Synthesis and characterization of renewable polyesters based on vanillic acid
Bibliographic record
Abstract
Abstract Vanillic acid, potentially derived from biosourced feedstocks, was used as starting material in the synthesis of novel polyesters. A diester obtained by esterification and etherification of vanillic acid served as first monomer, in combination with resorcinol bis(2‐hydroxyethyl) ether, hydroquinone bis(2‐hydroxyethyl) ether, or aliphatic diols with different carbon chain lengths as second monomers, and antimony trioxide as catalyst to synthesize a series of polyesters by melt polymerization. The materials obtained had polystyrene‐equivalent M n = 10,000–19,500 g/mol and M w = 19,300–39,500 g/mol. Thermal analysis yielded melting temperatures of 76–114°C and onset decomposition temperatures T5% = 321–360°C. Dynamic mechanical and tensile testing confirmed that polyesters derived from vanillic acid have properties superior to poly(ethylene terephthalate) (PET). Aqueous degradation experiments with bulk polyester samples at different pH over 16 weeks led to intrinsic viscosity decreases of 9–23% under neutral conditions (pH 7), 10–26% under acidic conditions (pH 3), and 7–19% under alkaline conditions (pH 12). The results obtained show that the good thermal stability and degradability of vanillic acid‐based polyesters, and the tunability of the properties of these materials through selection of the diol monomer used in their synthesis, make them excellent biosourced replacements for commercial polyesters such as PET.
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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 | 0.001 | 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".