MétaCan
Menu
Back to cohort
Record W2053651851 · doi:10.1002/app.30325

Preparation and properties of wheat straw fiber‐polypropylene composites. I. Investigation of surface treatments on the wheat straw fiber

2009· article· en· W2053651851 on OpenAlexaff
Mingzhu Pan, Zhou Ding-guo, James Deng, S. Y. Zhang

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovations
Fundersnot available
KeywordsStrawCrystallinityThermogravimetric analysisMaleic anhydrideMaterials scienceFiberCelluloseThermal stabilityComposite materialPolypropyleneCellulose fiberNatural fiberPolymer chemistryPolymerChemistryOrganic chemistryCopolymer

Abstract

fetched live from OpenAlex

Abstract Effects of alkalization, acetylation, and maleic anhydride grafted polypropylene (MAPP) treatments on the thermal and chemical properties of the wheat straw fiber were investigated using thermogravimetric analysis, infrared spectrophotometer, X‐ray diffraction, and scanning electric microscopy techniques. It was found that the wheat straw fiber was not prone to weight loss at 170°C and the treated wheat straw fiber exhibited more thermal stability than the untreated wheat straw fiber. Alkalization increased relative cellulose content and exhibited more crystalline due to a rearrangement of the crystalline regions. It also prolonged the degradation of the wheat straw fiber at higher temperatures due to the increased crystallinity of cellulose. Compared with alkalization, acetylation had more effect on the thermal and chemical stability in the wheat straw fiber contributing to a formation of ester bonding. MAPP improved the thermal stability partly because of a lower grafted ratio of maleic anhydride. Acetylation and MAPP treatment both decreased the crystallinity of the wheat straw fiber. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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)0.001
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 score0.001
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Polymer ScienceSame topicNatural Fiber Reinforced CompositesFrench-language works237,207