Improving image contrast and material discrimination with nonlinear response in bimodal atomic force microscopy

Area of Science:

• Materials Science
• Nanotechnology
• Surface Science

Background:

• Atomic force microscopy (AFM) is crucial for nanoscale material analysis.
• Bimodal AFM enhances image contrast by using two cantilever eigenmodes.
• Analyzing heterogeneous materials with phase separation requires high image contrast.

Purpose of the Study:

• To investigate the potential of nonlinear response analysis in bimodal AFM for improved material contrast.
• To explore the simultaneous measurement of multiple frequencies for enhanced imaging.
• To demonstrate the efficacy of machine learning algorithms in analyzing nonlinear AFM data.

Main Methods:

• Extended bimodal atomic force microscopy operation.
• Excitation and measurement of two cantilever eigenmodes.
• Analysis of nonlinear responses at harmonic and mixing frequencies.
• Simultaneous measurement of amplitude and phase across 17 frequencies.
• Application of a machine-learning algorithm for data analysis.

Main Results:

• Nonlinear response analysis yields significantly greater image contrast compared to standard bimodal AFM.
• Simultaneous measurement of 17 frequencies in a single scan is feasible.
• A machine-learning algorithm demonstrated a nearly threefold improvement in separating polymer blend components using nonlinear response data.

Conclusions:

• Nonlinear response analysis in bimodal AFM offers superior material contrast for heterogeneous materials.
• This approach enables quantitative material property assessment at high speeds and enhanced resolution.
• Machine learning integration further boosts the analytical capabilities of advanced AFM techniques.