Self-Assembly of CsPbBr<sub>3</sub> Nanocubes into 2D Nanosheets
Bibliographic record
Abstract
All-inorganic metal halide perovskites have attracted considerable attention due to their high application potentials in optoelectronics, photonics, and energy conversion. Herein, two-dimensional (2D) CsPbBr 3 nanosheets with a thickness of about 3 nm have been synthesized through a simple chemical process based on a hot-injection technique. The lateral dimension of CsPbBr 3 nanosheets ranges from 11 to 110 nm, which can be tuned by adjusting the ratio of short ligands (octanoic acid and octylamine) over long ligands (oleic acid and oleylamine). The nanosheets result from the self-assembly of CsPbBr 3 nanocubes with an edge length of about 3 nm, which possess the same crystal orientation. In addition, an amorphous region of about 1 nm in width is found between adjacent nanocubes. To investigate both the structure and the growth mechanism of these nanosheets, microstructural characterizations at the atomic scale are conducted, combined with X-ray diffraction analysis, 1 H nuclear magnetic resonance ( 1 H NMR) measurement, and density functional theory (DFT) calculation, aiming to determine the configuration of different ligands adsorbed onto CsPbBr 3 . Our results suggest that the adjacent nanocubes are mainly connected together by short ligands and inclined long ligands. On the basis of the DFT calculation results, a relationship is derived for the volume ratio of short ligands over long ligands and the lateral dimensions of CsPbBr 3 nanosheets. Moreover, a physicochemical mechanism is proposed to explain the 2D growth of CsPbBr 3 nanosheets. Such a finding provides new insights regarding the well-ordered self-arrangement of CsPbBr 3 nanomaterials, as well as new routes to synthesize 2D CsPbX 3 (X = Cl and I) nanosheets of suitable dimensions for specific and large-scale 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".