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Record W4414779346 · doi:10.1038/s41529-025-00667-7

A mechanistic study of iron passivation and transpassive behavior in sulfate solutions using thermo-kinetic diagrams

2025· article· en· W4414779346 on OpenAlexafffund
Mohammad Amin Razmjoo Khollari, Kashif Mairaj Deen, Edouard Asselin

Bibliographic record

Venuenpj Materials Degradation · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPassivationDissolutionCorrosionSulfatePolarization (electrochemistry)Pourbaix diagramAqueous solution

Abstract

fetched live from OpenAlex

Understanding the dissolution and passivation of iron in aqueous environments is essential for enhancing its corrosion resistance and expanding its applications. We present Thermo-Kinetic (TK) diagrams for iron in deaerated solutions with no added sodium sulfate (Na 2 SO 4 ) and with 0.1 M Na 2 SO 4 over the pH range 1–14, constructed by integrating current density contours from potentiodynamic polarization with thermodynamic E-pH diagrams. TK diagrams indicate that in solutions with no added Na 2 SO 4 , iron passivates above pH 7, with a minimum passive current density (i p ) of 5 ×10 −6 mA·cm −2 at pH 8. The addition of 0.1 M Na 2 SO 4 delayed passivation until pH 12 and increased i p nearly tenfold. Galvanostatic (GS) polarization and EIS validated the TK diagram results. XPS after GS polarization revealed an FeOOH/Fe 2 O 3 film at pH 10, while Fe 3 O 4 /Fe 2 O 3 dominated at pH 12 and 14. These results clarify how sulfate compromises iron passivity and highlight TK diagrams as a powerful tool for mapping corrosion behavior.

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)0.000
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 score0.040
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.035
GPT teacher head0.293
Teacher spread0.258 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuenpj Materials DegradationSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207