The Development of Epoxidized Hemp Oil Prepolymers for the Preparation of Thermoset Networks
Bibliographic record
Abstract
Abstract Epoxy thermosets comprised of plant oils along with simple curing agents are sustainable and environmentally friendly polymers. The curing agent selected, and its compatibility with epoxy monomers, strongly affects the curing kinetics, the extent of curing, and the final properties of an epoxy polymers. The goal of this work is to expand the application of epoxidized oils in formulating biobased thermoset polymer systems. Epoxidized hemp oil (EHO) was produced with 8% oxirane oxygen content (OOC) after 24 hours using in situ generated performic acid. Two model curing agents—one aromatic (trimellitic anhydride, TMA) and one biobased nonaromatic (citric acid, CA)—with similar molecular weights were selected to study the cure behavior of EHO in acetone. Both curing agents are insoluble in EHO. The prepolymerization curing reaction behavior was monitored via the OOC, attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy, and gel permeation chromatography. It was demonstrated that at 50 °C, the reaction of EHO with TMA was extremely fast to form esters of TMA, while the reaction of EHO with CA was slower and followed different pathways. The cured EHO/TMA epoxy network is rigid and has a high alpha relaxation temperature (T α) of 89 °C, which is associated with the glass transition temperature (T g), while the cured EHO/CA network system is semirigid with a T α of 40 °C. In addition, TGA analysis showed that the EHO/TMA resin system represents a more homogenous structure compared to the EHO/CA system, as indicated by the presence of lower‐temperature decompositions of citric acid derivatives.
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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".