Tuning Morphology and Thermal Transport of Asymmetric Smart Polymer Blends by Macromolecular Engineering
Bibliographic record
Abstract
A grand challenge in designing polymeric materials is to tune their properties by macromolecular engineering. In this context, one of the drawbacks that often limits broader applications of polymers under high temperature conditions is their poor thermal conductivity κ. Using molecular dynamics simulations, we establish a structure–property relationship in hydrogen-bonded polymer blends for possible improvement of κ. For this purpose, we investigate two experimentally relevant hydrogen-bonded systems: one system consists of short poly( N -acryloyl piperidine) (PAP) blended with longer chains of poly(acrylic acid) (PAA), and the second system is a mixture of PAA and short polyacrylamide (PAM) chains. Simulation results show that PAA-PAP blends are at the onset of phase separation over the full range of the PAP monomer mole fraction ϕ PAP, which intensifies even more for ϕ PAP > 0.3. While PAA and PAP interact with preferential hydrogen bonding, phase separation is triggered by the dominant van der Waals attraction between the hydrophobic side groups of PAP. However, if PAP is replaced with PAM, which has a similar chemical structure as PAP without the hydrophobic side group, PAA-PAM blends show much improved solubility. Better solubility is due to the preferential hydrogen bonding between PAA and PAM. As a result, PAM oligomers act as cross-linking bridges between PAA chains resulting in a three-dimensional highly cross-linked network. While κ for PAA-PAP blends remain almost invariant with ϕ PAP, PAA-PAM systems show improved κ with increasing PAM concentration and also with respect to PAA-PAP blends. Consistent with the theoretical prediction for the thermal transport of amorphous polymers, we show that κ is proportional to the materials’ stiffness, that is, the bulk modulus K and sound velocity v of PAA-PAM blends. However, no functional dependence between κ and K (or v ) is observed for the immiscible PAA-PAP blends.
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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".