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Record W2749112316 · doi:10.1080/1478422x.2016.1274088

The active/passive conditions for copper corrosion under nuclear waste repository environment

2017· article· en· W2749112316 on OpenAlexafffund
Z. Qin, Roshan Daljeet, Muyang Ai, Nasrin Farhangi, James J. Noël, S. Ramamurthy, David W. Shoesmith, Fraser King, Peter Keech

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
FundersNuclear Waste Management Organization
KeywordsBentoniteCorrosionRadioactive wasteCopperElectrolyteSpent nuclear fuelPassivityMaterials scienceSaturation (graph theory)Environmental scienceMetallurgyChemistryNuclear chemistryGeologyGeotechnical engineeringElectrode

Abstract

fetched live from OpenAlex

The focus of this research is to determine the conditions under which passivity of Cu is (with the possibility of pitting) or is not possible, in anticipated bentonite pore water compositions expected to contact a Cu-coated container in a deep geological repository. Cyclic voltammetry has been used to deduce the active/passive maps that define the environmental conditions (in terms of [Cl−], [SO42−], [HCO3−/CO32−], pH, and temperature) under which the Cu surface could be active or passive. A range of temperatures (up to 80°C), pH values (neutral to alkaline), and electrolyte compositions have been investigated. Such a database of maps shows that active/passive conditions change with the pH, temperature, and the type and concentration of anions. These maps will provide a basis for container corrosion models, as well as guidelines regarding the water compositions used when compacting the bentonite, the repository saturation time, and the optimum container spacing.This paper is part of a supplement on the 6th International Workshop on Long-Term Prediction of Corrosion Damage in Nuclear Waste Systems.

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 categoriesScience and technology studies, Scholarly communication
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 score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations34
Published2017
Admission routes2
Has abstractyes

Explore more

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