Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning.

Area of Science:

• Materials Science
• Nanotechnology
• Dielectric Spectroscopy

Background:

• Characterizing nanoscale dielectric properties of interfacial regions in polymer nanocomposites is challenging.
• Electrostatic force microscopy (EFM) offers local dielectric measurements but struggles with complex interphase geometries.
• Understanding these properties is critical for predicting and controlling overall material behavior.

Purpose of the Study:

• To develop and demonstrate a combined EFM and machine learning (ML) approach for measuring interfacial permittivity.
• To accurately quantify the dielectric properties of the interphase region in polymer nanocomposites at the nanoscale.
• To overcome limitations of previous analytical methods in complex interphase geometries.

Main Methods:

• Utilized a combination of Electrostatic Force Microscopy (EFM) and Machine Learning (ML).
• Trained ML models on finite-element simulations of electric field profiles between the EFM tip and nanocomposite surface.
• Applied the method to measure interfacial permittivity in 50 nm silica particles within a PMMA matrix.

Main Results:

• The ML-EFM approach accurately determined interface permittivity for functionalized nanoparticles.
• An extrinsic interfacial region was detected for silica particles with a polyaniline brush layer.
• For bare silica particles, the intrinsic interface was detectable, showing slight permittivity variations.

Conclusions:

• The developed ML-EFM method provides a pathway to quantify nanoscale interface dielectric properties.
• This approach accounts for complex interactions between filler, matrix, and interface permittivity, surpassing previous methods.
• Enables the design and optimization of nanodielectric materials by precisely characterizing interfacial dielectric behavior.