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Record W2803097793 · doi:10.1149/2.0601807jes

Photocatalytic Activity of ZnV<sub>2</sub>O<sub>6</sub>/Reduced Graphene Oxide Nanocomposite: From Theory to Experiment

2018· article· en· W2803097793 on OpenAlexaff
H. Sameie, Ali Asghar Sabbagh Alvani, Naimeh Naseri, Federico Rosei, Guido Mul, Bastian Mei

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGraphenePhotocatalysisRhodamine BOxideNanocompositeMaterials scienceCharge carrierSemiconductorChemical engineeringNanotechnologyHydrothermal circulationOptoelectronicsChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

A nanocomposite of ZnV 2 O 6 with hierarchical flower-like structure hybridized with reduced graphene oxide (rGO) was fabricated using a facile hydrothermal approach. The structure, morphology, optical and electronic properties were explored using comprehensive analytical techniques. The results revealed that the rGO sheets were decorated with the in situ-formed ZnV 2 O 6 nanoparticles yielding a well-combined composite structure. The photocatalytic activity of as-prepared ZnV 2 O 6 /rGO hybrids is 2.48 times larger than that of pristine ZnV 2 O 6 for the degradation of Rhodamine B (RhB). In parallel to the experimental results, the basic mechanisms of interfacial interaction, charge transfer/separation and subsequently their influence on the photocatalytic activity were theoretically studied by first-principles calculations. The photocatalytic enhancement is attributed to efficient interfacial electron transfer from ZnV 2 O 6 to rGO, leading to a prolonged lifetime of photoinduced charge carriers. We anticipate that these results will lead to new insights in the judicious design of graphene-based semiconductor photocatalysts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.252
Teacher spread0.244 · 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

Citations48
Published2018
Admission routes1
Has abstractyes

Explore more

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