All-cellulose hydrogel-based adhesive
Bibliographic record
Abstract
<p>Hydrogels showing strong adhesion to different substrates have garnered significant attention for engineering applications. However, the current development of such hydrogel-based adhesive is predominantly limited to synthetic polymers, owing to their exceptional performance and an extensive array of chemical options. To advance the development of sustainable hydrogel-based adhesives, we successfully create a highly robust all-cellulose hydrogel-based adhesive, which is composed of concentrated dialcohol cellulose nanorods (DCNRs) and relies on enhanced hydrogen bonding interactions between cellulose and the substrate. We implement a sequential oxidization-reduction process to achieve this high-performance all-cellulose hydrogel, which is realized by converting the two secondary hydroxyl groups within an anhydroglucose unit into two primary hydroxyl groups, while simultaneously linearizing the cellulose chains. Such structural and chemical modifications on cellulose chains increase out-of-plane interactions between the DCNRs hydrogel and substrate, as simulations indicate. Additionally, these modifications enhance the flexibility of the cellulose chains, which would otherwise be rigid. The resulting all-cellulose hydrogels demonstrate injectability and strong adhesion capability to a wide range of substrates, including wood, metal, glass, and plastic. This green and sustainable all-cellulose hydrogel-based adhesive holds great promise for future bio-based adhesive design.</p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.006 |
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; both teacher heads agree on what is shown here.
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".