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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".