Synthesis and Process Engineering of Poly(glycerol-co-diacids) for Thermoplastic Materials Development
Bibliographic record
Abstract
Biobased polymeric materials, obtained from renewable biomass resources, are seen as a platform which could enable the transition to a more environmentally sustainable economy, by gradually replacing fossil based counterparts. In this context, this thesis investigated the synthesis and usage of a biobased polymeric material, poly(glycerol succinate) (PGS) and its copolyesters, in thermoplastic blend materials synthesis and processing. Upgrading glycerol usage by its value added application in synthesis of materials as proposed in this thesis work is considered as a key step towards enabling oleaginous biomass biorefinery sustainable growth. To compensate for the low stiffness and high toughness of the synthesized polyesters, industrially relevant melt blending techniques were used and the synthesized poly(glycerol succinate) polyesters were melt blended with commercially available biobased thermoplastics poly(lactic acid) (PLA) and poly(butylene succinate) (PBS). The melt blending of these three polymeric materials allowed to achieve thermoplastic blend materials displaying tensile properties and notched Izod impact resistance comparable to petroleum based poly(propylene) and its copolymers. In a first section, synthesis conditions for the polycondensation of glycerol and succinic acid were screened, aiming to produce a toughness increase in PLA by melt blending with PGS. Incorporation of maleic anhydride as comonomer for the synthesis was employed as a tool for the reactive extrusion of PLA and poly(glycerol succinate-co-maleate) (PGSMA) in presence of free radical initiators, which favored the toughening of PLA by addition of PGSMA. In a second section, the reactive extrusion of PLA and PGSMA was investigated and the role of the grafting and crosslinking reactions occurring in the extrusion process was studied. In a third section, the incorporation of PBS to the blend system was evaluated. The addition of PBS allowed for the creation of biobased ternary blend materials with notched Izod impact resistance higher than 100 J/m, while maintaining tensile strength and modulus on the range of 20 – 30 MPa and 1 – 1.9 GPa, respectively. Linear regression models constructed accurately predicted a range of blend compositions were tensile properties and notched Izod impact resistance of the biobased PGSMA/PLA/PBS blend formulated was comparable to petroleum based poly(propylene) and its copolymers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".