Saturation of Two-Photon Excitation Provides Insight into the Effects of a Quantum Dot Blinking Suppressant: A Fluorescence Correlation Spectroscopy Study
Bibliographic record
Abstract
Quantum dots are now extensively used as luminescent tags for biological and chemical applications; however, their propensity to display intermittent luminescence (aka blinking) has limited their use in quantitative assays. Various surface active redox agents, such as β-mercaptoethanol, have been posited as antiblinking agents to help mitigate this challenge. We have examined the effect of β-mercaptoethanol on the luminescence intermittency of CdSe/ZnS quantum dots using fluorescence correlation spectroscopy. We find that β-mercaptoethanol lowers the quantum dot brightness and therefore lowers the probability of observing blinking but does not otherwise inhibit intermittent luminescence. Based on changes in fluorescence correlation data and excited-state lifetimes, we propose that the partitioning of β-mercaptoethanol onto the quantum dot surfaces increases nonradiative de-excitation rates. This behavior appears to be independent of the quantum dot surface passivation layer or the solution contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".