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Record W2364011092

Preparation and application technology of powdered phenol-formaldehyde resin adhesives

2008· article· en· W2364011092 on OpenAlexaboutno aff
Zhang Hong-jian

Bibliographic record

VenueAdhesion in China · 2008
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceAdhesiveFormaldehydePhenol formaldehyde resinPhenolComposite materialBambooOrganic chemistryChemistry
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.260
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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