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Record W3176580837 · doi:10.3390/nano11061620

Ultra-Low Percolation Threshold Induced by Thermal Treatments in Co-Continuous Blend-Based PP/PS/MWCNTs Nanocomposites

2021· article· en· W3176580837 on OpenAlexafffund
Daria Strugova, José Carlos Ferreira, Éric David, Nicole R. Demarquette

Bibliographic record

VenueNanomaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaÉcole de technologie supérieure
KeywordsMaterials sciencePercolation thresholdCarbon nanotubeNanocompositePolystyreneIsothermal processPercolation (cognitive psychology)PolypropyleneComposite materialCrystallizationMorphology (biology)Chemical engineeringPolymerElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

The effect of the crystallization of polypropylene (PP) forming an immiscible polymer blend with polystyrene (PS) containing conductive multi-wall carbon nanotubes (MWCNTs) on its electrical conductivity and electrical percolation threshold (PT) was investigated in this work. PP/PS/MWCNTs composites with a co-continuous morphology and a concentration of MWCNTs ranging from 0 to 2 wt.% were obtained. The PT was greatly reduced by a two-step approach. First, a 50% reduction in the PT was achieved by using the effect of double percolation in the blend system compared to PP/MWCNTs. Second, with the additional thermal treatments, referred to as slow-cooling treatment (with the cooling rate 0.5 °C/min), and isothermal treatment (at 135 °C for 15 min), ultra-low PT values were achieved for the PP/PS/MWCNTs system. A 0.06 wt.% of MWCNTs was attained upon the use of the slow-cooling treatment and 0.08 wt.% of MWCNTs upon the isothermal treatment. This reduction is attributed to PP crystals' volume exclusion, with no alteration in the blend morphology.

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 categoriesMeta-epidemiology (narrow)
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations24
Published2021
Admission routes2
Has abstractyes

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