Generalizable Framework for Algorithmic Interpretation of Thin Film Morphologies in Scanning Probe Images.

Area of Science:

• Materials Science
• Data Science
• Image Analysis

Background:

• Scanning probe microscopy (SPM) generates complex images of material surfaces.
• Analyzing the morphology of π-conjugated polymers (CPs) is crucial for understanding their properties.
• Existing methods for SPM image analysis can be limited in scope and adaptability.

Purpose of the Study:

• To introduce an open-source Python library, Materials Morphology Python (m2py), for analyzing SPM images.
• To demonstrate the library's workflow using nanostructured CPs.
• To enable quantitative description and comparison of morphological domains.

Main Methods:

• Signal enhancement of SPM data.
• Principal Component Analysis (PCA) for dimensionality reduction.
• Gaussian Mixture Model (GMM) for image segmentation into phases.
• Connected components labeling or persistence watershed segmentation for domain identification.

Main Results:

• The m2py library successfully segments SPM images of CPs into distinct phases.
• Morphological domains are accurately labeled and identified.
• The workflow provides quantitative descriptors of domain size, distribution, and shape.

Conclusions:

• m2py offers a widely adaptable tool for analyzing SPM-derived material morphology.
• The library facilitates detailed, quantitative characterization of nanostructured materials.
• Researchers can use m2py to precisely analyze and compare morphological features across samples.