Effects of β-Mercaptoethanol on Quantum Dot Emission Evaluated from Photoluminescence Decays
Bibliographic record
Abstract
β-mercaptoethanol (BME) has been used as an “anti-blinking” reagent with quantum dots (QDs), but its exact effects on the luminescence behavior of different QD materials have not been quantified. In this study, the luminescence lifetime decays of aqueous solutions of CdTe QDs solubilized with mercaptopropionic acid (MPA) are measured by time-correlated single photon counting (TCSPC) in the presence of varying concentrations of BME. The decays are fit to a model of radiative recombination and trapping that yields the respective time constants as well as the coefficient of intermittency (blinking). It is found that low concentrations of BME in its thiol form (neutral pH) lead to decreases in average lifetime but increased or constant quantum yields, indicating a higher fraction of radiative QDs than without BME. Correspondingly, the blinking coefficients are greatly reduced in the presence of BME at neutral pH. Higher concentrations of BME reduce emission by creating hole traps, a process that requires several hours after BME addition to manifest. Lifetimes are also reduced by the thiolate form of BME (basic pH) but to a lesser degree than at neutral pH. Strikingly, the blinking coefficients are almost entirely unchanged with BME addition at basic pH. In deoxygenated solutions, quantum yields are decreased rather than increased with BME, confirming that the enhancement results from BME’s antioxidant effects. These results provide a quantitative approach to studying blinking and trapping dynamics using time-resolved decays.
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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 | 0.000 | 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".