Impact of Butyl Glycidyl Ether Comonomer on Poly(glycerol–succinate) Architecture and Dynamics for Multifunctional Hyperbranched Polymer Design
Bibliographic record
Abstract
Abstract An original strategy is proposed to easily design functional materials from poly(glycerol–succinate) (PGS). This approach consists in the introduction of an epoxidized functional agent during the polyesterification between the glycerol and succinic acid. In order to model the effect of this epoxide group on the polymerization process and its resulting hyperbranched architecture, the butyl glycidyl ether (BGE) has been selected as comonomer agent. The theoretical potential reactions have been confronted with the topological units revealed by 2D NMR correlations. The regioselectivity against the primary alcohol and the stoichiometric balance of the system have been modified in situ by the kinetic control of parallel reactions. This had the effect to delay the gelation and increase the polyesterification conversion. The resulting hyperbranched polymers (HBPs) obtained just after gelation exhibit a temperature of glass transition (Tg) of −3.9 °C for PGS and −16.1 °C for poly(glycerol–succinate-co-butyl glycidyl ether) (PGS-co-BGE). This difference was explained by the BGE butyl tails effect which plays the role of dynamic spacer between the polymer chains during the relaxation process. The relaxation processes were investigated by the computation of the effective activation energy (Eα) through the Tg using the advanced isoconversional method and by the estimation of the β-relaxation activation energy (Eβ) by means of annealing experiments. The variation of Eα and Eβ values was discussed in terms of competition between the cooperative/noncooperative segment motions and the hindrance effect of the hydrogen-bonded network. The dynamic behavior of this system can be potentially generalizable to all the plastic glass containing a critical amount of secondary interactions.
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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".