Mn2+-doping into CsPbBr3 Perovskite Supercrystals: Enhancing Morphology and Substrate Variation
Bibliographic record
Abstract
The self-assembly of metal halide perovskite nanocrystals into micrometer-sized supercrystals with high structural order as influenced by the surface chemistry and particle morphology of the starting building blocks is of interest. In this work, we investigate the effects of Mn²⁺-doping on CsPbBr3 perovskite nanocrystals and their self-assembly into supercrystals. Mn²⁺-incorporation is found to improve the photoluminescence properties of both nanocrystals and supercrystals, resulting in higher photoluminescence quantum yields and longer radiative lifetimes compared to undoped counterparts. Structural analysis using powder X-ray diffraction and electron microscopy confirms that Mn²⁺-doping does not hinder the self-assembly of highly ordered, predominantly cubic supercrystals, but leads to one dimensional morphologies as dictated by the effect of increasing Mn2+ molar ratio incorporated during nanocrystal synthesis. Notably, we observe a breakdown of three dimensional supercrystal formation, driven by changes in constituent nanocrystal size distribution controlled by Mn²⁺ addition, contrasting with previous studies where capping ligand density was the driving factor in these morphological changes. Furthermore, we show though time-resolved powder X-ray diffraction and electron microscopy, that the self-assembly of metal halide perovskite supercrystals occurs early in the slow solvent evaporation process, and superstructures can be formed on a variety of substrates, extending the applications of these materials.
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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".