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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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 TiO 2 @carbon nanocomposites is described in this study. The nanocomposites are synthesized by injecting TiO 2 nanoparticles suspended in isopropanol into a dielectric barrier discharge operating at atmospheric pressure (AP‐DBD) generated in an N 2 /N 2 O 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 TiO 2 @C nanocomposites is examined. The water contact angle is drastically reduced from 93° for the reference TiO 2 powder to &lt;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.

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)0.001
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
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