Industrial byproducts as adhesive allies: Unraveling the role of proteins and isocyanates in polyurethane wood bonding
Bibliographic record
Abstract
Wooden structures are becoming increasingly popular in the construction world. However, these structures often rely on synthetic adhesives, raising concerns about the environmental risks associated with their chemical composition. In response to these concerns, this study aims to explore sustainable alternatives, particularly focusing on polyurethane adhesives that incorporate proteins from industrial byproducts. The investigation involved three protein sources: soybean meal, shrimp shells, and skim milk, modified under mild alkaline conditions to obtain protein concentrates. These concentrates were then incorporated into the adhesives at varying protein contents: 5%, 10%, and 15%. Additionally, two isocyanate systems were examined, one being petrochemical-based and the other a partially bio-based blend. Chemical, thermal, optical, and mechanical characterizations were conducted to evaluate the adhesive performance. This study demonstrates that the adhesives’ thermal properties remain unaffected by both the protein content and the isocyanate system. However, these factors influence the adhesive penetration into the wood substrate. Ultimately, the results suggest that higher protein content offers superior retention of mechanical strength in adhesives compared to the petrochemical reference when subjected to humid conditions. Overall, this research demonstrates the potential of proteins from industrial byproducts as sustainable adhesive allies, providing valuable insights into their interactions with different isocyanates.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".