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Record W3137631350 · doi:10.3390/ma14061524

Mechanical and Adsorptive Properties of Foamed EVA-Modified Polypropylene/Bamboo Charcoal Composites

2021· article· en· W3137631350 on OpenAlexaff
Wenzhu Li, Jing Zhang, Jingda Huang, Yuanchao Shao, Wenbiao Zhang, Chunping Dai

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of British Columbia
FundersNatural Science Foundation of Zhejiang Province
KeywordsPolypropyleneComposite materialMaterials sciencePorosityBamboo charcoalEthylene-vinyl acetateAdsorptionComposite numberFoaming agentFlexural strengthFiberPolymerChemistryCopolymer

Abstract

fetched live from OpenAlex

Due to its excellent adsorption and humidity control function, bamboo charcoal (BC) has often been mixed with polypropylene (PP) to produce PP/BC composites for interior paneling applications. However, due to the poor foaming quality of PP, PP/BC composites suffer as a result of their high density, which limits their scope of use. Here, to improve its foaming quality, PP was modified with ethylene vinyl acetate (EVA), and then the EVA-modified PP (E-PP) was mixed with different contents of BC (0 wt.%–50 wt.%), as well as foaming agent (Azodicarbonamide, AC) and its auxiliaries (ZnO, Znst), in a twin-screw extruder, followed by hot-pressing at high temperature to obtain foamed E-PP/BC composites. The resulting composites showed good porosity and pore distribution with an increase of BC content by up to 20%. Further increase in the BC content seemed to cause the foaming performance to decrease significantly. The product density and adsorption rate increased, while the mechanical strength decreased with increasing BC content. At a BC content of 40 wt.%, the foamed E-PP/BC composite showed the best combined performance, with a density of 0.90 g/cm3, 24-h formaldehyde adsorption rate of 0.48, and bending strength of 11.59 MPa.

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 score0.002
Threshold uncertainty score0.649

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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