Biocompatible Catechol‐Functionalized Cellulose‐Based Adhesives with Strong Water Resistance
Bibliographic record
Abstract
Abstract Numerous traditional adhesives have good adhesion in dry environments. However, non‐environmental‐friendliness and poor water resistance largely limit their practical applications. To prepare biocompatible adhesives with strong water resistance and adhesion strength, in this paper, catechol‐functionalized cellulose‐based adhesive polymers are synthesized by grafting N‐(3,4‐dihydroxyphenethyl)methacrylamide and methyl methacrylate onto cellulose chain through atom transfer radical polymerization (ATRP). The successful synthesis of the catechol‐functionalized cellulose‐based adhesive polymers is confirmed by FTIR and 1H NMR. The different characteristics of the adhesive polymers, such as thermal stability, swelling ratio, biocompatibility, and adhesion strength are investigated. Strong water resistance on various substrates is realized in underwater environment for the catechol‐functionalized cellulose‐based adhesive with addition of Fe3+. The adhesion strength and thermal stability are enhanced when the catechol content is increased. The adhesive with catechol content of 25.4% shows the adhesion strength of 0.45 MPa for iron substrate in underwater environment. In addition, the adhesive with addition of Fe3+ exhibits excellent adhesion in dry environment, with maximum adhesion strength of 3.50 MPa for iron substrate. The cell culture test shows that the adhesive polymers have excellent biocompatibility. The biocompatible adhesives with strong water resistance have potential application in electronic, wood, and building fields.
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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".