Connecting the Dots: The Kinetics and Thermodynamics of Hot, Cold, and Surface-Trapped Excitons in Semiconductor Nanocrystals
Bibliographic record
Abstract
The excitonics of semiconductor nanocrystals (NC) depend upon temperature in a complex manner due to the interplay between the kinetics of hot exciton relaxation/trapping and the thermodynamics leading to cold exciton recombination. We apply a semiclassical electron transfer model of surface trapping to temperature-dependent absorption and emission data to elucidate a microscopic picture of the factors which govern the fate of hot and cold excitons. The linear absorption spectra reveal a unique temperature-dependence to the energies of higher excitonic states, while oscillator strength is shown to be temperature invariant. We identify the phonon based origin to the anomalous low temperature peak energy trend in photoluminescence (PL) spectra. PL intensities, PL lifetimes, and absorption spectra are used to demonstrate that variation of quantum yield with temperatures arises from the thermally controlled fraction of NC which emit, rather than from an activated nonradiative pathway common to all NCs. Experimental quantum yield spectra are shown for several NCs and we perform a much-needed analysis of the role of surface PL in quantum yield. Finally, we show that a semiclassical electron transfer model including hot excitonic effects can explain experimental quantum yield spectra and suggests how to probe kinetic trapping processes via simple steady-state spectroscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".