A Common Mechanism Underlies the Dark Fraction Formation and Fluorescence Blinking of Quantum Dots
Bibliographic record
Abstract
CdSe quantum dots (QDs) are known to exhibit both power-law blinking dynamics and a dark fraction. A complete description of the mechanistic origins of these properties is still lacking. We show that a change in the pH of the QD environment systematically changes both the dark fraction and the blinking statistics. As pH is lowered, shorter "on" times and longer "off" times, as well as an increase in the permanent dark fraction, are observed. The increase in the dark fraction is preceded by a decrease in the emission intensity of a single QD. Interestingly, the form of the probability distribution function describing blinking changes when the QDs are taken from an air-exposed environment into an aqueous one. These results are used to propose a coupled role for H(+) ions by which they first reduce the intensity of the emitting state as well as affect the probabilities of the QD to switch between "on" and "off" states and eventually trap the QD in a permanent "off" state. We discuss and extend two theoretical blinking models to account for the effect of H(+) ions as well as to highlight their common principle of a diffusion-controlled mechanism governing blinking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".