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Record W4393230084 · doi:10.1016/j.jnucmat.2024.155053

A facility for studying corrosion via in-situ Raman spectroscopy

2024· article· en· W4393230084 on OpenAlexafffund
V.S. Ramsundar, Kevin Daub, S.Y. Persaud, Mark R. Daymond

Bibliographic record

VenueJournal of Nuclear Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsRaman spectroscopyCorrosionIn situCharacterization (materials science)Nuclear engineeringMaterials scienceSpectroscopyAnalytical Chemistry (journal)Chemical engineeringEnvironmental scienceChemistryEnvironmental chemistryNanotechnologyMetallurgyPhysicsOptics

Abstract

fetched live from OpenAlex

Several in-core components in nuclear power systems are exposed to high-temperature water in the presence of radiation fields. The dynamic effect of radiation and water chemistry on material performance in these environments is not well understood partly due to significant experimental challenges. A facility consisting of a high temperature/pressure corrosion loop coupled with in-situ Raman spectroscopy has been commissioned to examine material behaviour in more realistic reactor conditions. The in-situ Raman component of the facility has been validated by conducting experiments with both pre-oxidized, and freshly abraded SS304L in water at 80°C and 300°C. Testing in water also revealed the detection limitations of the system. The assessment reported in this paper highlights the capabilities to perform degradation studies of key nuclear components by conducting in-situ characterization of materials exposed to high temperature water via Raman spectroscopy, thereby providing chemical, structural and semi-quantitative kinetic information.

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)0.002
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 score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.274
Teacher spread0.245 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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