purpose of the Study:
- To develop a quantitative and automated method for characterizing the architecture of multilayer polymer membranes.
- To standardize the analysis of scanning electron microscopy (SEM) images of membranes.
- To enable precise determination of layer thicknesses within membrane structures.
Main Methods:
- Development of a quantitative method based on computer analysis of SEM images.
- Implementation of contour morphological heterogeneity function (CMHF) calculations.
- Application and validation of the method on polysulfone, polytetrafluoroethylene, and polyimide membranes.
Main Results:
- Successfully characterized the architecture of multilayer polymer membranes.
- Identified distinct structural zones (surface, transition, volumetric layers) within the membranes.
- Demonstrated the ability to determine layer thicknesses quantitatively.
Conclusions:
- The developed CMHF method provides a standardized and automated approach for SEM image analysis of polymer membranes.
- This technique allows for accurate metrological analysis of membrane structure and architecture.
- The algorithm facilitates computer vision-based characterization of filtering and separating equipment elements.
