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Record W2574642896 · doi:10.1002/cjce.22778

Oxidation‐separation kinetics of nitric oxide from flue gas using ferrate (VI) reagent in a spraying reactor

2017· article· en· W2574642896 on OpenAlexvenueno aff
Wei Yang, Yangxian Liu, Wen Xu, Qian Wang, Zhao Liang, Jianfeng Pan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsChemistryFlue gasReagentAbsorption (acoustics)Mass transferKineticsReaction rateInorganic chemistryOxideMass transfer coefficientChemical kineticsCatalysisChromatographyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Oxidation‐separation kinetics of nitric oxide (NO) from flue gas using ferrate (VI) reagent in a spraying reactor was studied. Effects of several key parameters on NO absorption rate were studied, and the results show that increasing ferrate (VI) concentration, solution pH, and NO concentration increases the NO absorption rate. Increasing reaction temperature and SO 2 concentration decrease NO absorption rate. The results of kinetic studies show that when < = 0.16 mol/L, the oxidation‐absorption process of NO can be seen as a pseudo‐2.28 order reaction (the reaction order is higher and the effect of reactant concentrations on reaction rate will become more obvious) and a pseudo‐1.0 order reaction when > 0.16 mol/L. The oxidation‐absorption process of NO is a fast reaction, which shows that the mass transfer process is the key rate control step of the whole NO absorption process. When > 0.16 mol/L, the mass transfer coefficient of gas phase and interfacial area play an important role for the oxidation‐absorption process of NO.

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)0.001
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 score0.022
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.032
GPT teacher head0.243
Teacher spread0.210 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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