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Record W4387138759 · doi:10.1002/maco.202313757

The transition from used fuel container corrosion under oxic conditions to corrosion in an anoxic environment

2023· article· en· W4387138759 on OpenAlexafffund
Elham Salehi Alaei, Mengnan Guo, Jian Chen, Mehran Behazin, Erik Bergendal, Christina Lilja, David W. Shoesmith, James J. Noël

Bibliographic record

VenueMaterials and Corrosion · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Waste Management Organization
KeywordsSulfideCopperCorrosionInorganic chemistryOxideCovelliteCupriteCyclic voltammetryCopper sulfideMaterials scienceScanning electron microscopeHydroxideElectrochemistryChemistryMetallurgyChalcopyriteElectrode

Abstract

fetched live from OpenAlex

Abstract The conversion of copper oxide films on copper to copper sulfide has been investigated in sulfide‐containing chloride solutions. Single‐phase Cu 2 O films and duplex films consisting of Cu 2 O and CuO, and possibly Cu(OH) 2 , were prepared electrochemically on copper specimens at various applied potentials and characterized using Raman spectroscopy, scanning electron microscopy, and energy dispersive X‐ray analyses. The surface condition of the specimens subsequently exposed to a solution containing sulfide was monitored by measuring the corrosion potential ( E corr ) for various exposure periods, then cathodic stripping voltammetry was performed. Cuprite (Cu 2 O) was observed to be converted to Cu 2 S by chemical reaction with sulfide, while the conversion mechanism for the mixed deposit could comprise a galvanic process involving Cu II reduction coupled to the formation of Cu 2 S by the reaction of sulfide with copper within pores in the Cu 2 O/CuO surface film and a chemical conversion of Cu 2 O to Cu 2 S. Cupric hydroxide was not converted to Cu 2 S on the time scale (24 h) of these experiments.

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 categoriesInsufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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.

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

Citations18
Published2023
Admission routes2
Has abstractyes

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