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Record W3099500656 · doi:10.1002/ppap.202000173

Atmospheric plasma dielectric barrier discharge: A simple route to produce superhydrophilic TiO<sub>2</sub>@carbon nanostructure

2020· article· en· W3099500656 on OpenAlexaff
Zineb Matouk, Badr Torriss, Rocío Rincón, Amir Mirzaei, J. Margot, Mohamed Chaker

Bibliographic record

VenuePlasma Processes and Polymers · 2020
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDielectric barrier dischargeSuperhydrophilicityContact angleWettingMaterials scienceX-ray photoelectron spectroscopyNanocompositeChemical engineeringDielectricAtmospheric pressureNanostructureAnalytical Chemistry (journal)Surface roughnessCarbon fibersNanotechnologyComposite materialOptoelectronicsChemistryComposite numberChromatography

Abstract

fetched live from OpenAlex

Abstract A one‐step technique for the deposition of superhydrophilic TiO2@carbon nanocomposites is described in this study. The nanocomposites are synthesized by injecting TiO2 nanoparticles suspended in isopropanol into a dielectric barrier discharge operating at atmospheric pressure (AP‐DBD) generated in an N2/N2O gas mixture. The influence of the voltage (3–8 kV) applied to a 2‐kHz‐operated AP‐DBD on the wettability of the as‐deposited TiO2@C nanocomposites is examined. The water contact angle is drastically reduced from 93° for the reference TiO2 powder to <5° for the deposited nanocomposite. This superhydrophilicity is not caused by the increase of the surface roughness determined by atomic force microscopy measurement but rather by the higher density of graphitic compounds at the surface, as confirmed by X‐ray photoelectron spectroscopy measurements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes1
Has abstractyes

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