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Record W3011723264 · doi:10.3390/ma13061327

Effect of the Pyro-Gasification Temperature of Wood on the Physical and Mechanical Properties of Biochar-Polymer Biocomposites

2020· article· en· W3011723264 on OpenAlexafffund
Ramzi Ayadi, Ahmed Koubaa, Flavia Lega Braghiroli, Sébastien Migneault, He Wang, Chedly Bradaï

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCentre Technologique des Résidus IndustrielsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsBiocharMaterials scienceComposite materialPolypropyleneWettingPolymerPolyethyleneComposite numberFourier transform infrared spectroscopyChemical engineeringExtrusionPyrolysis

Abstract

fetched live from OpenAlex

The physical and mechanical properties of wood (WPC) and biochar polymer composites (BPC) obtained at different pyro-gasification temperatures and different fiber proportions were investigated. Composite pellets made from wood chips or biochar and thermoplastic polymers (polypropylene or high-density polyethylene) were obtained by twin-screw extrusion, and test specimens were prepared by injection molding. Results showed that BPCs were more dimensionally stable compared to WPCs, but their mechanical properties decreased with increasing pyro-gasification temperatures due to the poor adhesion between the polymer and biochar. Indeed, FTIR investigations revealed the decrease or absence of hydroxyl groups on biochar, which prevents the coupling agent from reacting with the biochar surface. The change in the biochar chemical structure led to an improvement in the dimensional stability and hydrophobicity of the biocomposites. Despite the increased dimensional stability of BPCs compared to WPCs, BPCs still adsorb water. This was explained by the surface roughness and by the biochar agglomerations present in the composite. In conclusion, the thermochemical conversion of black spruce wood chips into biochar makes it brittle but more hydrophobic, thereby reducing the wettability of the BPCs.

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 score0.003
Threshold uncertainty score0.252

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations31
Published2020
Admission routes2
Has abstractyes

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