Chemical and Thermodynamic Control of the Surface of Semiconductor Nanocrystals for Designer White Light Emitters
Bibliographic record
Abstract
Small CdSe semiconductor nanocrystals with diameters below 2 nm are thought to emit white light due to random surface defects which result in a broad distribution of midgap emitting states, thereby preventing rational design of small nanocrystal white light emitters. We perform temperature dependent photoluminescence experiments before and after ligand exchange and electron transfer simulations to reveal a very simple microscopic picture of the origin of the white light. These experiments and simulations reveal that these small nanocrystals can be physically modeled in precisely the same way as normal-sized semiconductor nanocrystals; differences in their emission spectra arise from their surface thermodynamics. The white light emission is thus a consequence of the thermodynamic relationship between a core excitonic state and an optically bright surface state with good quantum yield. By virtue of this understanding of the surface and the manner in which it is coupled to the core excitonic states of these nanocrystals, we show both chemical and thermodynamic control of the photoluminescence spectra. We find that using both temperature and appropriate choice in ligands, one can rationally control the spectra so as to engineer the surface to target color rendering coordinates for displays and white light emitters.
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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 | 0.001 | 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".