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Record W3093177092 · doi:10.1002/app.49948

Impact of renewable carbon on the properties of composites made by using three types of polymers having different polarity

2020· article· en· W3093177092 on OpenAlexaff
Arturo Rodriguez‐Uribe, Michael R. Snowdon, Mohamed A. Abdelwahab, Amandine Codou, Manjusri Misra, Amar K. Mohanty

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComposite materialMaterials sciencePolylactic acidPolymerPolypropyleneParticle sizeParticle (ecology)Polarity (international relations)Chemistry

Abstract

fetched live from OpenAlex

Abstract Pine wood derived biocarbon (BioC) is investigated as a renewable alternative to carbon black (CB) for plastics and composites applications. Three different polymers with different polarity were used to prepare the composites: polypropylene (PP), polylactic acid (PLA), and polyamide 6 (PA6). Comparatively, CB had a nodule size of ~300 nm and surface area of 8 m 2 /g, whereas BioC showed an average particle size of ~950 nm and surface area of ~260 m 2 /g, respectively. CB, in the composites, was found in large aggregations in the flow direction (FD), while BioC particles showed a better dispersion. Aggregation of CB affected mostly the mechanical strength of the composites. Furthermore, it was found that the overall performance of composites was influenced more by the polarity of the phases, rather than the particle size or the surface area of the fillers. Even when the polarity of the particles had an expected trend (PA6 > PLA > PP with BioC > CB), the work of cohesion obtained for the composites was PA6‐BioC > PP‐BioC > PLA‐BioC, showing, in particular, that the chain‐to‐chain intermolecular forces in neat PLA are stronger as compared to those developed by the particle‐matrix interactions.

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 score0.017
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.239
Teacher spread0.197 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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