Enhancing the Adhesive Strength of a Plywood Adhesive Developed from Hydrolyzed Specified Risk Materials
Bibliographic record
Abstract
The current production of wood composites relies mostly on formaldehyde-based adhesives such as urea formaldehyde (UF) and phenol formaldehyde (PF) resins. As these resins are produced from non-renewable resources, and there are some ongoing issues with possible health hazard due to formaldehyde emission from such products, the purpose of this research was to develop a formaldehyde-free plywood adhesive utilizing waste protein as a renewable feedstock. The feedstock for this work was specified risk material (SRM), which is currently being disposed of either by incineration or by landfilling. In this report, we describe a technology for utilization of SRM for the development of an environmentally friendly plywood adhesive. SRM was thermally hydrolyzed using a Canadian government-approved protocol, and the peptides were recovered from the hydrolyzate. The recovered peptides were chemically crosslinked with polyamidoamine-epichlorohydrin (PAE) resin to develop an adhesive system for bonding of plywood specimens. The effects of crosslinking time, peptides/crosslinking agent ratio, and temperature of hot pressing of plywood specimens on the strength of formulated adhesives were investigated. Formulations containing as much as 78% (wt/wt) peptides met the ASTM (American Society for Testing and Materials) specifications of minimum dry and soaked shear strength requirement for UF resin type adhesives. Under the optimum conditions tested, the peptides–PAE resin-based formulations resulted in plywood specimens having comparable dry as well as soaked shear strength to that of commercial PF resin.
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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 | 0.001 |
| 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".