A spectroscopic overview of the differences between the absorbing states and the emitting states in semiconductor perovskite nanocrystals
Bibliographic record
Abstract
Semiconductor perovskites have been under intense investigation for their promise in optoelectronic applications and their novel and unique physical properties. There have been a variety of material implementations of perovskites from thin films to single crystals to nanocrystals. The nanocrystal form, in particular, is attractive as it enables solution processing and also spectroscopically probes both absorptive and emissive transitions. Broadly, the literature is comprised of experiments of either form, but the experiments are rarely performed in concert and are not discussed in a unified picture. For example, absorptive experiments are typically transient absorption measurements, which aim to measure carrier kinetics and dynamics. In contrast, the emissive experiments largely focus on excitonic fine structures and coupling to phonons. The time resolved emission experiments report on excited state lifetimes and their dependence on temperature. There are broad differences in the spectroscopy techniques and the questions asked in both classes of experiments. Yet there is one measure in common that suggests there are mysteries in our understanding of how the absorbing and emitting states are connected. The linewidth of emission spectra is always larger than the linewidth of absorption spectra. The question of the physics underlying linewidths is complex and is one of the central issues in perovskite nanocrystals. So why are the absorptive and emissive linewidths different? At present even this simple question has no clear answer. The more complex questions of the structure and dynamics of absorptive and emissive states are even more ambiguous. Hence there is a need to connect these experiments and the relevant states. Here, we provide an overview of the salient absorptive and emissive spectroscopy techniques in an effort to begin connecting these two disparate areas of inquiry.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".