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Record W604445377 · doi:10.1155/2016/8730806

Synthesis and Characterization of Graphene Oxide-Modified Bi<sub>2</sub>WO<sub>6</sub>and Its Use as Photocatalyst

2016· article· en· W604445377 on OpenAlexafffund
Xiaoyue Hu, Xiangchao Meng, Zisheng Zhang

Bibliographic record

VenueInternational Journal of Photoenergy · 2016
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsPhotocatalysisGrapheneRhodamine BMaterials scienceOxideHydrothermal circulationCatalysisAdsorptionDegradation (telecommunications)Chemical engineeringCharge carrierCharacterization (materials science)Nuclear chemistryNanotechnologyChemistryOptoelectronicsOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

A Bi2WO6photocatalyst modified with graphene was synthesized in a two-step template-free hydrothermal process. The prepared samples were characterized to explore their properties. The photocatalytic activities of the prepared samples were investigated by degrading dye model, Rhodamine B (RhB), under visible light irradiation. This showed that the modified Bi2WO6photocatalyst with 1.2 wt% of graphene greatly improved photocatalytic activity during the degradation of dye pollutants, compared to pure Bi2WO6. The enhancement can be interpreted as the integrated effects of ultrahigh charge carriers’ mobility and high adsorption of RhB on graphene. Additionally, effects including catalysts dosage amount, pH of RhB solution, and temperature of reactor on the photocatalytically degrading RhB were also studied and discussed.

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 score0.001
Threshold uncertainty score0.002

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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