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Chemical characteristics of thermo-hydrolytically recycled particles

2020· article· en· W3030375467 on OpenAlexaff
Qilan Fu, Xiang‐Ming Wang, Alain Cloutier, Fabrice Roussière

Bibliographic record

VenueBioResources · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité LavalFPInnovations
Fundersnot available
KeywordsMaterials scienceHydrolysisUrea-formaldehydeDecompositionBuffer (optical fiber)FormaldehydeThermal decompositionChemical engineeringBase (topology)PolymerComposite materialNuclear chemistryChemistryOrganic chemistryAdhesive

Abstract

fetched live from OpenAlex

Particles from waste laminated particleboards were recovered using various thermo-hydrolytic treatments. The size distribution and three main chemical properties, such as pH, buffer capacity, and nitrogen content of the control and resulting particles were determined. The effects of treatment temperature and duration on the chemical properties of recycled particles were investigated. The effects of the resulting particles on the gel time of urea-formaldehyde (UF) resin were also measured. The test results suggested that the pH of particles after hot water soaking pre-treatment and the different thermo-hydrolytic treatments increased to different extents, with the exception of the particles treated at 140 °C for 50 min and 160 °C for 20 min. The acid buffer capacity and base buffer capacity of particles treated at 140 °C for 50 min and 160 °C for 20 min had no statistical difference, but they were much higher than those of other types of treated particles. A high treatment temperature facilitated the decomposition of wood polymers and UF resin. Both temperature and treatment duration had significant effects on pH, acid buffer capacity, and base buffer capacity of wood particles. Wood particles recycled at a high temperature had a negative effect on the gel time of UF resin.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.371

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 head0.016
GPT teacher head0.220
Teacher spread0.205 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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