Preparation and application technology of powdered phenol-formaldehyde resin adhesives
Bibliographic record
Abstract
The preparation and application technology of the powdered phenol-formaldehyde resin adhesives(P-PF resins) were explored.The results showed that the brominable substance content,the free formaldehyde content,the free phenol content and the gelation time of P-PF resins obtained by spray drying were lower than that of the corresponding liquid phenol-formaldehyde resin adhesives(L-PF resins).The viscosity of L-PF resins had a clear effect on the normal properties of P-PF resins.The results from the direct application of P-PF resins to bamboo-based waferboard showed that the shape and size of the particle of P-PF resins and the moisture content of the wafer had vital effects on internal bonding strength(IB) of the board.The properties of board bonded with P-PF resins were better than those of board bonded with L-PF resins under the same condition.The optimum process parameters of manufacturing the bamboo-based waferboard were as follows: mat compression ratio of 1∶1.4(board density 0.86 g/cm3),sizing amount of 3.5%,wafer moisture content of 12%,hot pressing at(165±5)℃ and under 8.75 MPa,closing speed of 2.5 mm/s and pressing time of 1.35～1.45 min/mm.Waferboard made under the above conditions had excellent properties equal or even superior to the requirements of Canadian Standard CAN 3-O437.0-M85.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".