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Updated: Jun 17, 2026

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
Published on: January 3, 2016
Chemically selective analysis of molecular monolayers by nonlinear optical stokes ellipsometry
Nathan J Begue1, Garth J Simpson
1Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette Indiana 47907, USA.
Nonlinear optical Stokes ellipsometry (NOSE) with principal component analysis (PCA) enables fast, selective chemical analysis of molecular monolayers. This technique accurately distinguishes between different dyes, paving the way for NOSE microscopy.
Area of Science:
- Materials Science
- Analytical Chemistry
- Optics
Background:
- Molecular monolayers require precise chemical analysis.
- Existing methods for analyzing thin films can be time-consuming.
- Nonlinear optical (NLO) techniques offer surface sensitivity.
Purpose of the Study:
- To demonstrate the application of nonlinear optical Stokes ellipsometry (NOSE) coupled with principal component analysis (PCA) for chemically selective analysis.
- To validate the NOSE-PCA method for molecular monolayers.
- To showcase the potential for rapid and precise surface analysis.
Main Methods:
- Utilizing nonlinear optical Stokes ellipsometry (NOSE) for rapid polarization measurements of NLO materials and thin films.
- Applying principal component analysis (PCA) combined with linear curve fitting for accelerated data analysis.
- Validating the methodology with z-cut quartz and four thin dye films.
Main Results:
- NOSE-PCA significantly reduces analysis time compared to traditional nonlinear curve fitting.
- The technique achieves high precision, enabling discrimination between dyes with similar NLO properties.
- Successful chemical selectivity was demonstrated for molecular monolayers.
Conclusions:
- NOSE-PCA is a powerful tool for rapid and chemically selective analysis of molecular monolayers.
- The method offers a practical approach for surface analysis and microscopy.
- This technique advances the field of thin film characterization.
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