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Beyond power laws: A new approach for analyzing single molecule photoluminescence intermittency
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View: Figures


Image of FIG. 1.
FIG. 1.

On- and off-event duration histograms for 220 CdSe/CdS quantum QDs in PMMA. (a-b). Raw PI histograms presented on a log-log plot. The tail of the data is fanned, representing sparse events. (c-d). Each point in the raw histogram is divided by the average time to the nearest neighbors, resulting in a continuous probability density. The data in the right are fitted with linear least squares regression to obtain the power-law exponent. (e-f) CDFs generated from the data in (a) and (b), the CDF is a naturally decaying distribution that can be fitted and differentiated to generate the PDF without the need to smooth the data. The curvature of the CDF indicates that the data are not consistent with power-law.

Image of FIG. 2.
FIG. 2.

An illustration of the process by which simulated data are created. Simulated data sets for the power-law are generated by a uniform sampling of the experimental data below t min with probability 1-n tail /n (shaded region), and randomly from a power-law (or other PDF) employing best fit parameters determined using maximum likelihood estimation with sampling probability n tail /n.

Image of FIG. 3.
FIG. 3.

(Left) Best fits of the 0.5 μW data to power-law(-·-), lognormal(-), and Weibull(·) PDFs for 220 CdSe/CdS quantum dots in PMMA for on-times (a) and off times (b). None of the PDFs represent the data, with all fits rejected having p-values <0.0001 (derived from 10 000 simulated data sets as described in the text). For power-law best fit t min was 0.01 for both on and off with α = 1.55 (off) and 1.67 (on). (Right) Identical analysis applied to the PI data derived from a single CdSe/CdS quantum dot on-times (c) and off-times (d). The single QD data also fails the statistical test for all three distributions to 0.01. This was performed for all QDs at 0.5 μW, not a single QD (on or off) was represented by these functions.

Image of FIG. 4.
FIG. 4.

Complimentary CDFs for the on (a) and off (b) times observed VR-KAP at room temperature (black) and 60 °C (gray). Best fits to lognormal functions are overlaid on the data (dashed lines). On-interval data become strongly lognormal at 60 °C (p-value increases from 0.024 to 0.118), but the room temperature tail diverges from the fit. Off interval data are not well fit to lognormal at either temperature (p-values of 10−4 and 0.035 for room temperature and 60 °C, respectively).

Image of FIG. 5.
FIG. 5.

(Left) Complimentary CDF's on log-log axes for CdSe/CdS QDs on- (a) and off- (b) times for data taken using 0.5 μW (solid line) and 8.2 μW (dashed line). 110 QDs are used at each power. D values are 0.0397 (on times) and 0.073 (off times). (Right) Control study for determining the distribution of observed D values for CdSe/CdS QDs in PMMA with 0.5 μW excitation at 532 nm. The data from 220 CdSe/CdS quantum dots were randomly parsed into two sets and the CDF's for the on- (c) and off- (d) times were determined and D value computed. The above histogram represents 1000 random trials. The shaded box indicates D values corresponding to p-values that accept the hypothesis that the two distributions are the same. Notice that 60%–65% of the time this hypothesis fails. The dashed line represents a lognormal fit to the data. Integrating the tail of the normalized lognormal PDF greater than D obtained from the two power comparison gives 0.36% (on-times) and 0.001% (off times) probability that they could be the same, demonstrating a power-dependence for both on and off times.

Image of FIG. 6.
FIG. 6.

0.5 μW QD CDF's for different thresholds of 3 (solid), 6 (dashed), and 9 (dotted) standard deviations above RMS noise depicted in (a). On-times (b) show a faster fall off in probability at long-times with increased threshold. D value of 0.0153 between the thresholds of 3 and 9. The opposite is observed in the off times (c), where D is 0.0378 between the thresholds of 3 and 9 standard deviations.

Image of FIG. 7.
FIG. 7.

D values observed at 0.5 and 8.2 μW between individual quantum QDs within their own ensembles. The on- (a, b) and off- (c, d) times become more homogeneous with increasing power (the distributions of D shift toward 0). These histograms correspond to the p-value being accepted 41% (off, c) and 33% (on, a) for 0.5 μW, and 29% (off, d) 22% (on, b) at 8.2 μW, consitent with a decrease in the off-times resulting in more switches.

Image of FIG. 8.
FIG. 8.

The mean and standard deviations of the on (a, b)- and off (c, d) intervals computed for the 0.5 μW data. These data illustrate just how difficult it is to judge how similar the quantum QDs are. When the KS test is used on either of these distributions in a simulation like that used on the ensemble CDF's the distributions are considered the same 93% of the time. This illustrates that for the case of distributed data the mean and standard deviation are not reliable methods for capturing the degree of heterogeneity in the ensemble.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Beyond power laws: A new approach for analyzing single molecule photoluminescence intermittency