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Record W1968758094 · doi:10.1002/ejic.201200731

Photocatalytic TiO<sub>2</sub> Macroscopic Fibers Obtained Through Integrative Chemistry

2012· article· en· W1968758094 on OpenAlexafffund
Natacha Kinadjian, Mickael Le Béchec, Thierry Pigot, Fabien Dufour, Olivier Durupthy, Ahmed Bentaleb, Éric Prouzet, Sylvie Lacombe, Rénal Backov

Bibliographic record

VenueEuropean Journal of Inorganic Chemistry · 2012
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueEuropean Commission
KeywordsPhotocatalysisChemistryTitanium dioxideCatalysisMineralization (soil science)Chemical engineeringParticle sizeNanoscopic scaleHeterogeneous catalysisNanotechnologyMaterials sciencePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The photocatalytic properties of titanium dioxide depend not only on its electronic properties, but also on the material size and shape, which can increase interactions between the reactants and catalyst. Most studies to date show that reducing the particle size down to the nanoscale increases photocatalytic efficiency, as a result of a higher surface to volume ratio and because a larger proportion of the material is actually irradiated by light. We demonstrate that a multiscale shape design, which integrates surface roughness, particle shape, and 1D material processing and orientation, can favor photocatalytic properties in the solid–gas regime, especially mineralization (conversion into CO 2 ), when the hierarchical 1D orientation of the material is combined with unidirectional gas flow. Several materials with hierarchical structure were prepared and characterized. They have been tested for the photocatalytic mineralization of gaseous acetone and compared with commercial catalysts. Our study reveals that a suitable combination of multiscale design and optimization of the material orientation and gas flow favors high mineralization.

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 score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

Explore more

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