Melting of alkane nanocrystals: towards a representation of polyethylene
Bibliographic record
Abstract
To simulate macroscopic properties at the molecular level, interfaces must be represented the most efficiently. In the study reported in this article, we use molecular dynamics (MD) simulation to reveal the impact of the environment around nanocrystals constituted of alkane chains on the melting temperature. The Gibbs-Thomson law was used to compare different simulated systems and experimental data. Based on thermodynamics argument, this law discloses the linear relationships between the melting, or crystallisation, temperature and the inverse of the crystal thickness. The crystal edges of simulated nanocrystals and the alkane chain length have been varied. For each nanocrystal, a simulated melting temperature (Tm) was obtained and reported with respect to the crystal thickness. The simulated enthalpy of fusion (Δhm), surface (σe) and lateral (σ) free energies were thus extracted. All these properties have been compared to experimental data, and to properties stemming from nanocrystals in empty space. It is shown that nanocrystals surrounded by amorphous chains lead to values that agree better experimental data showing the great improvement in the model.
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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".