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Record W2290045821 · doi:10.1002/cjce.22466

Oxygen consumption rate model in HCl oxidation over a supported CuO‐CeO<sub>2</sub> composite oxide catalyst under lean oxygen condition

2016· article· en· W2290045821 on OpenAlexvenueno aff
Yong Dai, Zhaoyang Fei, Xihua Xu, Chen Xian, Jihai Tang, Mifen Cui, Xu Qiao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCatalysisOxygenAdsorptionOxideDesorptionChemistryChemical engineeringDiffusionComposite numberInorganic chemistryCatalytic oxidationReaction rateMaterials scienceThermodynamicsPhysical chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lean‐oxygen oxidation of HCl, in which O 2 conversion can reach as high as ∼60 to ∼90 %, can greatly simplify the process of recycling Cl 2 using gas‐phase catalysis technology. The O 2 consumption rate in lean‐oxygen HCl oxidation over a supported CuO‐CeO 2 composite oxide catalyst was studied using an integral tube reactor. After eliminating the diffusion effects, three empirical kinetic models were proposed, which assumed O 2 adsorption, surface reaction, and Cl 2 desorption as the rate‐controlling steps, respectively. Based on O 2 adsorption as the rate‐controlling step, the results indicate that the kinetic Model I best describes the coincidence between the predicted results and the experimental data. The results laid an essential foundation for reactor simulation, scale‐up, and optimization of the industrial process.

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 score0.010
Threshold uncertainty score0.545

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 communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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