Synthesis of Fully Biobased Polyesters from Plant Oil
Bibliographic record
Abstract
Self-metathesis of fatty acid methyl esters (FAMEs) from natural oils and commercial oleic acid was carried out using a microwave reactor in solvent-free conditions. Self-metathesis products were further identified and quantified by gas chromatography–mass spectroscopy (GC–MS) and gas chromatography–flame ionization detector (GC–FID). Conversion of ∼50% was achieved within a short span (∼2 min) in the presence of 0.05 mol % Hoveyda–Grubbs second generation catalyst ( HG2 ) giving an equilibrium mixture of alkenes, α,ω-diester, and FAMEs. Highly pure dimethyl-9-octadecene-1,18-dioate (diester) was separated, and the desired quantity of it was reduced to 9-octadecene-1,18-diol (diol). Condensation polymerization of diester and diol as monomers was performed using conventional heating, microwave irradiation, and microwaves coupled with conventional heating. Characterization and analysis of synthesized biopolyesters were carried out using different techniques including nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), gel permeation chromatography (GPC), thermal gravimetric analysis (TGA), dynamic mechanical analysis (DMA), and tensile tests. Polyesters with the highest molecular weight of 337 kDa, ∼50 °C melting point, degradation temperature of about 400 °C, and the maximum strength of ∼5.5 MPa were obtained. These materials have great future potential to be used in different applications as a substitute of nonrenewable polyesters.
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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) | 0.001 | 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 | 0.001 |
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".