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Polypropylene/Wood Flour Composites Prepared by Solid State Shear Milling

2015· article· en· W2057801810 on OpenAlexaff
Hao Shi, Li Guo, Ying Zeng, Yuan Bo Liu, Juan Zhu

Bibliographic record

VenueAdvanced materials research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsWood flourMaterials sciencePolypropyleneComposite materialFiberDispersion (optics)PolymerShear (geology)Wood-plastic compositeComposite number

Abstract

fetched live from OpenAlex

In this paper, solid state shear milling method was successfully employed to prepare polypropylene/wood flour composites (WPC) and their structure and performances were investigated. The experimental results showed through solid state shear milling, the aggregates of wood fiber were broken down and polymer closely adhered to wood fiber, which improved the dispersion of wood fiber and the interfacial interaction between PP and wood fiber. As a result, the performances of WPCs in this way were more excellent than that of WPCs by the conventional method. This study could provide a novel approach to resolve the problems of the dispersion, stabilization and compositing of wood flour with polymer matrix and prepare WPC with higher performances.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.048
GPT teacher head0.365
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.

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
Published2015
Admission routes1
Has abstractyes

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