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Record W4990686 · doi:10.5006/c2009-09425

Mechanistic Studies of the Crevice Corrosion of Alloy 22 in Chloride-Nitrate Solutions

2009· article· en· W4990686 on OpenAlexaff
Fraser King, Licai Mao, Jing‐Li Luo, Michael J. Apted, John H. Kessler, Andrew Sowder

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCrevice corrosionCorrosionChlorideAlloyNitrateMetallurgyMaterials scienceInorganic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Alloy 22 is susceptibility to crevice corrosion in concentrated chloride solutions at elevated temperature. A number of oxyanions, most notably nitrate, sulfate, and carbonate, inhibit the aggressiveness of the chloride ion. The ratio of aggressive to inhibitive anions is a key parameter in predicting the possibility of the crevice corrosion of waste packages in the Yucca Mountain repository. The results of a preliminary experimental program to study the effects of chloride and nitrate ions on the propagation and stifling of the crevice corrosion of Alloy 22 are described. A coupled-electrode technique was used to study the stifling of crevice corrosion in various solutions following artificial initiation achieved through galvanostatic polarization. The environments studied included CaCl2-NaNO3 mixtures with varying [NO3−]:[Cl−] ratio and total salinity and a solution of 5 mol·dm-3 NaCl with and without added NO3−. The test temperature in all cases was 120° C. Even with electrochemical polarization, it was found difficult to initiate crevice corrosion in CaCl2 solutions containing nitrate. Some superficial damage was observed in concentrated solutions. In contrast, initiation occurred readily in NaCl solutions, although the coupled current decreased quickly in nitrate-containing solutions, indicating stifling of the crevice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.308
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2009
Admission routes1
Has abstractyes

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