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Record W3210698568 · doi:10.1039/d1ra05552f

Eco-benign PVA/aluminum phosphate as an alternative to formaldehyde-based adhesives in wood-based panels

2021· article· en· W3210698568 on OpenAlexaff
Zhenzeng Wu, Tingjie Chen, John Tosin Aladejana, Zhutao Zhang, Shengwei Liang, Yuanjiao Xiao, Jiahui Lin, Xiaodong Wang, Yongqun Xie

Bibliographic record

VenueRSC Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversité Laval
FundersWuyi UniversityNatural Science Foundation of Fujian Province
KeywordsAdhesiveFourier transform infrared spectroscopyDifferential scanning calorimetryThermogravimetryX-ray photoelectron spectroscopyMaterials scienceFormaldehydeScanning electron microscopeNuclear chemistryChemical engineeringComposite materialPolymer chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Aluminum phosphate (AP) shows great potential to replace formaldehyde-based adhesives in the wood industry, except for its weak hygroscopic resistance and low wet bonding strength. This study chose PVA as an AP modifier to prepare a PVA-AP organic-inorganic hybrid adhesive (PAP). The preparation, bonding mechanism and heat resistant property of PAP were studied by using X-ray photoelectron spectroscopy (XPS), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetry-differential scanning calorimetry (TG-DSC), nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM). The result showed that covalent bonds between PVA and AP were built. The mechanical properties of PAP improved remarkably; the dry and wet bonding strength are 2.28 and 0.79 MPa with 15.2% and 690% increment, respectively, compared to the control samples. The thermostabilities of PAP and plywood samples were improved. In conclusion, PVA could effectively improve the hygroscopic resistance and low wet bonding strength of AP adhesives.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.249
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations11
Published2021
Admission routes1
Has abstractyes

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Same venueRSC AdvancesSame topicLignin and Wood ChemistryFrench-language works237,207