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Record W2800392420 · doi:10.3390/asi1020012

Crude Glycerol as an Innovative Corrosion Inhibitor

2018· article· en· W2800392420 on OpenAlexafffund
Isam Al Zubaidi, Robert Jones, Mohammed Alzughaibi, Moayed Albayyadhi, Farzad Darzi, Hussameldin Ibrahim

Bibliographic record

VenueApplied System Innovation · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Regina
FundersUniversity of Regina
KeywordsCorrosionHydrochloric acidGlycerolCorrosion inhibitorScanning electron microscopeNuclear chemistryBiodieselChemistryMetallurgyMaterials scienceComposite materialBiochemistryCatalysis

Abstract

fetched live from OpenAlex

Crude glycerol, a byproduct of biodiesel production, was evaluated as a potential green inhibitor for steel corrosion in an acidic environment. The study was conducted using steel specimens placed in hydrochloric acid solutions (0.5 M) at a constant room temperature (25 °C) and crude glycerol concentrations in the range 0.1%–1.0% w/w. The criteria used to evaluate the extent of corrosion were the weight loss and corrosion rate. Additionally, fresh and spent samples were characterized using scanning electron microscopy and potentiodynamic polarization measurements. It was found that, generally, the corrosion inhibition increased with the inhibitor concentration. Results also showed that the maximum inhibition efficiency was achieved at 70 h residence time after which the inhibition efficiency at a given concentration either remained unchanged or dropped slight. Additionally, the overall maximum inhibition efficiency (98%) was observed at 70 h residence time and a 1% inhibitor concentration.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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

Citations22
Published2018
Admission routes2
Has abstractyes

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