A Python Toolbox for Unbiased Statistical Analysis of Fluorescence Intermittency of Multilevel Emitters

Area of Science:

• Photophysics and Spectroscopy
• Quantum Dot Research
• Statistical Analysis in Physical Sciences

Background:

• Fluorescence intermittency, characterized by temporal variations in emission intensity and decay rates, is a common phenomenon in various single emitters, including organic fluorophores and quantum dots.
• Accurate statistical analysis of intermittency is crucial to prevent interpretation artifacts and understand the underlying photophysical processes.
• Existing methods may struggle with complex systems exhibiting numerous switching states, necessitating advanced analytical tools.

Purpose of the Study:

• To develop and present a Python toolbox for unbiased statistical analysis of single-emitter fluorescence intermittency.
• To provide a robust method for analyzing time-tagged single-photon detection data.
• To enable verification of hypothesized mechanistic intermittency models and assess conclusions drawn from complex photophysical systems.

Main Methods:

• Implementation of Bayesian changepoint analysis for detecting transitions in fluorescence intensity and decay rates.
• Application of level clustering algorithms to identify distinct emission states.
• Utilizing Monte Carlo simulations to illustrate and benchmark the statistical tools.

Main Results:

• The Python toolbox offers an unbiased approach to analyzing fluorescence intermittency data.
• Demonstrated the applicability of Bayesian changepoint analysis and level clustering to real experimental data.
• Monte Carlo analysis validated the tool's capability in characterizing complex photophysics, such as in perovskite quantum dots with multiple states.

Conclusions:

• The developed Python toolbox provides a reliable method for unbiased statistical analysis of single-emitter fluorescence intermittency.
• This tool is essential for accurate interpretation of photophysical properties and for validating mechanistic models.
• The approach is particularly valuable for studying complex systems like perovskite quantum dots with intricate switching behaviors.