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Record W2608470995 · doi:10.1021/acs.iecr.7b00714

Ceria/Acrylic Polymer Microgel Composite: Synthesis, Characterization, and Anticorrosion Application for API 5L X70 Substrate in Chloride-Enriched Medium

2017· article· en· W2608470995 on OpenAlexafffund
Ubong Eduok, Ericmoore Jossou, Ahmed A. Tiamiyu, Joseph Omale, Jerzy A. Szpunar

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research Chairs
KeywordsMaterials scienceAcrylic acidComposite numberCorrosionAdsorptionChemical engineeringDissolutionPolymerPolymerizationAnodeCathodic protectionMetalElectrochemistrySubstrate (aquarium)Composite materialPolymer chemistryMetallurgyElectrodeCopolymerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ceria/poly(acrylic acid) microgel composite with anticorrosion potential has been synthesized via an in situ polymerization method. This polymer composite matrix has demonstrated significant reduction in API 5L X70 steel corrosion in 0.5 M HCl. In-depth studies of the anticorrosion properties of this microgel have been conducted by corrosion electrochemistry, and its adsorption on steel altered both anodic dissolution and cathodic hydrogen evolution in the acid medium. Increment of CeO 2 content within the PAA/CeO 2 hybrid composite improved its surface protective performance; 1 and 5 g of CeO 2 within the composite recorded 82 and 90% inhibition efficiency, respectively, compared to PAA alone (62%), at equal concentration. PAA formed a protective polymeric film on steel upon molecular adsorption, but in the presence of the PAA/CeO 2, the metal surface protection was enhanced by the adhesion of compact hybrid films. PAA/CeO 2 microgel composite may have a future as an anticorrosive paint component for metal surface treatments.

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)0.001
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 score0.006
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.061
GPT teacher head0.327
Teacher spread0.266 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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