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Record W4402482205 · doi:10.3390/polym16182582

Modification of the Surface Crystallinity of Polyphenylene Sulfide and Polyphthalamide Treated by a Pulsed-Arc Atmospheric Pressure Plasma Jet

2024· article· en· W4402482205 on OpenAlexaff
Abdessadk Anagri, Sarab Ben Saïd, Cyrille Bazin, Farzaneh Arefi‐Khonsari, Jérôme Pulpytel

Bibliographic record

VenuePolymers · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsCegep de Thetford
FundersAssociation Nationale de la Recherche et de la Technologie
KeywordsCrystallinityMaterials scienceAtmospheric-pressure plasmaWettingJet (fluid)Atmospheric pressurePlasmaSurface energySurface modificationPolymerChemical engineeringSulfideNitrogenComposite materialAnalytical Chemistry (journal)MetallurgyChemistryEnvironmental chemistryOrganic chemistryMeteorologyAerospace engineering

Abstract

fetched live from OpenAlex

Atmospheric plasma jets generated from air or nitrogen using commercial sources with relatively high energy densities are commonly used for industrial applications related to surface treatments, especially to increase the wettability of polymers or to deposit thin films. The heat fluxes to which the substrates are subjected are typically in the order of 100–300 W/cm2, depending on the treatment conditions. The temperature rise in the treated polymer substrates can have critical consequences, such as a change in the surface crystallinity or even the surface degradation of the materials. In this work, we report the phase transitions of two semicrystalline industrial-grade polymer resins reinforced with glass fibers, namely polyphenylene sulfide (PPS) and polyphthalamide (PPA), subjected to plasma treatments, as well as the modeling of the associated heat transfer phenomena using COMSOL Multiphysics. Depending on the treatment time, the surface of PPS becomes more amorphous, while PPA becomes more crystalline. These results show that the thermal history of the materials must be considered when implementing surface engineering by this type of plasma discharge.

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 score0.018
Threshold uncertainty score0.568

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)0.0010.000

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 head0.015
GPT teacher head0.240
Teacher spread0.226 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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