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Record W2982020639 · doi:10.1021/acs.est.9b03060

Suppressing Ammonia Re-Emission with the Aid of the Co<sub>3</sub>O<sub>4</sub>-NPs@KIT-6 Catalyst in Ammonia-Based Desulfurization

2019· article· en· W2982020639 on OpenAlexaff
Tieyue Qi, Lidong Wang, Yuguo Wang, Lei Xing, Lin Zhang, Jie Liu, Huining Xiao, Shihan Zhang

Bibliographic record

VenueEnvironmental Science & Technology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of New Brunswick
FundersChina Scholarship CouncilNatural Science Foundation of Hebei ProvinceMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsFlue-gas desulfurizationAmmoniaCatalysisAmmonia productionChemistryWaste managementInorganic chemistryNuclear chemistryEnvironmental chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The re-emission of NH3 and SO2 caused by the decomposition of (NH4)2SO3 is a crucial concern in ammonia-based desulfurization. In this study, a novel Co3O4-NPs@KIT-6 catalyst with a three-dimensional two-helix structure is proposed for converting (NH4)2SO3 into (NH4)2SO4. The oxidation rate of (NH4)2SO3 with the catalyst was 7.5 times that without any catalyst, and this improvement was attributed to appropriately dispersed Co3O4 nanoparticles with a size of 4–10 nm that interacted with the KIT-6 support. Therefore, the number of active sites with substitution and hole defects was substantially increased, which is advantageous for high catalytic activities. Consequently, the amount of NH3 and SO2 re-emission during (NH4)2SO3 oxidation was reduced by 43.9%, which considerably reduced potential environmental risks. The results of this study serve to advance ammonia desulfurization by improving the desulfurization efficiency, downsizing the oxidation tank, and generating considerable profit from efficient reclaiming of (NH4)2SO4 as a fertilizer.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.006
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.007
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.004
GPT teacher head0.205
Teacher spread0.200 · 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.

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

Citations19
Published2019
Admission routes1
Has abstractyes

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