Quantitative Comparison of Recombination Rates of Core/Shell Quantum Dots in Colloidal Solutions and Self-Assembled Monolayer Superlattices
Bibliographic record
Abstract
Understanding the fundamental excited-state dynamics of quantum dot thin films is key to rationally engineering high quality devices. However, studying the radiative and nonradiative recombination rates of quantum dot thin films is challenging due to the small quantity of quantum dots in a thin film. In this work, we measure the photoluminescence quantum yield of self-assembled quantum dot monolayer thin films and quantify their radiative and nonradiative rates. The recombination rates of core/shell quantum dot self-assembled monolayer superlattices are systematically compared with their colloidal solution counterparts. Both the radiative and nonradiative rates of these quantum dots were found to be enhanced in the thin film samples. The increase in the overall nonradiative rate is expected and can be attributed to the stripping of ligands from the nanocrystal surface as well as energy transfer in close-packed solid-state samples. In contrast, the increase in overall radiative rate in the film reveals a change in the fundamental optical properties of quantum dot films, suggesting that the oscillator strength of the nanocrystals increases in the films compared with in solutions. The increase in the oscillator strength is likely due to changes in the organic ligand shell coverage and its effect on the electronic band structure of the quantum dot.
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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 | 0.001 |
| 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 | 0.000 |
| 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".