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Record W2179330985 · doi:10.5006/1686

Mass Transfer of Diluted Volatile and Nonvolatile Components to a Nucleate Boiling Electrode

2015· article· en· W2179330985 on OpenAlexafffund
Chen Shen, Artin Afacan, Jing‐Li Luo, Ali Siddiqui, Stan J. Klimas

Bibliographic record

VenueCORROSION · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Alberta
FundersCanadian Nuclear Laboratories
KeywordsBoilingSubcoolingNucleate boilingMass transferNucleationMaterials scienceElectrodeAlloyMetalChemical engineeringThermodynamicsChemistryMetallurgyChromatographyHeat transferHeat transfer coefficientOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The mass transfer behavior of electro-active species from bulk solution to a metal surface (Alloy 800) under subcooled boiling and fully developed nucleate boiling conditions was investigated using a novel pool-boiling device. The electrochemical method was used to measure the mass transfer rate on a boiling surface. Potassium ferricyanide and hydrogen peroxide were used as the nonvolatile and volatile reaction species, respectively. It was found that for both volatile and nonvolatile species, the mass transfer rate was increased in the subcooled nucleate boiling region where the subcooled electrolyte temperature varied between 80°C and 95°C. This was a result of the enhanced micromixing effect. On the other hand, under the fully developed nucleate boiling region where the electrolyte temperature remained relatively constant at 96°C, the increase in mass transfer rate with increasing heat flux was attributed to the increased macromixing effects. Also, the mass transfer rate for hydrogen peroxide was found to increase at higher rate than that of ferricyanide ions because of the volatile nature of hydrogen peroxide, allowing it to be transferred through both liquid and vapor phases during the reaction.

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)0.000
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 score0.200
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.217
Teacher spread0.192 · 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 routes2
Has abstractyes

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Same venueCORROSIONSame topicHeat Transfer and Boiling StudiesFrench-language works237,207