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 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 | 0.000 | 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) | 0.000 | 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".