Differential effects of β-mercaptoethanol on CdSe/ZnS and InP/ZnS quantum dots
Bibliographic record
Abstract
The small thiol β-mercaptoethanol (BME) has been used as an anti-blinking reagent for CdSe/ZnS quantum dots (QDs), although its effects on QD photoluminescence are complex. It acts as an antioxidant as well as a hole scavenger on both CdSe and CdTe, which leads to changes in emission intensity and lifetime that vary qualitatively according to BME concentration, time of incubation, and pH of the solution. Because the band edge energies of InP/ZnS are shifted from those of CdTe and CdSe, it may be expected that thiols including BME might be unable to trap holes from these QDs. In this study, we use steady-state and time-resolved emission spectroscopy with physical fitting models combined with blinking analysis to compare the effects of different concentrations of BME on CdSe/ZnS vs. InP/ZnS QDs over time. We also find excellent correspondence between simple physical model parameters and blinking off times, a finding that will be useful for all blinking studies involving semiconductor nanoparticles. BME alters blinking in InP/ZnS QDs with a single ZnS shell, but not those with double thickness shells. The effects are similar to those seen with CdSe/ZnS, despite very different effects of BME on steady-state spectra, and highly pH-dependent.
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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 | 0.000 |
| 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".