Imaging Heterogeneously Distributed Photo‐Active Traps in Perovskite Single Crystals
Bibliographic record
Abstract
Abstract Organic–inorganic halide perovskites (OIHPs) have demonstrated outstanding energy conversion efficiency in solar cells and light‐emitting devices. In spite of intensive developments in both materials and devices, electronic traps and defects that significantly affect their device properties remain under‐investigated. Particularly, it remains challenging to identify and to resolve traps individually at the nanoscopic scale. Here, photo‐active traps (PATs) are mapped over OIHP nanocrystal morphology of different crystallinity by means of correlative optical differential super‐resolution localization microscopy (Δ‐SRLM) and electron microscopy. Stochastic and monolithic photoluminescence intermittency due to individual PATs is observed on monocrystalline and polycrystalline OIHP nanocrystals. Δ‐SRLM reveals a heterogeneous PAT distribution across nanocrystals and determines the PAT density to be 1.3 × 10 14 and 8 × 10 13 cm −3 for polycrystalline and for monocrystalline nanocrystals, respectively. The higher PAT density in polycrystalline nanocrystals is likely related to an increased defect density. Moreover, monocrystalline nanocrystals that are prepared in an oxygen‐ and moisture‐free environment show a similar PAT density as that prepared at ambient conditions, excluding oxygen or moisture as chief causes of PATs. Hence, it is concluded that the PATs come from inherent structural defects in the material, which suggests that the PAT density can be reduced by improving crystalline quality of the material.
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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 | 0.000 |
| 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 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".