Epoxidized pine oil‐siloxane: Crosslinking kinetic study and thermomechanical properties
Bibliographic record
Abstract
ABSTRACT In this study, a novel approach to toughen biobased epoxy polymer with different types of siloxanes was explored. Three different modified siloxanes, e.g., amine‐terminated polydimethyl siloxane (PDMS‐amine), glycidyl‐terminated polydimethyl siloxane (PDMS‐glycidyl), and glycidyl‐terminated polyhedral oligomeric silsesquioxane (POSS‐glycidyl) were used as toughening agents. The curing and kinetics of bioepoxy was investigated by differential scanning calorimetry and Fourier transform infrared spectroscopy. The mechanical, thermal, and morphological properties of the cured materials were investigated. Rheological characterization revealed that the inclusion of POSS‐glycidyl slightly increased the complex viscosity compared to the neat resin. The morphology of the cured bioresin was characterized by transmission electron microscopy and scanning electron microscopy. The inclusion of POSS‐glycidyl to bioepoxy resin resulted in a good homogeneity within the blends. The inclusion of PDMS‐amine or PDMS‐glycidyl was shown to have no effect on tensile and flexural properties of the bioresins, but led to a deterioration in the impact strength. However, the inclusion of POSS‐glycidyl enhanced the impact strength and elongation at break of the bioresins. Dynamic mechanical analysis showed that the siloxane modified epoxy decreased the storage modulus of the bioresins. The thermal properties, such as decomposition temperature, coefficient of linear thermal expansion, and heat deflection temperature were improved by inclusion of POSS‐glycidyl. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42451.
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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) | 0.001 | 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".