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Record W2192204515 · doi:10.5006/1690

In Situ X-Ray Diffraction Measurement Method for Investigating the Oxides Films on Austenitic Stainless Steel in Simulated Pressurized Water Reactor Primary Water

2015· article· en· W2192204515 on OpenAlexaff
Masashi Watanabe, Toshio Yonezawa, Takahisa Shobu, Tetsuo Shoji

Bibliographic record

VenueCORROSION · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsHatch (Canada)
FundersMitsubishi Heavy IndustriesJapan Society for the Promotion of ScienceElectric Power Research Institute
KeywordsMaterials scienceIn situPressurized water reactorAusteniteMetallurgyAustenitic stainless steelDiffractionCorrosionNuclear engineeringChemistryMicrostructureOpticsEngineering

Abstract

fetched live from OpenAlex

Synchrotron x-ray diffraction analytical techniques have been used to investigate the structure of oxide films formed on Type 316L (UNS S31603) austenitic stainless steel in simulated pressurized water reactor primary water. An in situ technique for investigating the layer structures of oxide films has been developed using this measurement method. The observed layer structures of the oxide films changed depending on the dissolved hydrogen concentration (DH) in PWR primary water. In two cases, where DH = 5 cm3/kg (H2O) or 30 cm3/kg (H2O), a (NixFe(1−x))Fe2O4-type spinel oxide was observed as the outer oxide, and a FeCr2O4-type spinel oxide was detected as the thin inner oxide. When DH = 30 cm3/kg (H2O), the Fe:Ni ratio in the (NixFe(1−x))Fe2O4 outer spinel oxide was much larger than when DH = 5 cm3/kg (H2O). In addition, sequential in situ measurements when the hydrogen concentration varied from 5 cm3/kg (H2O) to 30 cm3/kg (H2O) also demonstrated that the oxide layer structure seemed to adjust its characteristic composition as a function of the DH. The oxide layer structure could also be reversed to that of the initial state with DH = 5 cm3/kg (H2O) when the DH was switched back from 30 cm3/kg (H2O) to 5 cm3/kg (H2O).

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 score0.082
Threshold uncertainty score0.340

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)0.0000.000

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 head0.049
GPT teacher head0.291
Teacher spread0.242 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicNuclear Physics and ApplicationsFrench-language works237,207