Determination of Complete Melting and Surface Premelting Points of Silver Nanoparticles by Molecular Dynamics Simulation
Bibliographic record
Abstract
A molecular dynamics simulation based on the embedded-atom method was conducted at different sizes of single-crystal Ag nanoparticles (NPs) with diameters of 4 to 20 nm to find complete melting and surface premelting points. Unlike the previous theoretical models, our model can predict both complete melting and surface premelting points for a wider size range of NPs. Programmed heating at an equal rate was applied to all sizes of NPs. Melting kinetics showed three different trends that are, respectively, associated with NPs in the size ranges of 4 to 7 nm, 8 to 10 nm, and 12 to 20 nm. NPs in the first range melted at a single temperature without passing through a surface premelting stage. Melting of the second range started by forming a quasi-liquid layer that expanded to the core, followed by the formation of a liquid layer of 1.8 nm thickness that also subsequently expanded to the core with increasing temperature and completed the melting process. For particles in the third range, the 1.8 nm liquid layer was formed once the thickness of the quasi-liquid layer reached 5 nm. The liquid layer expanded to the core and formed thicker stable liquid layers as the temperature increased toward the complete melting point. The ratio of the quasi-liquid layer thickness to the NP radius showed a linear relationship with temperature.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".