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Record W4386502419 · doi:10.1080/1478422x.2023.2247666

Depth profiles of akaganéite, goethite and maghemite on A1010, HPS and weathering steel panels after wet-dry corrosion tests

2023· article· en· W4386502419 on OpenAlexaff
J. Sawicki

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2023
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaghemiteGoethiteWeatheringWeathering steelCorrosionMetallurgyMaterials scienceGeologyGeochemistryChemistryHematite

Abstract

fetched live from OpenAlex

The panels of high chromium steel (A1010), high-performance steel (HPS 70W) and weathering steel (WS) were exposed to accelerated salt-spray corrosion test for 1 week and to less aggressive test for additional 13 weeks. After the tests, the corrosion products formed were extracted layer after layer and examined as a function of their depth with Mössbauer transmission spectroscopy. In the case of A1010 steel, the amounts of goethite (α-FeOOH) and akaganéite (β-FeOOH) were almost equal along the depth of the corroded layer up to ∼20 μm. The rust layers on HPS and WS panels were much thicker, respectively up to ∼0.6 mm and ∼1 mm, poorly adherent and contained mostly akaganéite near the metal–rust interface, α-FeOOH increasingly further away, and – after long exposure – also up to 20% Fe as maghemite (γ-Fe 2 O 3 ). Formation of β-FeOOH near the interface was promoted by chloride concentrated by spray-dry cycles. The microstructure and depth profiles of identified species, and especially the role of β-FeOOH, are discussed.

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)0.001
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 score0.059
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.234
Teacher spread0.227 · 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
Published2023
Admission routes1
Has abstractyes

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