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Record W2062899323 · doi:10.1139/v2012-079

Photochemical conversion of AgCl nanocubes to hybrid AgCl–Ag nanoparticles with high activity and long-term stability towards photocatalytic degradation of organic dyes

2012· article· en· W2062899323 on OpenAlexvenueno aff
Jizhuang Wang, Changhua An, Meiyu Zhang, Chuan Qin, Xijuan Ming, Qinhui Zhang

Bibliographic record

VenueCanadian Journal of Chemistry · 2012
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhotocatalysisChemistryMethyl orangePhotochemistryBleachDecompositionDegradation (telecommunications)CatalysisNanoparticleRadicalChemical engineeringNanotechnologyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The performance of a photocatalytic reaction is mainly determined by the quality of the photocatalyst. For real applications, significantly enhancing the stability and activity of the photocatalysts still remains a challenge for materials scientists and chemists. In this paper, we have achieved a highly efficient plasmonic AgCl–Ag nanophotocatalyst via photochemical conversion of AgCl nanocubes. Compared with reported photocatalysts, the as-achieved nanophotocatalyst exhibits superior activity, long-term stability, and wide applicability in the decomposition of organic dye pollutants. For example, only 30 s is needed to bleach methyl orange molecules assisted by AgCl–Ag nanoparticles. Furthermore, the catalyst can be reused up to 50 times without significant loss of activity. A possible mechanism was discussed and the specified photocatalytic reactions verified that both O 2 •– and OH • radicals were the main active species in decomposing pollutants. The excellent performance of the present photocatalyst suggests promising applications in environmental remediation, clean energy creation, and solar cells.

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 score0.031
Threshold uncertainty score0.730

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 studies0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations66
Published2012
Admission routes1
Has abstractyes

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