Deep Learning Based Surface Classification of Functionalized Polymer Coatings.

Area of Science:

• Materials Science
• Surface Chemistry
• Biotechnology

Background:

• Characterizing functionalized polymer surfaces is crucial for applications like medical implants and biosensors.
• Existing methods for surface analysis can be complex, slow, or lack scalability.
• There is a need for simple, rapid, and broadly applicable surface analysis techniques.

Purpose of the Study:

• To develop and validate a low-technology method for characterizing diverse polymer surfaces.
• To assess the efficacy of deep learning in analyzing protein stain patterns on polymer coatings.
• To demonstrate the versatility of the method for identifying unknown polymer surfaces.

Main Methods:

• Utilized chemical vapor deposition polymerization to create ten distinct polymer coatings with varying functional groups.
• Deposited a defined protein solution onto polymer surfaces to create stain patterns upon drying.
• Captured images of stain patterns using polarized light microscopy.
• Analyzed images with a deep-learning neural network (convolutional neural network, CNN).

Main Results:

• The CNN achieved 96% classification accuracy for known polymer coatings.
• The method successfully classified an unknown halogenated polymer coating with 95% accuracy.
• Protein stain pattern analysis via CNN proved highly reproducible despite subtle chemical differences.

Conclusions:

• The developed method offers a simple, fast, and scalable approach for polymer surface characterization.
• Deep learning analysis of protein stain patterns is a versatile tool applicable to a wide range of functionalized surfaces.
• This technique extends beyond pre-trained polymers, demonstrating its potential for novel material identification.