Green Approaches To Engineer Tough Biobased Epoxies: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it