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Record W4402483920 · doi:10.1002/ppap.202400137

Electrophoretic Deposition of Multi‐Walled Carbon Nanotubes: The Key Role of Plasma Functionalization and Polymerization

2024· article· en· W4402483920 on OpenAlexafffund
Lynn Hein, Sylvain Coulombe, Renzo Cecere, Rosaire Mongrain

Bibliographic record

VenuePlasma Processes and Polymers · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsMcGill University Health CentreMcGill University
FundersMcGill University Health CentreNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill UniversityMcGill University
KeywordsSurface modificationCarbon nanotubePlasma polymerizationElectrophoretic depositionMaterials sciencePlasmaChemical engineeringNanotechnologyDeposition (geology)PolymerizationKey (lock)Polymer chemistryPolymerComposite materialComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

ABSTRACT The electrophoretic deposition of multi‐walled carbon nanotubes (MWCNTs) has been well‐researched; however, preparatory steps lead to MWCNT coating contamination and deposits often have weak adhesion to the substrate. This work targets these two weaknesses. First, MWCNTs were functionalized by nonthermal, radiofrequency plasma, producing oxygenated MWCNTs (O‐MWCNTs), with which water‐based suspensions were prepared. Second, an ethane‐based plasma polymer was applied on the metallic substrate as an interlayer to improve coating adhesion. O‐MWCNT coatings were produced at 5–40 V for 1–60 min. Homogeneous coatings with thicknesses up to 10 µm were achieved, the composition was 90‐95 at% carbon with the balance element being oxygen, and coating adhesion without damage was confirmed for shear stresses up to 16 Pa.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes2
Has abstractyes

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