Binary Blends of Entangled Star and Linear Poly(hydroxybutyrate): Effect of Constraint Release and Dynamic Tube Dilation
Bibliographic record
Abstract
In order to further understand the relaxation behavior of binary blends of star and linear chains, new polymer blends consisting of linear poly(hydroxybutyrate) (PHB) matrix and PHB star molecules are designed, and their dynamics is investigated by varying the star concentrations and the molar mass of the linear matrix, while keeping few or no star–star entanglements in the blends. By studying the constraint release Rouse (CRR) relaxation of the star polymer diluted in the linear matrix at concentrations low enough to neglect star–star entanglements, we first point out the importance of the number of short linear chain entanglements on the CRR time of the long chains. For the blends composed of a larger proportion of star molecules, we then use this new definition of CRR time to determine the necessary time of a star–star entanglement segment to relax by CRR and explore its dilated tube, at the rhythm of the disentanglement/re-entanglements of the short chains. By considering this as the new reference time for describing the contour length fluctuations of the arm in its “fat” tube, i.e., the tube which only involves star–star entanglements, we propose a simple and consistent way to take into account two opposite effects resulting from the short linear matrix. On the one hand, fast relaxation leads to a large dilution effect, which is reflected by the dilation of the tube in which the long chains are moving. On the other hand, the long chains can only move in their fat tube at the rhythm of the motion of the short chains, which can slow down their relaxation compared to the motion of the same stars diluted in a real solvent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".