Dual Physically Cross‐Linked Hydrogels Incorporating Hydrophobic Interactions with Promising Repairability and Ultrahigh Elongation
Bibliographic record
Abstract
Abstract Novel dual physically cross‐linked (DPC) hydrogels with great tensile strength, ultrahigh elongation, and promising repairability are designed by introducing cellulose nanocrystal (CNC) or hydrophobized CNC (CNC‐C8) into polymers physically cross‐linked by hydrophobic forces. C18 alkyl chain is grafted to N‐[3‐(dimethylamino)propyl]methacrylamide (DMAPMA) for hydrophobic monomer (DMAPMA‐C18), and C8 to CNC surface for hydrophobic CNC (CNC‐C8). CNC‐C8 (or CNC) DPC hydrogels are synthesized, with monomers N,N‐dimethylacrylamide (DMAc) and DMAPMA‐C18 polymerized to form the first network physically cross‐linked by hydrophobic interactions, on which the secondary cross‐linking points are formed by hydrophobic interactions between CNC‐C8 and DMAPMA‐C18, electrostatic interactions between CNC‐C8 (or CNC) and DMAPMA, as well as hydrogen bonding between CNC‐C8 (or CNC) and DMAc. Compared with optimum CNC DPC hydrogels of the highest tensile strength (238 ± 8 kPa), the optimum CNC‐C8 DPC hydrogel with 0.0675 w/v% DMAPMA‐C18 and 0.4 w/v% CNC‐C8 possesses stronger tensile strength of 331 ± 32 kPa and excellent elongation of 4268% ± 1446% as well, demonstrating the enhanced mechanical property of the hydrogel by introduced hydrophobic interactions. In addition, such DPC hydrogel can be facilely repaired with tetrahydrofuran (THF) on the cut surfaces while retaining good tensile stress and elongation behaviors.
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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".