Phenolated larch‐bark formaldehyde adhesive with various amounts of sodium hydroxide
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the effect of different amounts of sodium hydroxide (NaOH) introduced during the resin synthesis on the properties of bark‐phenol‐formaldehyde (BPF) adhesives aims at achieving a balance between storage life and other properties of BPF adhesives. Design/methodology/approach Based on the best synthetic technologies for the production of BPF adhesives obtained in a previous study, a new synthetic technology is developed for the production of BPF adhesives that involve a three‐step addition of NaOH using different amounts of NaOH in the third charge. Gel permeation chromatography is used to evaluate properties of the phenol‐formaldehyde (PF) and BPF adhesives. Findings The amount of NaOH in the third charge has an important influence on many BPF adhesive properties. The paper determines that the synthetic technology involving three‐step NaOH additions with only water introduced in the third charge of NaOH produces a BPF adhesive with the longest storage life and best bonding strength. Research limitations/implications BPF adhesives are very complex systems with many unknown variables. Practical implications The improved storage life of the BPF adhesive prepared with the new synthetic technology is comparable to that of a commercial PF adhesive, which indicates that this new technology shows greater potential for commercial applications. Originality/value A new synthetic technology is developed to produce a BPF adhesive that is more comparable to commercial PF adhesives than other BPF adhesives in terms of storage life and other resin properties.
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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".