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Record W3040998233 · doi:10.1149/1945-7111/aba4e8

DFT Analysis of Ethanol Electro-Oxidation on Fe(110) and Fe3C(110) and its Correlation with the Stress Corrosion Cracking of Carbon Steel

2020· article· en· W3040998233 on OpenAlexaff
Ali Riahi, Amir Foroozan, Mahmoudreza Ghaznavi, Ali Seifitokaldani

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsDehydrogenationCarbon steelCorrosionCementiteEthanolMaterials scienceMetallurgyElectrochemistryFerrite (magnet)Stress corrosion crackingChemistryOxideCarbon fibersInorganic chemistryCatalysisElectrodeMicrostructureComposite materialPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Striking mechanical and morphological similarities of the stress corrosion cracking (SCC) of carbon steel in ethanolic media with those governed by a cleavage-like mechanism in CO-CO2 aqueous solutions, prompted the investigation of the possibility of ethanol electrochemical oxidation into CO on ferrite (Fe) and cementite (Fe3C) surfaces. Density functional theory computations on (110) surfaces reveal that the catalytic activity of Fe and Fe3C through the α dehydrogenation pathway can significantly reduce the energy barrier of electro-oxidation of ethanol and production of CO to 0.575 and 0.480 eV, respectively. These first principle calculations indicate that at the anodic potentials applied during potentiostatic slow strain rate testing, ethanol electrooxidation to CO is thermodynamically viable on carbon steel, giving further credit to the involvement of cleavage type SCC of carbon steel in ethanolic environments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of The Electrochemical SocietySame topicCorrosion Behavior and InhibitionFrench-language works237,207