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Record W2588980538 · doi:10.1021/acsami.7b00158

Efficient Upconverting Multiferroic Core@Shell Photocatalysts: Visible-to-Near-Infrared Photon Harvesting

2017· article· en· W2588980538 on OpenAlexafffund
Jianming Zhang, Yue Huang, Lei Jin, Federico Rosei, Fiorenzo Vetrone, Jérôme P. Claverie

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversité de SherbrookeInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsMaterials sciencePhotocatalysisPhotochemistryVisible spectrumPhotodegradationPhoton upconversionNanoshellNanoparticleIrradiationBismuth ferriteFluorescenceNanotechnologyLuminescenceOptoelectronicsMultiferroicsOpticsCatalysis

Abstract

fetched live from OpenAlex

upconverting nanoparticles (UCNPs). A bismuth ferrite (BFO) shell is assembled around the UCNPs via a hydrothermal process. The photocatalytic degradation assays of methylene orange and 4-chlorophenol reveal that these core@shell nanostructures possess remarkably enhanced reaction activity under visible and NIR irradiation, compared to the BFO powder alone and the BFO-UCNP mixture. Photo-charge scavenger tests and fluorescent assays indicate that hydroxyl radicals play a pivotal role in the photodegradation mechanism. The enhanced photoactivity of the core@shell structure is attributed to the NIR radiation which is converted into visible light by UCNPs, and which is then captured by BFO via a nonradiative luminescence resonance energy transfer process. Therefore, this core@shell architecture optimizes solar energy use by efficiently harvesting visible and NIR photons.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.259
Teacher spread0.236 · 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

Citations93
Published2017
Admission routes2
Has abstractyes

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