Graphite Oxide Improves Adhesion and Water Resistance of Canola Protein–Graphite Oxide Hybrid Adhesive
Bibliographic record
Abstract
Protein derived adhesives are extensively explored as a replacement for synthetic ones, but suffers from weak adhesion and water resistance. Graphite oxide (GO) has been extensively used in nanocomposites, but not in adhesives applications. The objectives of this study were to prepare functionally improved protein adhesive by exfoliating GO with different oxidation levels, and to determine the effect of GO on adhesion mechanism. GO were prepared by oxidizing graphite for 0.5, 2, and 4 h (GO-A, GO-B and GO-C, respectively). Increasing oxidation time decreased C/O ratio; while the relative proportion of C-OH, and C = O groups initially increased up to 2 h of oxidation, but reduced upon further oxidation. Canola protein-GO hybrid adhesive (CPA-GO) was prepared by exfoliating GO at a level of 1% (w/w). GO significantly increased (p < 0.05) adhesion; where GO-B addition showed the highest dry, and wet strength of 11.67 ± 1.00, and 4.85 ± 0.61 MPa, respectively. The improvements in adhesion was due to the improved exfoliation of GO, improved adhesive and cohesive interactions, increased hydrogen bonding, increased hydrophobic interactions and thermal stability of CPA-GO. GO, as we proposed for the first time is easier to process and cost-effective in preparing protein-based adhesives with significantly improved functionalities.
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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".