Highly Toughened and Transparent Biobased Epoxy Composites Reinforced with Cellulose Nanofibrils
Bibliographic record
Abstract
Biobased nanofillers, such as cellulose nanofibrils (CNFs), have been widely used as reinforcing fillers for various polymers due to their high mechanical properties and potential for sustainable production. In this study, CNF-based composites with a commercial biobased epoxy resin were prepared and characterized to determine the morphology, mechanical, thermal, and barrier properties. The addition of 18⁻23 wt % of CNFs to epoxy significantly increased the modulus, strength and strain of the resulting composites. The addition of fibrils led to an overall increase in strain energy density or modulus of toughness by almost 184 times for the composites compared to the neat epoxy. The addition of CNFs did not affect the high thermal stability of epoxy. The presence of nanofibrils had a strong reinforcing effect in both glassy and glass transition region of the composites. A significant decrease in intensity in tan δ peak for the epoxy matrix occurred with the addition of CNFs, indicating a high interaction between fibrils and epoxy during the phase transition. The presence of highly crystalline and high aspect ratio CNFs (23 wt %) decreased the water vapour permeability of the neat epoxy resin by more than 50%.
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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".