Study of the Curing Kinetics of Epoxy Resins with Biobased Hardener and Epoxidized Soybean Oil
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The goal of this research was to study the kinetics of the reaction of diglycidyl ether of bisphenol A (DGEBA)-based epoxy resin cured with sebacic acid as a biobased hardener in the presence of three different loadings of epoxidized soybean oil (ESO) (i.e., 10, 20, 30 wt %). Nonisothermal differential scanning calorimetric (DSC) and model-free isoconversional method was used to analyze the curing kinetic data and determine the activation energy of the reactions. It was found that the biobased hardener increased the enthalpy of reaction as well as the activation energy of reaction in comparison to the amine hardeners that are currently used for epoxy curing. The addition of epoxidized soybean oil increased the enthalpy of reaction, maximum exothermic temperature, and activation energy of the system. Kissinger–Akahira–Sunose (KAS) and Starink methods were used to determine the activation energy of the studied systems. It was also found that the curing reaction of epoxy with 30 wt % of ESO is diffusion controlled in comparison with other counterparts.
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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".