A Confinement‐Driven Nucleation Mechanism of Metal Oxide Nanoparticles Obtained via Thermal Decomposition in Organic Media
Bibliographic record
Abstract
Thermal decomposition is a very efficient synthesis strategy to obtain nanosized metal oxides with controlled structures and properties. For the iron oxide nanoparticle synthesis, it allows an easy tuning of the nanoparticle's size, shape, and composition, which is often explained by the LaMer theory involving a clear separation between nucleation and growth steps. Here, the events before the nucleation of iron oxide nanocrystals are investigated by combining different complementary in situ characterization techniques. These characterizations are carried out not only on powdered iron stearate precursors but also on a preheated liquid reaction mixture. They reveal a new nucleation mechanism for the thermal decomposition method: instead of a homogeneous nucleation, the nucleation occurs within vesicle-like-nanoreactors confining the reactants. The different steps are: 1) the melting and coalescence of iron stearate particles, leading to "droplet-shaped nanostructures" acting as nanoreactors; 2) the formation of a hitherto unobserved iron stearate crystalline phase within the nucleation temperature range, simultaneously with stearate chains loss and Fe(III) to Fe(II) reduction; 3) the formation of iron oxide nuclei inside the nanoreactors, which are then ejected from them. This mechanism paves the way toward a better mastering of the metal oxide nanoparticles synthesis and the control of their properties.
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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".