Evolutionary Shape Control During Colloidal Quantum‐Dot Growth
Bibliographic record
Abstract
Size-dependent optical properties of semiconductor nanocrystals are of great interest because of the myriad of phenomena stemming from them. The preparation of more complex colloidal shapes will facilitate the systematic study of shape-dependent phenomena. It is shown that a strategy to obtain systematically more complex nanocrystal structures is to exert a sequence of shape-directing steps during the colloidal growth. Using experiments based on multiple reagent injections we show how changes in the type of surfactant introduced during growth of CdSe nanocrystals promotes shape evolution. On this basis, we propose a means to achieve a further generation of shape design in nanometer-sized colloids by using a series of growth steps, each one building from the previous conditions of shape as well as surface-specific reactivity. To understand the shape formation and stability in nanocrystalline colloids, and particularly the importance of surface ligands, we introduce an analogy with the thermodynamics of droplets.
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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 | 0.001 |
| 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 | 0.001 | 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".