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Record W2155912383 · doi:10.5539/jmsr.v3n2p8

Combination of Esterified Kraft Lignin and MAPE as Coupling Agent for Bark/HDPE Composites

2013· article· en· W2155912383 on OpenAlexafffundvenue
Nicolas Mariotti, Xiang Ming Wang, Denis Rodrigue, Tatjana Stevanović

Bibliographic record

VenueJournal of Materials Science Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovationsNatural Sciences and Engineering Research CouncilUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesFPInnovations
KeywordsHigh-density polyethyleneMaterials scienceLigninDifferential scanning calorimetryComposite materialPolyethyleneThermogravimetric analysisMaleic anhydrideSuccinic acidUltimate tensile strengthFlexural strengthKraft paperSuccinic anhydridePolymerOrganic chemistryChemistryCopolymer

Abstract

fetched live from OpenAlex

Refined black spruce bark fibers and high density polyethylene (HDPE) were used to produce wood plastic composites (WPC) while exploring for the first time the potential of using esterified Kraft lignin as a coupling agent. Two types of lignin esterification (succinic acid and maleic acid anhydrides) were examined alone or in combination with maleated polyethylene (MAPE), and compared with a commercial coupling agent: maleated polyethylene (MAPE). Three contents of coupling agents (2%; 5%; 10% by weight) were considered for esterified lignin and three combinations of lignin and MAPE (100:0; 50:50; 0:100), while bark fibers content was kept constant at 30%. Morphological (scanning electron microscopy - energy dispersive X-ray spectroscopy [SEM-EDS]), thermal (thermogravimetric analysis [TGA] and differential scanning calorimetry [DSC]) and mechanical properties (tensile and flexural) of the composites were measured. Two optimum formulations were determined based on the best bark-matrix interface leading to highest mechanical properties: an equal content of maleic acid anhydride esterified lignin and MAPE at 5%, or an equal content of succinic esterified lignin and MAPE at 2%.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.052
GPT teacher head0.369
Teacher spread0.317 · 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
Published2013
Admission routes3
Has abstractyes

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