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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.002
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 score0.025
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
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