Single particle raster image analysis of diffusion for particle mixtures

Area of Science:

• Biophysics
• Chemical Physics
• Optical Microscopy

Background:

• Raster Image Correlation Spectroscopy (RICS) is a technique for analyzing diffusion in images.
• Single Particle Raster Image Analysis (SPRIA) offers an alternative by analyzing individual particles.
• Analyzing mixtures of particles presents challenges for diffusion analysis.

Purpose of the Study:

• To extend the Single Particle Raster Image Analysis (SPRIA) method for analyzing particle mixtures.
• To compare the effectiveness of SPRIA and RICS for analyzing mixtures with varying diffusion coefficients.
• To develop a method for determining the number of distinct diffusion coefficients in a sample.

Main Methods:

• Extension of SPRIA to analyze mixtures of particles with multiple diffusion coefficients.
• Application of maximum likelihood estimation for individual particle diffusion coefficient determination.
• Analysis of simulated and experimental data with two and three diffusion coefficients.
• Investigation of Raster Image Correlation Spectroscopy (RICS) for mixture analysis by plotting correlation function level curves.

Main Results:

• SPRIA accurately estimates diffusion coefficients and their proportions in mixtures.
• A simple technique for determining the number of diffusion coefficients was proposed.
• RICS requires a significant quotient between diffusion coefficients to distinguish between one and two components.
• A minor correction to the RICS autocorrelation function was described.

Conclusions:

• SPRIA is a robust method for analyzing particle mixtures with different diffusion coefficients.
• The study provides insights into the limitations of RICS for analyzing complex mixtures.
• The findings contribute to improved quantitative analysis in dynamic imaging techniques.