<i>In situ</i> cell-scale probing nucleation, growth and evolution of CsPbBr<sub>3</sub> nanowires by optical absorption spectroscopy
Bibliographic record
Abstract
Abstract The growth kinetics of colloidal lead halide perovskite nanomaterials are an integral part of their applications, remains poorly understood due to complex nucleation processes and lack of in situ size monitoring method. Here we demonstrated that absorption spectra can be used to observe in situ growth processes of ultrathin CsPbBr3 nanowires in solution with reference to the effective mass infinite deep square potential well model. By means of this method, we have found that the ultrathin nanowires, fabricated by hot injection method, were firstly formed within one minute. Subsequently, they merge with each other into a thicker structure with increasing reaction time. We revealed that the nucleation, growth, and merging of the CsPbBr3 nanowires are determined by the acid concentration and ligand chain length. At lower acidity, the critical nucleation size of the nanowire is smaller, while the shorter the ligand chain length, the faster the merging among the nanowires. Moreover, the merging mode between nanowires changed with their nucleation size. This growth kinetics of CsPbBr3 nanowires provides a reference for optimizing the synthesis conditions to obtain the one-dimensional CsPbBr3 with desired size, thus enabling accurate control of the nanowire shape.
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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".