A Python toolbox for unbiased statistical analysis of fluorescence intermittency of multi-level emitters

TL;DR

This paper introduces a Python toolbox for unbiased statistical analysis of fluorescence intermittency in single emitters, enabling more accurate interpretation of complex photophysical behaviors across various nanomaterials.

Contribution

It provides novel Bayesian changepoint analysis and level clustering tools tailored for single-photon detection data, facilitating mechanistic insights into complex intermittency phenomena.

Findings

Validated methods with Monte Carlo simulations

Demonstrated analysis on perovskite quantum dots

Benchmarking of statistical inference accuracy

Abstract

We report on a Python-toolbox for unbiased statistical analysis of fluorescence intermittency properties of single emitters. Intermittency, i.e., step-wise temporal variations in the instantaneous emission intensity and fluorescence decay rate properties are common to organic fluorophores, II-VI quantum dots and perovskite quantum dots alike. Unbiased statistical analysis of intermittency switching time distributions, involved levels and lifetimes is important to avoid interpretation artefacts. This work provides an implementation of Bayesian changepoint analysis and level clustering applicable to time-tagged single-photon detection data of single emitters that can be applied to real experimental data and as tool to verify the ramifications of hypothesized mechanistic intermittency models. We provide a detailed Monte Carlo analysis to illustrate these statistics tools, and to benchmark the extent to which conclusions can be drawn on the photophysics of highly complex systems, such as perovskite quantum dots that switch between a plethora of states instead of just two.