Bibliographic record
Abstract
Due to an increased environmental awareness and thus, concerns over the use of fossil-based monomer for polymer production, there is an ongoing effort to find alternatives to non-renewable traditional monomers. This has ushered in the rapid growth in the development of bio-based materials such as green monomers and biodegradable polymers from vegetable and animal resources. Glycerol, as a renewable bio-based monomer, is an interesting candidate for sustainable polymer production. Glycerol is a renewable material that is a by-product of the transesterification of vegetable oils to biodiesel. Utilization of the excess glycerol derived from the growing biodiesel industry is important to oleochemical industries. The main objective of this thesis was to produce high molecular weight polyglycerol from glycerol and synthesize stimuli-responsive polyglycerol hydrogels. The work began with an investigation of the step-growth polymerization of glycerol to relatively high molecular weight polyglycerol using several catalysts. The catalytic reaction mechanisms were compared and the polymer products were fully analyzed. High molecular weight partially branched polyglycerol with multimodal molecular weight distributions was obtained. The polymerization of glycerol proceeded fastest with sulphuric acid as catalyst as indicated by the highest observed conversion of monomer along with the highest molecular weights. Theoretical models were used to predict the gel point and to calculate monomer functionality. High molecular weight polyglycerol was used to synthesize novel stimuli-responsive hydrogels. Real-time monitoring of step-growth polymerization of glycerol was investigated using in-line and off-line Attenuated Total Reflectance/Fourier Transform infrared (ATR-FTIR) technique.
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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) | 0.002 | 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".