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Record W1989325610 · doi:10.1002/app.26229

Grafting of poly (methyl acrylate) onto sulfite pulp fibers and its effect on water absorbance

2007· article· en· W1989325610 on OpenAlexaff
Vaibhav Jain, Huining Xiao, Yonghao Ni

Bibliographic record

VenueJournal of Applied Polymer Science · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGraftingPulp (tooth)Materials scienceSulfiteCeric ammonium nitrateMethyl acrylatePolymer chemistryCelluloseCrystallinityChemical engineeringNuclear chemistryMonomerChemistryComposite materialPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract To prepare super water absorbent hydrogels of wood cellulose fibers, poly (methyl acrylate) (PMA) was copolymerized onto softwood sulfite pulp fibers using free radical initiator followed by alkaline hydrolysis. Ceric ammonium nitrate (CAN) was used as the free radical initiator. Effects of various parameters such as fiber concentration, monomer/pulp (M/pulp) ratio, CAN concentration, and reaction time on the grafting yield and on other grafting parameters were investigated. The graft conversion was the same from low to medium fiber concentration. The amount of initiator required was found to be independent of fiber concentration to achieve maximum grafting yield. Different fiber fractions (classified based on their length) have no effect on the grafting yield. The evidence of graft copolymerization was determined by using ATR‐IR spectroscopy. The X‐ray diffraction (XRD) analysis shows that grafting takes place both in amorphous and crystalline regions of cellulose fibers and the decrease in crystallinity of the grafted fibers with an increase in grafting yield was confirmed. The surface morphology of the PMA‐g‐cellulose was characterized by scanning electron microscopy (SEM). The water retention value of the hydrolyzed grafted pulp was determined based on a centrifugation technique. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007

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)0.005
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 score0.017
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.287
Teacher spread0.276 · 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

Citations18
Published2007
Admission routes1
Has abstractyes

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