Depth-profiling by confocal Raman microscopy (CRM): data correction by numerical techniques.

Area of Science:

• Materials Science
• Analytical Chemistry
• Optical Physics

Background:

• Confocal Raman microscopy (CRM) depth profiling with dry optics suffers from distortions like compressed depth scales.
• These distortions arise from combined diffraction, refraction, and instrumental effects.
• Accurate depth profiling is crucial for understanding material interfaces and properties.

Purpose of the Study:

• To explore regularized deconvolution and depth scale rescaling for improving CRM depth profiling accuracy.
• To evaluate these methods for correcting experimental data from polymer interfaces.
• To assess the potential of water immersion objectives in reducing optical distortions.

Main Methods:

• Regularized deconvolution based on a predictive depth resolution model.
• Computer simulations of smooth and sharp material transitions.
• Application of methods to experimental data from a polymer interface.
• Evaluation of depth scale rescaling and water immersion objectives.

Main Results:

• Regularized deconvolution effectively recovers lost profile features from sharp and smooth transitions.
• Depth scale rescaling is limited to smooth transitions near the surface.
• Water immersion objectives show potential for reducing distortions and expanding the applicability of simple rescaling.

Conclusions:

• Regularized deconvolution is a robust method for correcting CRM depth profiling distortions.
• Simple rescaling is useful for smooth transitions, especially with water immersion objectives.
• These improved methods enhance the noninvasive monitoring of processes like Fickean sorption in polymers.