MétaCan
Menu
Back to cohort
Record W4297348410 · doi:10.3390/coatings12101406

Fabrication of a Conductive Additive for the Anticorrosion Enhancement of Zinc-Rich Epoxy Coatings

2022· article· en· W4297348410 on OpenAlexaff
Yuxing Bai, Xuliang Jin, Junqing Xie, Xiao Lv, Tingting Guo, Li Zhang, Jesse Zhu, Yuanyuan Shao, Haiping Zhang, Hui Zhang, Bin Yuan, Aiming Yin, Jinfeng Nie, Fan Cao, Zhengjun Xu

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceDielectric spectroscopyCorrosionCoatingZincComposite materialEpoxySalt spray testCathodic protectionGloss (optics)PolypyrroleElectrochemistryChemical engineeringMetallurgyPolymerElectrodeChemistry

Abstract

fetched live from OpenAlex

In the study, a conductive polypyrrole (PPy) is deposited on the lamellar sericite powder (SCP) surfaces by an in situ oxidization growth method and the prepared PPy/SCP conductive additive is successfully applied on the zinc-rich primer (ZRP) coating. The equal mass substitution and the equal volume substitution methods of the conductive additives to zinc dusts are discussed, as well as the optimal replacing ratio to achieve the best corrosion protection effect of the ZRP coatings. The results indicate that the equal volume substitution method is in favor of corrosion resistance of coating film. The salt spray test and the electrochemical impedance spectroscopy (EIS) and polarization curves show that the prepared ZRP coating with a 66% zinc content and replacing ratio of 1:3 possesses the best corrosion-resistant performance and an optimal adhesion strength. The replacement of PPy/SCP particles to zinc dusts using the equal volume substitution method is feasible to achieve the improvement in anticorrosion ability through a synergic function of the cathodic protection effect and barrier effect.

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 score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.288
Teacher spread0.257 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCoatingsSame topicConducting polymers and applicationsFrench-language works237,207