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Record W4289523627 · doi:10.1149/1945-7111/ac862c

The Effect of Alloy Composition on The Dealloying of Ni- and Fe-Based Engineering Alloys in Boiling Caustic Solutions

2022· article· en· W4289523627 on OpenAlex
Hooman Gholamzadeh, Bander Alsekhan, Adil Shaik, Kevin Daub, S.Y. Persaud

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanoporousAlloyCaustic (mathematics)BoilingMetallurgyNanoscopic scaleElectrochemistryComposition (language)Chemical engineeringElectrodeNanotechnologyChemistry

Abstract

fetched live from OpenAlex

The effect of alloy composition on the dealloying susceptibility of Ni- and Fe-based alloys is studied in near-boiling caustic solutions. A comprehensive comparison of the alloys is performed using electrochemical measurements and nanoscale characterization to evaluate dealloying behaviour. Results indicate a general increase in dealloying resistance when Ni content is increased. In alloys with similar Ni content, higher Cr content delays dealloying, while an increase in Mo content promotes dealloying. Nanoscale characterization confirms a nanoporous surface film with a core–shell ligament structure. The shell is nearly pure Ni, while ligament cores have a composition approaching that of the parent material.

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.

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)0.002
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 score0.002
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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