Water-Based Route to Colloidal Mn-Doped ZnSe and Core/Shell ZnSe/ZnS Quantum Dots
Bibliographic record
Abstract
Relatively monodisperse and highly luminescent Mn(2+)-doped zinc blende ZnSe nanocrystals were synthesized in aqueous solution at 100 °C using the nucleation-doping strategy. The effects of the experimental conditions and of the ligand on the synthesis of nanocrystals were investigated systematically. It was found that there were significant effects of molar ratio of precursors and heating time on the optical properties of ZnSe:Mn nanocrystals. Using 3-mercaptopropionic acid as capping ligand afforded 3.1 nm wide ZnSe:Mn quantum dots (QDs) with very low surface defect density and which exhibited the Mn(2+)-related orange luminescence. The post-preparative introduction of a ZnS shell at the surface of the Mn(2+)-doped ZnSe QDs improved their photoluminescence properties, resulting in stronger emission. A 2.5-fold increase in photoluminescence quantum yield (from 3.5 to 9%) and of Mn(2+) ion emission lifetime (from 0.62 to 1.39 ms) have been observed after surface passivation. The size and the structure of these QDs were also corroborated by using transmission electron microscopy, energy dispersive spectroscopy, and X-ray powder diffraction.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".