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Record W2744901889 · doi:10.1002/app.45530

Size effect of charcoal particles on the properties of bamboo charcoal/ultra‐high molecular weight polyethylene composites

2017· article· en· W2744901889 on OpenAlexaff
Suiyi Li, Haiying Wang, Chuchu Chen, Xiaoyan Li, Qiaoyun Deng, Meng Gong, Dagang Li

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of New Brunswick
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsComposite materialMaterials scienceBamboo charcoalCrystallinityUltimate tensile strengthPolyethyleneParticle sizeUltra-high-molecular-weight polyethyleneCreepYoung's modulusBambooParticle (ecology)ModulusFiberChemistry

Abstract

fetched live from OpenAlex

ABSTRACT This study was aimed at examining the size effect of charcoal particles on the properties of bamboo charcoal (BC)/ultra‐high molecular weight polyethylene (UHMWPE) composites. Four types of BC with various particle sizes were mixed with UHMWPE using a twin‐screw extruder. It was found that the melting temperature and crystallinity of the composites were slightly decreased with the addition of BC. The incorporation of BC remarkably improved the tensile properties and creep resistance of UHMWPE, and the particle size of BC strongly affected the properties of BC/UHMWPE composites. The BC with lowest particle size exhibited best reinforcement, where the tensile strength and Young's modulus were increased by 385% and 517% compared with neat UHMWPE. The composites with 70 wt % BC possessed conductivities of 16.8, 14.1, 13.5, and 10.9 S/m. The storage modulus and glass transition temperature of the composites also increased with the addition of BC. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45530.

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)0.002
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 score0.007
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.230
Teacher spread0.221 · 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
Published2017
Admission routes1
Has abstractyes

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