Highly Stretchable, Repairable, and Tough Nanocomposite Hydrogel Physically Cross‐linked by Hydrophobic Interactions and Reinforced by Surface‐Grafted Hydrophobized Cellulose Nanocrystals
Bibliographic record
Abstract
Abstract Highly stretchable, repairable, and tough nanocomposite hydrogels are designed by incorporating hydrophobic carbon chains to create first‐layer cross‐linking among the polymer matrix and monomer‐modified polymerizable yet hydrophobic nanofillers to create second‐layer strong polymer‐nanofiller clusters involving mostly covalent bonds and electrostatic interactions. The hydrogels are synthesized from three main components: hydrophobic monomer DMAPMA‐C18 by reacting N‐[3‐(dimethylamino)propyl]methacrylamide] (DMAPMA) with 1‐bromooctadecane, monomer N,N‐dimethylacrylamide (DMAc), and monomer‐modified polymerizable hydrophobized cellulose nanocrystal(CNC‐G) obtained by reacting CNC with 3‐trimethoxysily propyl methacrylate. The polymerization of DMAPMA‐C18 and DMAc and physical cross‐linking due to the hydrophobic interactions between C18 chains make DMAPMA‐C18/DMAc hydrogel. The additional introduction of CNC‐G brings more interactions into the final hydrogel (DMAPMA‐C18/DMAc/CNC‐G): the covalent bonds between CNC‐G and DMAPMA‐C18/DMAc, hydrophobic interactions, electrostatic interactions between negatively charged CNC‐G and positively charged DMAPMA‐C18, and hydrogen bonds. The optimum DMAPMA‐C18/DMAc/CNC‐G hydrogel exhibits excellent mechanical performance with elongation stress of 1085 ± 14 kPa, strain of 4106 ± 311%, toughness of 3.35 × 104 kJ m−3, Young's modulus of 844 kPa, and compression stress of 5.18 MPa at 85% strain. Besides, the hydrogel exhibits good repairability and promising adhesive ability (83–260 kN m−2 toward various surfaces).
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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".