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Record W4297175899 · doi:10.1007/s11270-022-05867-4

Aqueous Acetamiprid Degradation Using Combined Ultrasonication and Photocatalysis Under Visible Light

2022· article· en· W4297175899 on OpenAlexafffund
Carolina Sayury Miyashiro, Safia Hamoudi

Bibliographic record

VenueWater Air & Soil Pollution · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversité LavalCentre in Green Chemistry and CatalysisAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotocatalysisAcetamipridSonicationPhotodegradationDegradation (telecommunications)Aqueous solutionPesticide degradationNuclear chemistryChemistrySonochemistryVisible spectrumGrapheneOxideMaterials sciencePhotochemistryPesticideCatalysisNanotechnologyOrganic chemistryChromatographyImidacloprid

Abstract

fetched live from OpenAlex

Acetamiprid (ACE), a neonicotinoid pesticide widely used in pest control, was found in high concentrations in soils, rivers, and lakes. In the present study, ACE degradation was investigated using visible light driven photocatalysis over nitrogen-graphene oxide (N-GO) and palladium-graphene oxide (Pd-GO)-doped ZnO photocatalysts combined with ultrasonication implemented either as a pretreatment (sonolysis) or operated simultaneously with photocatalysis (sonophocatalysis). The effectiveness of the two ACE degradation processes was determined separately. The sonolysis pretreatment allowed reaching almost 40% acetamiprid conversion within 30 min of reaction. Pursuing with the photodegradation reaction in the presence of N-GO-ZnO and Pd-GO-ZnO resulted in a maximum conversion of 98% of ACE within 5 h. As for the sonophotocatalysis process, the reaction time was shortened from 5 to 2 h with 100% acetamiprid conversion. In addition, the photocatalysts were shown to keep their activity even after 5 sonophotocatalytic cycles, thus proving their reusability. Supplementary Information: The online version contains supplementary material available at 10.1007/s11270-022-05867-4.

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)0.000
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 score0.048
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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
Published2022
Admission routes2
Has abstractyes

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