Interpreting time-resolved photoluminescence of perovskite materials
Bibliographic record
Abstract
Time-resolved photoluminescence (TRPL) spectroscopy is a powerful technique to investigate excited charge carrier recombinations in semiconductors and molecular systems. The analysis of the TRPL decays of many molecular systems (e.g. molecules and organic materials) is usually fairly straightfoward and can be fitted with an exponential function allowing extraction of the rate constants. Due to the non-excitonic nature of charge carriers in lead halide perovskite materials coupled with the presence of localised trap states in their band-gap, the TRPL of these materials is much more complicated to interpret. Here we discuss two models used in the literature to simulate charge carrier recombinations and TRPL in perovskites. These models consider the bimolecular nature of direct electron-hole recombination but differ in their treatment of trap-mediated recombination with one model describing trapping as a monomolecular process whereas the other as a bimolecular process between free carriers and the available trap states. In comparison, the classical analysis of perovskite TRPL decay curves (using a sum of exponentials) can lead to misinterpretation. Here we offer some recommendations for meaningful measurements of lead halide perovskite thin-films. The fluence dependence as well as charge carrier accumulation due to incomplete depopulation of all photoexcited carriers between consecutive excitation pulses are discussed for both models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".