Dispersive Electron-Transfer Kinetics from Single Molecules on TiO<sub>2</sub> Nanoparticle Films
Bibliographic record
Abstract
The distributions of electron-transfer dynamics in dye-sensitized TiO 2 films are probed using single-molecule microscopy. The time-dependent emission (i.e., blinking dynamics) of rhodamine 6G (R6G) and rhodamine B (RB) sensitized TiO 2 films are quantified by constructing cumulative distribution functions of emissive (“on”) and nonemissive (“off”) events. Maximum likelihood estimation (MLE) methods and quantitative goodness-of-fit tests based on the Kolmogorov–Smirnov (KS) statistics are used to establish the best fit to the photophysical data. The on-time distributions for R6G and RB on TiO 2 are fit by power laws, but only for emissive durations that last longer than ∼0.7 s. Furthermore, large variations in the power-law exponents are observed when using least-squares fitting as compared to the combined MLE and KS-test approach. The off-time distributions for molecules on TiO 2 and glass are not consistent with power laws and are instead well represented by log-normal distributions. Our observations support the hypothesis that electron-transfer processes are responsible for blinking on TiO 2 as well as glass substrates. Furthermore, the on-time and off-time distributions are sensitive to the chromophore as well as the substrate. To understand the origin of these power-law and log-normal distributions, single-molecule blinking dynamics are modeled using Monte Carlo simulations based on a three-level system with the rate constants for population and depopulation of the nonemissive state being log-normally distributed (i.e., Albery model). In this framework, the rate constants for FET and BET are log-normally distributed, consistent with a Gaussian distribution of activation energies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".