Quantitative modeling of the role of surface traps in CdSe/CdS/ZnS nanocrystal photoluminescence decay dynamics
Bibliographic record
Abstract
Charge carrier trapping is an important phenomenon in nanocrystal (NC) decay dynamics because it reduces photoluminescence (PL) quantum efficiencies and obscures efforts to understand the interaction of NC excitons with their surroundings. Particularly crucial to our understanding of excitation dynamics in, e.g., multiNC assemblies, would be a way of differentiating between processes involving trap states and those that do not. Direct optical measurement of NC trap state processes is not usually possible because they have negligible transition dipole moments; however, they are known to indirectly affect exciton photoluminescence. Here, we develop a framework, based on Marcus electron transfer theory, to determine NC trap state dynamics from time-resolved NC exciton PL measurements. Our results demonstrate the sensitivity of PL to interfacial dynamics, indicating that the technique can be used as an indirect but effective probe of trap distribution changes. We anticipate that this study represents a step toward understanding how excitons in nanocrystals interact with their surroundings: a quality that must be optimized for their efficient application in photovoltaics, photodetectors, or chemical sensors.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".