A study of mechanical properties of biobased epoxy network: Effect of addition of epoxidized soybean oil and poly(furfuryl alcohol)
Bibliographic record
Abstract
ABSTRACT A novel biobased thermoset interpenetrating network was introduced in this study. Epoxidized soybean oil (ESO) and poly(furfuryl alcohol) (PFA) were added to a commercial biobased epoxy resin. It was hypothesized that addition of ESO and PFA can decrease brittleness of bioepoxy resin and also increase biobased content. Mechanical properties of samples were evaluated using tensile and impact test. It was found that the addition of ESO and PFA increased notched Izod impact energy by 76.6%. This significant increase was related to incorporation of long flexible chains of ESO into the matrix. Hybridization of ESO and PFA in bioepoxy reduced tensile strength (around 70%), tensile modulus (around 90%), and glass transition temperature in comparison to neat bioepoxy. Tensile strength and modulus of hybridized system can be further improved by addition of natural fibers and the resultant composite may be considered as a good candidate for applications in which damping properties are important. Crosslink density was calculated using dynamic mechanical analysis and a decrease in crosslink density was observed in hybridized system. PFA domains were observed in the matrix using atomic force microscopy in peak force quantitative nano‐mechanical mode and it revealed inhomogeneity in the crosslinked structure. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 44352.
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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".