MétaCan
Menu
Back to cohort
Record W2726594236 · doi:10.1002/sia.6257

X‐ray photoelectron study of oxides formed on Ni metal and Ni‐Cr alloy surfaces under electrochemical control at 25 °C and 150 °C

2017· article· en· W2726594236 on OpenAlexafffund
Brad P. Payne, Peter Keech, N. S. McIntyre

Bibliographic record

VenueSurface and Interface Analysis · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationCanadian Nuclear LaboratoriesWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNichromeHydroxideOxidizing agentOxideNon-blocking I/OAlloyX-ray photoelectron spectroscopyNickelMetalElectrochemistryMetal hydroxideNickel oxideInorganic chemistryMaterials scienceLayer (electronics)ChemistryMetallurgyElectrodeChemical engineeringCatalysisNanotechnologyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The oxide chemical composition on metallic Ni and NiCr alloy electrodes has been studied for changes in simulated reactor coolant solution chemistry, through a range of oxidizing potentials and pH settings at 25 °C and 150 °C. Even under strongly reducing conditions, the Ni surface has a 2‐nm layer of oxide and hydroxide from which further oxides form, depending on the subsequent oxidative step. For basic (normal operating) pH conditions and under oxidation potentials near 0 volts (vs. SCE), a predominantly Ni(OH) 2 layer is formed that appears to remain relatively stable up to at least 48 hours of oxidation at 150 °C. For the NiCr alloy, similar stability is imparted by a thin film of Cr(OH) 3 / Cr 2 O 3 and Ni(OH) 2 ./NiO. Under milder oxidizing (but still basic) conditions, the surface is stabilized by a thin film that is mostly Ni(OH) 2 /NiO. Under neutral solution conditions, the same oxide/ hydroxide films do not seem to be as effective in stabilizing the surface. Copyright © 2017 John Wiley & Sons, Ltd.

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 score0.010
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.285
Teacher spread0.269 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueSurface and Interface AnalysisSame topicCorrosion Behavior and InhibitionFrench-language works237,207