Rapid broadband discrete nanomechanical mapping of soft samples on atomic force microscope

Area of Science:

• Materials Science
• Nanotechnology
• Surface Science

Background:

• Nanomechanical mapping (NM) is crucial for understanding soft sample dynamics.
• Conventional NM methods are slow due to continuous scanning limitations.
• Faster NM is required to capture dynamic material property changes.

Purpose of the Study:

• To develop a rapid, broadband discrete nanomechanical mapping technique for soft samples.
• To overcome the throughput limitations of conventional continuous scanning NM.
• To enable efficient investigation of dynamic nanomechanical distributions.

Main Methods:

• Utilizing discrete mapping to reduce the number of measurement locations.
• Employing an online-searching learning-based technique for rapid probe engagement/withdrawal.
• Implementing control-based nanoindentation for broad-frequency nanomechanical property acquisition.
• Exploring a decomposition-based learning approach for swift probe transitions between locations.

Main Results:

• Demonstrated a significant increase in mapping speed compared to conventional methods.
• Successfully mapped nanomechanical properties of Polydimethylsiloxane (PDMS) and PDMS-epoxy samples.
• Acquired broadband nanomechanical data at discrete locations efficiently.

Conclusions:

• The developed discrete NM approach enables rapid and efficient characterization of soft materials.
• This technique overcomes throughput limitations, allowing for dynamic analysis.
• The method is validated for complex soft sample systems.