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Record W1540239372 · doi:10.2172/821960

The Effect of pH on Nickel Alloy SCC and Corrosion Performance

2002· report· en· W1540239372 on OpenAlexaff
David Morton, Mike Hansen

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCorrosionAlloyStress corrosion crackingGrowth rateCoolantMetallurgyPressurized water reactorNickelMaterials scienceCorrosion fatigueChemistryHydrogenNuclear chemistryThermodynamics

Abstract

fetched live from OpenAlex

Alloy X-750 condition HTH stress corrosion crack growth rate (SCCGR) tests have been conducted at 360 C (680 F) with 50 cc/kg hydrogen as a function of coolant pH. Results indicate no appreciable influence of pH on crack growth in the pH (at 360 C) range of {approx} 6.2 to 8.7, consistent with previous alloy 600 findings. These intermediate pH results suggest that pH is not a key variable which must be accounted for when modeling pressurized water reactor (PWR) primary water SCC. In this study, however, a nearly three fold reduction in X-750 crack growth rate was observed in reduced pH environments (pH 3.8 through HCl addition and pH 4-5.3 through H{sub 2}SO{sub 4} addition). Crack growth rates did not directly correlate with corrosion film thickness. In fact, 10x thicker corrosion films were observed in the reduced pH environments.

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 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 score0.030
Threshold uncertainty score0.930

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.275
Teacher spread0.252 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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