Investigating the influence of ligands on the surface-state emission of colloidal CdSe quantum dots
Bibliographic record
Abstract
Semiconductor based light generation is of enormous contemporary interest, given that a large fraction of global energy is used for lighting. White-light semiconductor colloidal quantum dots may find application in future solid state lighting technologies. These dots possess two inherent emission bands, a narrow emissive band attributed to a quantum confined exciton, and a broad emission associated with surface trapping. White light CdSe colloidal semiconductor nanocrystals passivated with phosphonic acids were synthesized by a hot-injection method. Aliquots of this sample are then ligand exchanged with amine and thiol ligands. These samples are embedded in polystyrene films, and a series of temperature dependent photoluminescence measurements are performed. The spectral width as a function of temperature is plotted for all samples. These data are then analyzed in terms of three models. The results suggest that surface line shape broadness may be tied to strong electron-phonon coupling and is largely ligand dependent. The amine and phosphonic acid passivated samples showed large temperature dependence over the range studied, whereas the thiol passivated sample had a lower dependence. This is tentatively explained in terms of hole delocalization in the case of thiol passivation.
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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".