In Situ Probing of Polymer Residence on the Nanoparticle Surface by Combining Deep Learning and Single-Molecule...

Area of Science:

• Polymer Science
• Materials Science
• Nanotechnology

Background:

• Understanding polymer-nanoparticle interactions is crucial in fields like drug delivery and coatings.
• Existing methods struggle to accurately quantify adsorption dynamics in complex polymer solutions.

Purpose of the Study:

• To develop a novel in situ method for analyzing polymer adsorption and desorption on nanoparticle surfaces.
• To validate the method's accuracy and efficiency compared to traditional techniques.

Main Methods:

• Utilizing single-molecule fluorescence tracking to monitor diffusion-state transitions.
• Employing a 1D convolutional neural network (1D-CNN) for adsorption-state identification.
• Validating the 1D-CNN model with numerically generated and experimental data.

Main Results:

• The 1D-CNN model achieved 85% accuracy and an F1 score of 0.86 in identifying adsorption states, even with a high diffusion coefficient ratio.
• The proposed method significantly outperformed hidden Markov models and threshold-based approaches.
• Polymer adsorption probability decreased with decreasing nanoparticle size, while adsorption duration increased.

Conclusions:

• The developed method provides a robust and accurate tool for studying polymer-nanoparticle interactions in situ.
• Findings reveal size-dependent adsorption dynamics of poly(ethylene glycol) on silica nanoparticles.
• This work advances the understanding of interfacial phenomena in polymer nanocomposites.