Improved utilization of crude glycerol from biodiesel industries: Synthesis and characterization of sustainable biobased polyesters
Bibliographic record
Abstract
The present work describes the synthesis of biobased polyesters using glycerol of different purities and sources with the aim of understanding how glycerol composition can influence the resulting structure and properties of biobased polyesters. Glycerol and succinic acid based polyesters were synthesized using crude and technical grade glycerol obtained from biodiesel producing facilities. It was shown that the presence of impurities in crude glycerol can greatly decrease the yield of reaction and also lead to products with different chemical structure and composition than those derived from pure glycerol. In particular, the presence of fatty acids and soaps was shown to produce incorporation of fatty acid residues and formation of carboxylate residues in the polymer backbone respectively. The products synthesized from industrial technical grade glycerol with 95 wt% purity were similar to those formulated from pure glycerol, showing rubbery behavior at room conditions. The materials synthesized from crude glycerol showed different thermal and chemical properties due to incorporation of impurities from the glycerol source to the polymer backbone. It was concluded that technical glycerol could be used as an alternative to pure glycerol on the synthesis of polyesters without inducing major changes on the synthesis products.
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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 | 0.001 | 0.001 |
| 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 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".