Multicolor single-molecule imaging is widely applied to answer questions in biology and materials science. However, most studies rely on spectrally distinct fluorescent probes or time-intensive sequential imaging strategies to multiplex. Here, we introduce blinking-based multiplexing (BBM), a simple approach to differentiate spectrally overlapped emitters based solely on their intrinsic blinking dynamics. The blinking dynamics of hundreds of rhodamine 6G and CdSe/ZnS quantum dots on glass are obtained using the same acquisition settings and analyzed with a change point detection algorithm. Although substantial blinking heterogeneity is observed, the analysis yields a blinking metric with 93.5% classification accuracy. We further show that BBM with up to 96.6% accuracy is achieved by using a deep learning algorithm for classification. This proof-of-concept study demonstrates that a single emitter can be accurately classified based on its intrinsic blinking dynamics and without the need to probe its spectral color.

By removing the effects of ensemble averaging and molecular aggregation, we untangle the factors that govern the dispersive electron transfer kinetics of eosin-sensitized TiO 2 , focusing on the impact of environmental heterogeneity versus injection from multiple excited states. The blinking dynamics of single eosin Y chromophores on nanocrystalline TiO 2 films are analyzed using a change point detection algorithm for binned data. Robust statistical analysis based on maximum likelihood estimation, Kolmogorov−Smirnov tests, and log likelihood ratio tests is used to determine the functional form that best fits the resulting on-and off-time distributions and to distinguish between mechanisms for dispersive electron transfer. Using this approach, we find that the on-and off-time distributions for eosin Y on TiO 2 are best fit to lognormal distributions corresponding to μ on = −0.64 ± 0.04, σ on = 1.52 ± 0.02, μ off = −0.23 ± 0.04, and σ off = 1.96 ± 0.03, respectively. Monte Carlo simulations based on the Albery model for dispersive electron transfer (i.e., where the median rate constant κ is modified by the exponential of a parameter, x, that is normally distributed, k = κ e −γx ) successfully reproduce this behavior using a median rate constant for injection and back electron transfer of ∼10 10 and ∼10 4 s −1 , respectively, and a corresponding energetic dispersion, γ, of ∼200−350 meV. To examine how injection from both the singlet and triplet excited states contributes to this dispersion, we studied two rhodamine sensitizers, R123 and 5ROX, that inject only from their singlet excited state. Surprisingly, when access to the T 1 state is minimized in going from EY to R123 and 5ROX, kinetic dispersion actually increases. Collectively, these observations support the interpretation that static and dynamic inhomogeneities at the EY−TiO 2 interface govern kinetic dispersion, with dynamic fluctuations in binding configuration and/or vibrational motion playing a decisive role.

Although single-molecule imaging is widely applied in biology and materials science, most studies are limited by their reliance on spectrally distinct fluorescent probes. We recently introduced blinking-based multiplexing (BBM), a simple approach to differentiate spectrally overlapped single emitters based solely on their intrinsic blinking dynamics. The original proof-of-concept study implemented two methods for emitter classification: an empirically derived metric and a deep learning algorithm, both of which have significant drawbacks. Here, a multinomial logistic regression (LR) classification is applied to rhodamine 6G (R6G) and CdSe/ZnS quantum dots (QDs) in various experimental conditions (i.e., excitation power and bin time) and environments (i.e., glass versus polymer). We demonstrate that LR analysis is rapid and generalizable, and classification accuracies of 95% are routinely observed, even within a complex polymer environment where multiple factors contribute to blinking heterogeneity. In doing so, this study (1) reveals the experimental conditions (i.e., P exc = 1.2 μW and t bin = 10 ms) that optimize BBM for QD and R6G and (2) demonstrates that BBM via multinomial LR can accurately classify both emitter and environment, opening the door to new opportunities in single-molecule imaging.