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Record W2910370547 · doi:10.1002/cjce.23447

Thermodynamic and kinetic performance of an S<sub>2</sub>O<sub>8</sub><sup>2‐</sup>/CaO<sub>2</sub> solution for NO removal

2019· article· en· W2910370547 on OpenAlexaffvenue
Zhiping Wang, Yanguo Zhang, Zhongchao Tan, Qinghai Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Waterloo
FundersDeutsche Forschungsgemeinschaft
KeywordsKinetic energyChemistryActivation energyNuclear chemistryMolar ratioAnalytical Chemistry (journal)Oxidative phosphorylationThermodynamicsPhysical chemistryChromatographyCatalysisPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The thermodynamic and kinetic performance of the oxidative absorbent S2O82‐/CaO2 for NO removal was evaluated. The results indicated that the optimal molar ratio of Na2S2O8/CaO2 was 0.1:0.2 mol/mol. The experimental results showed that the combination of Na2S2O8 and CaO2 had a positive synergistic effect on the NO removal. The results of the thermodynamic calculation verified that the use of the oxidative absorbent S2O82‐/CaO2 was effective for NO removal. The results of the kinetic study showed that the NO reaction was a 1.1‐order equation and the average activation energy of the reaction was 53.0 kJ/mol. The oxidative absorbent S2O82‐/CaO2 shows potential for the simultaneous wet removal of NO and SO2 with high efficiency, no secondary pollution, and low cost.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.170
Teacher spread0.164 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2019
Admission routes2
Has abstractyes

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