Specific Heat, Melting, Crystallization, and Oxidation of Zinc Nanoparticles and Their Transmission Electron Microscopy Studies
Bibliographic record
Abstract
The specific heat, C p, of zinc nanoparticles (size distribution 30−180 nm and peak at 30 nm) was measured, and their melting behavior was investigated as the ZnO shell grew around the metal particles and thickened. Both structural and chemical analyses were performed by using Transmission Electron Microscopy and techniques of energy filtering and energy dispersive X-ray analyses. The C p of Zn nanoparticles is slightly higher than that of bulk metal. The melting point of Zn nanocrystals confined to the ZnO shell is only 1−2 K less than that of bulk Zn, much less than that expected from the Gibbs−Thomson equation. This is attributed to the increase in pressure on the zinc core because (i) zinc expands more on heating and on melting than ZnO, (ii) the ZnO shell thickens at the expense of the zinc core, and (iii) there is an epitaxial interaction between Zn and the ZnO shell. The enthalpy of melting decreases on thermal cycling. Nanodroplets of Zn supercooled by a few degrees before crystallizing in two steps. The high temperature step is attributed to heterogeneous nucleation at the core−shell interface, and the low temperature step to homogeneous nucleation in the particle core. The amount crystallized on homogeneous nucleation decreased on thermal cycling as the ZnO shell thickened when oxygen diffused through this layer. The enthalpy of crystallization also decreased on thermal cycling.
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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 | 0.001 | 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".