Thermal decomposition of single source precursors and the shape evolution of CdS and CdSe nanocrystals
Bibliographic record
Abstract
A systematic analysis of the thermal decomposition of various single-source precursors is reported with the aim of finding a correlation between the pattern of thermal decomposition and morphology of the nanocrystals resulting from the thermolysis of these precursors in a coordinating solvent. The precursors studied are cadmium complexes of N,N′-dioctylthiourea, N,N′-diocyclohexylthiourea, N,N′-diisopropylthiourea, N,N′-tetramethylthiourea, dithiobiurea, ethylxanthic acid, thiosemicarbazide, selenosemicarbazide. Cadmium complexes of thiosemicarbazide and selenosemicarbazide uniquely yield rod-shaped CdS and CdSe nanocrystals respectively while all other precursors yield spherical CdS nanoparticles. Nanorod formation without the aid of any external shape-directing agent is explained through analysis of the thermal decomposition patterns, as observed by thermogravimetric analysis, for the range of precursor molecules. It is suggested that the low activation energy for the semicarbazide precursor decomposition, compared to those that produce dot-shaped nanocrystals, provides conditions favourable for the growth of nanorods. Evidence supporting the idea that the semicarbazide precursors furthermore release a structure-directing agent during decomposition is provided by infrared spectra and elemental analysis. Hence it can be presumed that thiosemicarbazide and selenosemicarbazide ligands each act as both the source of sulfur and a shape-directing agent.
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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 | 0.000 | 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".