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Record W3092439083 · doi:10.1149/1945-7111/abbea7

Investigating the Influence of Cr and Mo Additions to Commercial Ni-Based Alloys Exposed to Neutral and Acidic Chloride Solutions

2020· article· en· W3092439083 on OpenAlexafffund
Jeffrey D. Henderson, Baian Almusned, Mojtaba Momeni, Samantha Anderson, Vahid Dehnavi, Dmitrij Zagidulin, David W. Shoesmith, James J. Noël

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionChlorideElectrochemistryMetallurgyChemistryInorganic chemistryMaterials scienceElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

The corrosion behaviour of four commercially available Ni-based alloys is presented for both natural and potential-controlled corrosion in chloride solutions. Electrochemical evidence suggests a balance of Cr and Mo is essential to maintaining passive film stability in chloride solutions, especially those in which acidic conditions may develop. In near-neutral solutions, increased Cr content results in lower corrosion rates and improved passive properties; however, an increase in Cr content above 15 wt.% provided only minor additional benefits. In acidic solutions, Mo content is essential to corrosion resistance and imparts two major benefits: increased film stability and rapid repassivation of breakdown events. Since localized corrosion and the critical chemistry which accompanies these processes are of concern in many chloride-containing applications, a delicate balance of Cr and Mo must be considered; although optimal concentrations have yet to be determined.

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)0.000
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 score0.015
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.026
GPT teacher head0.250
Teacher spread0.224 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

Explore more

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