Green Approaches To Engineer Tough Biobased Epoxies: A Review
Bibliographic record
Abstract
Abstract Epoxy resins possess a variety of excellent properties including adhesion, mechanical performance, electrical insulation and chemical resistance; however cured epoxy resin is brittle and typically petroleum based. Rising concerns about depletion of nonrenewable resources and climate change have resulted in attempts to find green alternatives for petroleum based materials and mitigate greenhouse gas emissions. The present review is aimed to discuss green approaches to overcome epoxy resins brittleness and deal with ongoing research strategies to make tough biobased epoxies. First, the key toughening modifiers such as rubbers, thermoplastics, nanofillers, dendritic and block copolymers are briefly discussed and pros and cons of each method are presented. Then, the studies that followed green approaches are thoroughly reviewed. The utilization of epoxidized vegetable oils, biobased hyperbranched polymers and biobased copolymers in epoxy matrix are discussed. The challenges for commercialization of biobased modifiers are assessed and the present and prospective status of research and development of the tough biobased epoxies are explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".