Artificial Intelligence Clustering Approach for Force Mapping Analysis of Polyacrylic Acid (PAA)/Polyethylene Oxide...

Area of Science:

• Materials Science
• Surface Chemistry
• Biotechnology

Background:

• Biosensor biocompatibility is crucial for effective analyte detection and unwanted entity repulsion.
• Weak polyelectrolyte polymer brushes offer tunable surface properties by responding to external stimuli like pH and ionic strength.
• Understanding polymer brush conformation is key to designing advanced biosensing platforms.

Purpose of the Study:

• To characterize the conformation of polyacrylic acid (PAA) and polyethylene oxide (PEO) polymer brushes.
• To investigate the influence of pH and ionic strength on polymer brush behavior.
• To apply Artificial Intelligence (AI) for analyzing mixed polymer brush systems.

Main Methods:

• Atomic Force Microscopy (AFM) in spectroscopic mode was utilized to measure polymer brush conformation.
• Force curves were obtained using conventional tips for enhanced sensitivity to ion-polymer interactions.
• Statistical analysis and an AI clustering model were employed to interpret large datasets of force curves, particularly for mixed PAA/PEO brushes.

Main Results:

• Neutral polyethylene oxide (PEO) brushes showed no significant changes with varying pH and salinity.
• Polyacrylic acid (PAA) brushes exhibited pH-dependent behavior: shrinking at low pH (H3O+) and swelling with Na+ and K+ ions.
• The AI model successfully differentiated between PAA and PEO domains within a 50% PAA/50% PEO mixed brush.

Conclusions:

• PAA and PEO polymer brushes display distinct responses to environmental stimuli, offering tunable surface properties for biosensor applications.
• AFM combined with AI provides a powerful approach for characterizing complex polymer brush systems.
• This study advances the design of biocompatible surfaces for sophisticated biosensing technologies.