Fabricating ultra-robust hydrogels with adhesive properties by restraining crack propagation with bamboo cellulose-based carbon nanomaterials
Bibliographic record
Abstract
The bamboo fiber functionalized with phthalic anhydride underwent carbonization, yielding bamboo cellulose-derived carbon nanomaterials (C-BCN). These C-BCN were subsequently integrated into an acrylamide precursor solution to synthesize an ultra-robust, fatigue-resistant conductive hydrogel (PAM-C-BCN). During in situ polymerization, the abundant active sites on the C-BCN surface facilitated covalent cross-linking with the polyacrylamide (PAM) matrix. This interfacial interaction promoted strong adhesion between the PAM chains and the carbon nanostructures, forming a densely interpenetrated network through macromolecular entanglement. The synergistic coupling of the rigid C-BCN framework with the flexible polymer chains conferred exceptional mechanical resilience and energy dissipation capabilities to the composite hydrogel. Compared to the PAM hydrogel, the PAM-C-BCN hydrogel exhibited an improvement in mechanical properties, with a fracture strength of 363 kPa (a 2.5% increase), an elongation of approximately 2 254% (a 2.0% increase), a fracture energy of 30 kJ/m 2 (a 3.1% increase), and a toughness of 3.04 MJ/m 3 (a 4.1% increase). Moreover, PAM-C-BCN hydrogel demonstrated high adhesion (up to 7.5 kPa on pigskin) and conductivity (0.21 S/m). This strategy required neither complex design nor processing, offering a simple and efficient approach with great potential for hydrogel applications requiring high mechanical performance. At the crack tip of PAM-C-BCN hydrogel, C-BCN exhibited superior crack propagation resistance compared to SiO 2 nanoparticles. Importantly, this strategy offered valuable insights for developing tough and stretchable hydrogels.
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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".