Enhanced Stokes Shift and Phase Stability by Cosynthesizing Perovskite Nanoparticles (MAPbI<sub>3</sub>/MAPbBr<sub>3</sub>) in a Single Solution
Bibliographic record
Abstract
Herein, diblock copolymer reverse micelle templating (RMD) is used to control the reaction kinetics of metal halide hybrid perovskites formation to fabricate systems showing dual‐phase emission. Through micelle templating, desired compositions can be engineered which show high phase stability of mixtures of perovskite nanoparticles (NPs) through micellar shielding and stabilizing of the cage structure. In addition, Stokes shift of around 150 nm, one of the largest reported for perovskite systems, can be obtained with careful control over synthesis kinetics. Using an unconventional approach, that is, mixing methylammonium iodide (MAI) and lead bromide PbBr2, systems consisting of both green‐ and red‐emitting NPs are fabricated by a two‐step reaction process using RMD. By obtaining two stable phases in a single solution, the NP system can absorb in the ultraviolet region and emit in the red region, making them excellent candidates for downconversion to improve solar cells efficiency, as shown for two polymer active layers in organic bulk heretojunction solar cells (OPVs). Exploiting the phase stabilizing effect of the micelles, the reaction kinetics of perovskite formation can be tuned for various halide substitutions, opening up new avenues for coexisting perovskite phases for photovoltaic and light‐emitting 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".