Chemically Modified Canola Protein–Nanomaterial Hybrid Adhesive Shows Improved Adhesion and Water Resistance
Bibliographic record
Abstract
Irrespective to the continuous research effort, the progress of protein based adhesives is still hindered due to poor adhesion and water resistance properties. Here we report an improved method to develop chemically modified canola protein–nanomaterial (CMCP-NM) hybrid adhesives with significantly improved adhesion and water resistance by exfoliating graphite oxide (GO) or nanocrystalline cellulose (NCC) at low addition levels (1% w/w NM/protein) in ammonium persulfate (APS) modified canola protein. Modification of canola protein with ammonium persulfate at optimum conditions (1% w/w APS/protein) significantly improved ( p < 0.05) both dry and wet adhesion strengths from 6.38 ± 0.31 and 1.98 ± 0.08 MPa to 10.47 ± 0.47 and 4.12 ± 0.23 MPa, respectively. APS induced protein cross-linking via Tyr-Tyr and Tyr-His interactions were observed, which contributed to a covalently stabilized protein network. In the second part of the study, NCC or GO were exfoliated in CMCP at 1% w/w (NCC or GO/protein) addition level. Prepared CMCP-NM adhesive showed a further increase ( p < 0.05) in both adhesion and water resistance (12.50 ± 0.26, 4.79 ± 0.23 MPa for NCC and 11.82 ± 0.42, 4.99 ± 0.28 MPa—dry and wet strength, respectively). Synergistic effects of protein cross-linking and nanomaterial exfoliation, improved cohesive interactions, thermal stability and increased hydrophobic functional groups contributed to the improvement in CMCP-NM adhesive. The outcome of this research enables the utilizing of renewable macromolecules such as protein toward replacing traditional adhesives with adverse health and environmental effects.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".