Qualitative estimation of the depth dependence of glass transition temperature of polymer surface using schematic mode-coupling theory
Bibliographic record
Abstract
The phenomenon of glass transition near a polymer surface is significantly different from that of the bulk. This is due to the fact that the chains near the free surface are more mobile as there are more free volume. But how can we, at least qualitatively, describe the relationship between the motion of the chain and the free volume around it in glassy and melt states? In this work, we aim at verifying this line of reasoning from the first principles so that a tractable expression governing the glass transition can be obtained. First, the surface density profile of the polymer was calculated using the Euler–Lagrange equation of the square gradient theory. The numerical results show that density at each layer of the surface is a function of the depth from the surface into the bulk. Second, at each layer, the intermolecular radial distribution function in the Fourier space was computed using polymer reference interaction site model. Finally, with these, the structure factor and the correlation length can be known, allowing us to determine the transition point in schematic mode-coupling theory. In line with experimental observations and simulation results reported in the literature, our model points to the fact that the presence of larger free volume near the surface layer contributes to a reduction in the glass transition temperature of the polymer film, even though the surface layer is more compressible than the bulk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".