Commissioning-oriented AFM-TERS workflow on planar gold thin films using benchmark dyes and near-/far-field...

Area of Science:

• Surface science and nanotechnology
• Chemical analysis and spectroscopy
• Materials science

Background:

• Tip-Enhanced Raman Spectroscopy (TERS) offers nanoscale chemical sensitivity but faces challenges like thermal drift and inconsistent quantification.
• Routine TERS application is hindered by factors including unstable optical coupling, heterogeneous hotspot formation, and variable signal measurement.

Purpose of the Study:

• To present a commissioning-oriented AFM-TERS workflow for planar gold thin films.
• To address and mitigate common limitations in TERS, such as thermal drift and optical coupling inconsistencies.
• To establish standardized approaches for signal quantification and interpretation in TERS.

Main Methods:

• Implemented a 20-minute thermal stabilization period to reduce lateral drift rates.
• Utilized camera-based scattering-footprint analysis for standardized optical coupling.
• Performed near-field mapping of Rhodamine 6G, Methylene Blue, and Crystal Violet with paired far-field controls.
• Investigated concentration-dependent measurements to assess local variability and hotspot dominance.

Main Results:

• Thermal stabilization reduced lateral drift by approximately 3.6×, enhancing platform stability.
• Standardized optical coupling served as an alignment proxy, not a direct hotspot measure.
• TERS enabled local identification of Rhodamine 6G at 10^-8 mol·L^-1, surpassing ensemble SERS limits.
• Significant local variability was observed, with far-field hotspots sometimes dominating the near-field response (FF > NF).

Conclusions:

• The developed AFM-TERS workflow improves platform stability and provides a standardized approach to optical coupling.
• TERS allows for nanoscale molecular identification at lower concentrations than ensemble SERS under specific conditions.
• Reporting paired near-field/far-field intensity ratios and differential signal metrics is recommended for robust quantification when enhancement factor assumptions are uncertain.