Thermodynamics, morphology, mechanics, and thermal transport of PMMA-PLA blends
Bibliographic record
Abstract
Thermodynamics controls structure, function, stability, and morphology of polymer blends. However, obtaining the precise information about their mixing thermodynamics is a challenging task, especially when dealing with complex macromolecules. This is partially because of a delicate balance between the local concentration/composition fluctuations and the monomer level (multibody) interactions. In this context, the Kirkwood-Buff (KB) theory serves as a useful tool that connects the local pairwise fluid structure to the mixing thermodynamics. Using larger scale molecular dynamics simulations, within the framework of KB theory, we investigate a set of technologically relevant poly(methyl methacrylate)-poly(lactic acid) blends with the aim to elucidate the underlying microscopic picture of their phase behavior. Consistent with the existing experiments, we emphasize the importance of properly accounting for the entropic contribution to the mixing Gibbs free-energy change $\mathrm{\ensuremath{\Delta}}{\mathcal{G}}_{\mathrm{mix}}$ that controls the phase morphology. We further show how the relative microscopic interaction details and the molecular level structures between different mixing species can control the nonlinear mechanics, ductility, and heat flow. Therefore, this study provides a guiding principle for the design of light weight functional materials with extraordinary physical 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".