Reappraising the Luminescence Lifetime Distributions in Silicon Nanocrystals
Bibliographic record
Abstract
The luminescence dynamics in ensembles of nanocrystals are complicated by a variety of processes, including the size-dependence of the radiative and non-radiative rates in inhomogeneous broadened samples and interparticle interactions. This results in a non-exponential decay, which for the specific case of silicon nanocrystals (SiNCs) has been widely modeled with a Kohlrausch or “stretched exponential” (SE) function. We first derive the population decay function for a luminescence decay following exp[− ( t / τ) β ]. We then compare the distributions and mean times calculated by assuming that either the luminescence decay or the population decay follows this function and show that the results are significantly different for β much below 1. We then apply these two types of SE functions as well as other models to the luminescence decay data from two thermally grown SiNC samples with different mean sizes. The mean lifetimes are strongly dependent on the experimental setup and the chosen fitting model, none of which appears to adequately describe the ensemble decay dynamics. Frequency-resolved spectroscopy (FRS) techniques are then applied to SiNCs in order to extract the lifetime distribution directly. The rate distribution has a half width of ~ 0.5 decades and mainly resembles a somewhat high-frequency-skewed lognormal function. The combination of TRS and FRS methods appear best suited to uncovering the luminescence dynamics of NC materials having a broad emission spectrum.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".