Controlling the Mechanism of Phase Transformation of Colloidal In<sub>2</sub>O<sub>3</sub> Nanocrystals
Bibliographic record
Abstract
Controlling the crystal structure of transparent metal oxides is essential for tailoring the properties of these polymorphic materials to specific applications. The structural control is usually done via solid state phase transformation at high temperature or pressure. Here, we report the kinetic study of in situ phase transformation of In2O3 nanocrystals from metastable rhombohedral phase to stable cubic phase during their colloidal synthesis. By examining the phase content as a function of time using the model fitting approach, we identified two distinct coexisting mechanisms, surface and interface nucleation. It is shown that the mechanism of phase transformation can be controlled systematically through modulation of temperature and precursor to solvent ratio. The increase in both of these parameters leads to gradual change from surface to interface nucleation, which is associated with the increased probability of nanocrystal contact formation in the solution phase. The activation energy for surface nucleation is found to be 144 ± 30 kJ/mol, very similar to that for interface nucleation. Despite the comparable activation energy, interface nucleation dominates at higher temperatures due to increased nanocrystal interactions. The results of this work demonstrate enhanced control over polymorphic nanocrystal systems and contribute to further understanding of the kinetic processes at the nanoscale, including nucleation, crystallization, and biomineralization.
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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) | 0.000 | 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".