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Record W2898853942 · doi:10.21967/jbb.v3i4.126

Synthesis and characterization of glycidyl methacrylate (GMA) grafted eucalyptus fibers

2018· article· en· W2898853942 on OpenAlexvenueno aff
Lifang Guo, Jun Huang, Jianfeng Xi, Qingyang Ye, Huimin Zhai, Xiaojun Wang

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsGlycidyl methacrylateGraftingMaterials scienceThermogravimetric analysisFourier transform infrared spectroscopyCrystallinityPolymer chemistryThermal stabilityMonomerCelluloseNuclear chemistryChemical engineeringChemistryComposite materialPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Graft-copolymerization of bleached kraft eucalyptus fibers was carried out with glycidyl methacrylate monomer by using Fe 2+ -thiourea dioxide-H 2 O 2 as an initiator system. The cellulose-poly(glycidyl methacrylate) graft copolymers (CPGMA) were characterized via Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), 13 C cross polarization magic angle spinning NMR spectra ( 13 C CP/MAS NMR), X-ray diffraction (XRD) and thermal gravimetric analysis (TGA). Results showed the grafting percent was from 0 to 244%, the epoxy group content from 0 to 4.37 mmol/g, and the grafting efficiency over 97%, when the grafting percent exceeded 38%. Additionally, the morphological analysis denoted that the grafting not only took place on the fiber surface, but also inside the fiber wall. Both FTIR and solid state 13 C CP/MAS NMR analysis identified the occurrence of grafting. Fiber crystallinity was strongly affected by the percent grafting, decreasing from 69.8% (eucalyptus pulp) to 26.2% at the percent grafting of 198%. TGA analytical data indicated that GMA grafting resulted in the reduction of thermal stability of eucalyptus fibers at the percent grafting of 198%.

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 score0.012
Threshold uncertainty score0.365

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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