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Record W2315293599 · doi:10.1109/nano.2014.6968144

TiO<inf>2</inf> nanowires membranes for the use in photocatalytic filtration processes

2014· article· en· W2315293599 on OpenAlexafffund
Robert Liang, Mélisa Hatat-Fraile, Maricor J. Arlos, Mark R. Servos, Y. Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsUniversity of Waterloo
FundersCanadian Water NetworkNatural Sciences and Engineering Research Council of CanadaEvonik IndustriesCanada Research Chairs
KeywordsNanowireMembranePhotocatalysisPhotocurrentFiltration (mathematics)Dielectric spectroscopyMaterials scienceChemical engineeringElectrochemistryAdsorptionNuclear chemistryNanotechnologyChemistryOrganic chemistryOptoelectronicsPhysical chemistryElectrodeCatalysisBiochemistry

Abstract

fetched live from OpenAlex

TiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> nanowire membranes are multifunctional in that they provide liquid separation of contaminated water and treated water and the ability to oxidize or degrade organic pollutants. TiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> nanowires exhibit greater photocatalytic efficiency compared to bulk materials due to the high surface area and size effects in the quantum scale. In this work, TiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> nanowire membranes can be used as a filtration membrane and developed using an electrophoretic deposition process and characterized. The performance of the membrane is evaluated by its photoelectrochemical properties (photocurrent density vs. time, electron lifetime, and electrochemical impedance spectroscopy) and the adsorption/photocatalytic degradation rates of a dye pollutant (congo red).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.236
Teacher spread0.209 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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