Quantitative Nanometrology of Binary Particle Systems Using Fluorescence Recovery after Photobleaching: Application...

Area of Science:

• Materials Science
• Nanotechnology
• Physical Chemistry

Background:

• Accurate characterization of nanoparticle size is crucial for understanding their behavior in various systems.
• Distinguishing between nanoparticles of different sizes and free dye molecules presents analytical challenges.

Purpose of the Study:

• To apply Fluorescence Recovery After Photobleaching (FRAP) for precise nanoparticle size determination in binary mixtures.
• To investigate the utility of machine learning for enhancing FRAP data analysis.
• To validate experimental findings with theoretical simulations.

Main Methods:

• Utilized Fluorescence Recovery After Photobleaching (FRAP) to analyze nanoparticle systems.
• Employed a biexponential model for initial size determination and a triexponential model after machine learning preprocessing.
• Incorporated machine learning (gradient boosting) for data preprocessing.
• Performed molecular dynamics simulations to study dye adsorption to silica nanoparticles.

Main Results:

• Successfully demonstrated the ability to measure individual nanoparticle sizes in binary systems using FRAP.
• Achieved accurate size determination of 6 nm LUDOX HS40 and 11 nm LUDOX AS40 nanoparticles, along with free R6G dye.
• Showcased improved accuracy in resolving multiple components through machine learning-assisted analysis.
• Molecular dynamics simulations confirmed size-dependent adsorption of R6G to silica nanoparticles.

Conclusions:

• FRAP, enhanced by machine learning, is a robust method for sizing nanoparticles in complex mixtures.
• The study accurately quantified nanoparticle sizes and identified components in a binary system.
• Experimental results align with theoretical predictions regarding dye-nanoparticle interactions.