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Record W2299282539 · doi:10.1155/2016/4054351

Ag/AgCl Loaded Bi<sub>2</sub>WO<sub>6</sub>Composite: A Plasmonic Z-Scheme Visible Light-Responsive Photocatalyst

2016· article· en· W2299282539 on OpenAlexafffund
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
KeywordsPhotocatalysisSurface plasmon resonanceMaterials scienceHydrothermal circulationComposite numberVisible spectrumHeterojunctionPlasmonBand gapChemical engineeringNanotechnologyOptoelectronicsPhotochemistryNanoparticleCatalysisComposite materialChemistry

Abstract

fetched live from OpenAlex

Hierarchical flower-like Bi2WO6was successfully synthesized by facile hydrothermal method at low pH. And Ag/AgCl was loaded by photoreduction on its surface. As-prepared photocatalysts were characterized by various techniques. Bi2WO6was successfully synthesized at a size of 2-3 μm. Depositing Ag/AgCl did not destroy the crystal structure, and both Ag+and metallic Ag0were found. The band gap of the composite was 2.57 eV, which indicates that visible light could be the activating irradiation. In the photocatalytic activity test, the composite with 10 wt% Ag/AgCl boasted the highest removal efficiency (almost 100%) in 45 min. The significant enhancement can be attributed to the surface plasmon resonance (SPR) effect and the establishment of heterostructures between Ag/AgCl and Bi2WO6. A possible mechanism of photocatalytic oxidation in the presence of Ag/AgCl-Bi2WO6was proposed. This work sheds light on the potential applications of plasmonic metals in photocatalysis to enhance their activities.

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 score0.004

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)0.0010.001

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 head0.009
GPT teacher head0.260
Teacher spread0.250 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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