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Record W4377099141 · doi:10.1002/cjce.24959

Effect of the oxidation of activated carbon by hydrogen peroxide on its catalytic activity in the regeneration of Co(<scp>II</scp>)<scp>TETA</scp>

2023· article· en· W4377099141 on OpenAlexvenueno aff
Zi‐qiu Li, Peiyun Chen, Xiang‐li Long

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsTriethylenetetramineHydrogen peroxideActivated carbonChemistryCatalysisCobaltInorganic chemistryCarbon fibersOxygenCobalt oxideHydrogenOxideNuclear chemistryAdsorptionMaterials scienceOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract Cobalt(II) triethylenetetramine (Co(II)TETA) formed by soluble cobalt(II) salt combining with triethylenetetramine will be used as a wet denitration technique since it can interact with nitric oxide to accomplish quick absorption of NO from gas phase. However, the oxygen in the flue gas will oxidize Co(II)TETA to Co(III) TETA, resulting in the reduction of denitrification efficiency. Activated carbon has been used to promote the regeneration of Co(II)TETA due to its unique surface characteristics. Hydrogen peroxide solution is utilized as a modifier in the carbon modification to improve the catalytic performance of activated carbon. The experiments demonstrate that the best regeneration efficiency of Co(II)TETA is gained by the modified carbon impregnated in 0.05 mol L −1 H 2 O 2 solution at 70°C for 12 h with a solid/liquid ratio of 1/50 (g/mL) followed being activated at 400°C for 2 h in N 2 . After being treated with hydrogen oxide solution, the surface area and acidity of the carbon is increased. Continuous experiments reveal that the NO removal efficiency gained by modified activated carbon is about 8.36% higher than that gained by the original carbon.

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)0.004
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 score0.011
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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