Characterization of blinking dynamics in quantum dot ensembles using image correlation spectroscopy
Bibliographic record
Abstract
Quantum dots (QDs) are being increasingly applied as luminescent labels in optical studies for biophysical and cell biological applications due to their unique spectroscopic properties. However, their fluorescence “blinking” characteristics that follow power law statistics make it difficult to use QDs in some quantitative biophysical applications. We present image correlation spectroscopy (ICS) in combination with total internal reflection fluorescence microscopy as a tool to characterize blinking dynamics in QDs. We show that the rate of decay of the ICS measured ensemble correlation function reflects variation in blinking dynamics and can be used to distinguish different blinking distribution regimes. To test and confirm our hypothesis, we also analyze image time series simulations of ensembles of point emitters with set blinking statistics. We show that optimization of the temporal sampling and the number of QDs sampled is essential for detecting changes in blinking dynamics with ICS. We propose that this experimental characterization of the QD blinking statistics can actually serve as a sensitive reporter for certain quantitative biological applications.
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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 | 0.002 |
| 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 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".