Enhanced Two‐Photon‐Pumped Emission from In Situ Synthesized Nonblinking CsPbBr<sub>3</sub>/SiO<sub>2</sub> Nanocrystals with Excellent Stability
Bibliographic record
Abstract
Abstract Perovskites have emerged as a class of cutting‐edge photovoltaic and light‐emitting materials. However, poor stability due to high moisture sensitivity and undesirable blinking severely limits their further application. Here, to solve these problems without destroying optoelectronic performance, a simple process for the fabrication of nonblinking CsPbBr3 quantum dots (QDs) is investigated. By embedding CsPbBr3 QDs into waterless silica spheres, the blinking of QDs can be strikingly suppressed, with an effective improvement of the moisture resistance and enhanced photostability. The silica sphere can also prevent anion exchange of different halide elements between perovskite QDs. Ultrastable amplified spontaneous emission (ASE) from QDs/SiO2 with no degradation for at least 12 h is observed under continuous laser irradiation (4 × 107 continuous intense laser shots), with almost no ASE degradation evident after 60 d of storage under ambient conditions. Most notably, the ASE threshold (Pth) of CsPbBr3 QDs is decreased by 50% and the relative efficiency increased by 388%. The perovskite QDs coated by the waterless SiO2 shell provide a novel platform for realizing perovskite nanomaterials with improved operational stability, nonblinking properties, and enhanced emission all at the same time, which is especially attractive for photovoltaic and light‐emitting device 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".