Nanochitin-Fortified Polyphenol Complexes for Dry and Wet Adhesion
Bibliographic record
Abstract
Synthetic adhesives commonly used in shipbuilding, plumbing, and various industrial and household applications pose environmental and health concerns due to chemical leaching and other issues. In this work, we present a sustainable alternative using chitin nanofibers (ChNF) to enhance the networking and surface binding of biomolecules. We investigate aqueous-based formulations composed of tannic acid (TA), poly(vinyl alcohol) (PVA), and chitin nanofibers, which form robust adhesive complexes. These are driven by multiple interactions involving phenolic and hydroxyl groups, which are present at high densities and contribute to exceptional adhesion upon drying. Unlike most two-component structural adhesives, the ChNF-based adhesives introduced here do not rely on organic solvents and demonstrate versatility across surfaces with contrasting topologies and surface energies, including stainless steel, polypropylene, wood, and others. With an ultimate shear strength reaching up to 20 MPa, these adhesives rival commercially available structural adhesives commonly used for bonding metals, wood, and glass. The addition of chitin nanofibers enhances adhesion by up to 400%, depending on the PVA-to-TA ratio. Furthermore, these adhesives exhibit long-term structural integrity under wet conditions, showing no signs of swelling or degradation. To elucidate the mechanisms underlying adhesion in both wet and dry states, we conducted comprehensive analyses, including morphological, mechanical, rheological, spectroscopic, thermal, and surface characterizations. The findings highlight the potential of ChNF-based adhesives as a viable and sustainable alternative for diverse industrial applications.
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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".