Soy-Based Adhesives Functionalized with Pressure-Responsive Crosslinker Microcapsules for Enhanced Wet Adhesion
Bibliographic record
Abstract
With excellent bonding performance, formaldehyde-based adhesives have been widely used for the production of woody materials. However, these adhesives could potentially cause harm to human health and the environment. Herein, we report an eco-friendly soy-based adhesive (SBA) by constructing a pressure-responsive crosslinker system with a core–shell structure. Isophorone diisocyanate (IPDI) was encapsulated in microcapsules as the core, and a shell mainly consisting of polyurethane was used to hinder the crosslinking reaction between the soy protein and IPDI, thus avoiding an increase in viscosity of the adhesive. As the crosslinker and curing agent for the SBA, the encapsulated IPDI in the microcapsules could be released under an external pressure, which induces in situ crosslinking of IPDI with wood and soy protein, promoting the curing reaction of the SBA and building chemical bridges between the SBA and wood, thereby decreasing curing temperature and improving water resistance of the SBA. The SBA modified with the pressure-responsive crosslinker system exhibited improved wet shear strength, moderate viscosity, low curing temperature, and very low cytotoxicity, showing great potential as an alternative to formaldehyde-based adhesives. Furthermore, the pressure-responsive microcapsule crosslinker system has been also successfully applied to functionalize many other biomass-derived adhesives (i.e., cottonseed protein, peanut meal, oxidized starch, and sodium alginate) to dramatically improve their water resistance and mechanical properties. This work provides a versatile strategy to develop formaldehyde-free, sustainable, and high-performance bio-based adhesives.
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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) | 0.001 | 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".