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Record W2944448449 · doi:10.1080/1478422x.2019.1613780

Gum Arabic as corrosion inhibitor in the oil industry: experimental and theoretical studies

2019· article· en· W2944448449 on OpenAlexafffund
Chen Shen, Víctor H. Álvarez, Josh D. B. Koenig, Jing‐Li Luo

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsTafel equationCorrosionCorrosion inhibitorAdsorptionChemisorptionGum arabicMaterials scienceBrineCarbon steelDielectric spectroscopyElectrochemistryPolymerChemical engineeringArabicMetallurgyChemistryComposite materialOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Corrosion inhibitors are commonly used in the oil industry due to their effectiveness, easy application and relatively low cost. Electrochemical and molecular simulation methods were used to investigate the application of Gum Arabic (GA) as a natural polymer corrosion inhibitor for carbon steel. The Tafel analysis results showed that GA works as a mixed-type corrosion inhibitor on carbon steel with the increased Open Circuit Potential. In synthetic brine, the adsorption isotherm study showed that GA inhibitor films were mainly formed via chemisorption. The corrosion efficiency of GA measured by polarisation curve, polarisation resistance and impedance measurements, were 94.0, 83.5 and 90%, respectively. Molecular simulations studies indicated that high molecular weight of carbohydrates have strong interaction with Fe (111) surface.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.264
Teacher spread0.256 · 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

Citations59
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion ControlSame topicCorrosion Behavior and InhibitionFrench-language works237,207