Fe<sup>3+</sup> Assisted Synthesis of Stable 3D‐in‐2D CsPbBr<sub>3</sub>/CsPb<sub>2</sub>Br<sub>5</sub> Nanocomposites for Optical Gain Media
Bibliographic record
Abstract
Abstract Metal halide perovskite nanocrystals are sought after for many optical and optoelectronic applications, such as light‐emitting diode and solar cells, due to their outstanding optical properties. However, their ionic nature makes them susceptible to ambient conditions. One rational solution to this challenge is the passivation or encapsulation of perovskite nanocrystals to isolate them from their environments. Thus, there is an urgent need to develop efficient methods for encapsulating emissive perovskite nanocrystals. A facile post‐synthesis method is proposed to treat CsxFA(1−x)PbBr3 nanocrystals, in the presence of Fe3+ cations, to create a robust and water‐stable nanocomposite structure, where 3D CsPbBr3 nanocrystals are embedded in and thus protected by the 2D CsPb2Br5 nanosheets (named as CsPbBr3/CsPb2Br5 hereafter). These Fe3+ cations facilitate the formation of the CsPbBr3/CsPb2Br5 composite and regulate the growth of 2D CsPb2Br5 sheets. By performing controlled experiments, the possible mechanism of 2D nanosheet growth is proposed and discussed in detail. More importantly, the composite can remain stable in water for three months and exhibits amplified spontaneous emission under femtosecond laser irradiation. This work presents a synthesis pathway for producing durable perovskite composites that are promising for future lasing applications.
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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) | 0.001 | 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".