While fitting the FCS curves, we observed that the stretching exponent (β) played a crucial role in achieving satisfactory results. Initially, we attempted to fit our FCS curves by constraining β to unity, but this approach compromised the quality of the fit. (Figure ). Thus, it became evident that fitting our FCS curves is unattainable without incorporating β into the fitting equations. The value of β varies between 0 and 1, depending on the distribution of τR, with a lower β value, suggesting a broader distribution of τR. We further observed that the value of τR is sensitive to the β value, which incorporates some uncertainty in τR. This uncertainty may hinder our ability to accurately discern the trend of τR with respect to FCS window size (Figure ). This issue could have been resolved by maintaining the same β value across all fittings. However, doing so would compromise the quality of the fit in several ). Through our second set of simulations, we confirmed that our derived τR values with similar β (~0.71-0.83) can effectively elucidate the trend of τR with FCS window size. Figure illustrates three sets of simulated curves, with each individual set comprising three curves featuring different β values varied within the range of 0.71 to 0.83 but sharing the same τR. However, the τR changes from one set to another from 1 ms to 3 ms and 10 ms. Here, we varied β (~0.71 to 0.83) based on our fitting results, which also exhibited a similar variation (Tables ). Interestingly, in this case, we could readily observe the trend of τR because all three curves with the same τR are distinctly separated from the other three curves with a different τR, even when the difference between these two τR values is only 2 ms. In our fittings, as β was varied within a narrow range from ~0.71 to 0.83, the resulting uncertainty in τR was not significant to impede the realization of relative change in the τR from one experiment to another. QDs and PNCs have baffled FCS by their intrinsic blinking properties. The blinking of QDs adheres to power law statistics, where the distributions of ON/OFF times were found to be contingent on excitation power. The key implication of power law statistics is that the average duration of ON/OFF states is influenced by the duration of experimental observation time (integration time), allowing blinking to manifest across all experimental time scales. As previously mentioned, the temporal responses of carrier dynamics in QDs cover a wide range, extending from sub-ns to several seconds or even longer. Processes like exciton formation, carrier recombination, and relaxation in QDs occur at a faster timescale (sub-ns), whereas carrier diffusion, Auger-related processes, carrier trapping/de-trapping, etc., occur at intermediate to slow timescales. All these processes participate in the QD blinking, leading to a distribution of blinking timescale. This further explains the origin of power-law statistics of QD blinking and the necessity of incorporating the stretching exponent parameter (β) in the FCS fitting equation. In a recent study, we examined the blinking behavior of PNCs and found that, like QDs, the blinking of d-PNC follows power law statistics. Since the average duration of ON/OFF states in power-law blinking is influenced by the experimental observation window, τR continues to increase as the observation window of FCS is extended. This work provides a rational understanding of carrier dynamics in 12 faceted PNCs.