Starch-based reversible adhesive: Effect of off-stoichiometric ratios on transesterification vitrimer properties
Bibliographic record
Abstract
Bio-based epoxy vitrimers are gaining attention as sustainable alternatives to traditional epoxies due to renewable starting materials and recyclability, enabling material circularity. Transesterification, a dynamic bond reaction mechanism in vitrimers, relies on ample hydroxy and esters groups in the polymer network to rearrange under moderate conditions. To reduce reliance on petroleum we investigated starch, a natural feedstock rich in hydroxy groups. Ester bond densities were adjusted with an ester containing crosslinker (pentaerythritol tetrakis(3-mercaptopropionate) (PETMP)) at various epoxy to crosslinker molar ratios (1:0.7, 1:1, 1:1.25 and 1:1.5) with TEMPO as the catalyst. The reformation efficiency of the vitrimer increased to 239 % from 31 % when the ratio changed from 1:1 to 1:1.5. Essential dynamic behavioral parameters (dynamic bond exchange activation energy (E a(d) ), Arrhenius prefactor (τ 0 ), and freezing transition temperature (T v )) were analysed. Adding excess amount of crosslinker decreased the T g , crosslink density, E a(d) and T v of the starch-based epoxy vitrimer. Adhesion tests on birch veneer found that a 1:1.5 ratio had the highest adhesion performance at 3.02 ±0.7 MPa with an adhesion recovery as high as 91 % averaging 61 +/− 23 %. These insights into density of dynamic bonds and active functional groups will contribute to developing new bio-based vitrimers with designed circularity.
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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 | 0.000 | 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 | 0.001 |
| 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".