MétaCan
Menu
Back to cohort
Record W2402941619 · doi:10.1002/ppap.201500223

Nebulization of Nanocolloidal Suspensions for the Growth of Nanocomposite Coatings in Dielectric Barrier Discharges

2016· article· en· W2402941619 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 · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceNanocompositeSuspension (topology)NanoparticleDielectricEconomies of agglomerationDielectric barrier dischargeChemical engineeringElectrodeTransmission electron microscopyColloidNanotechnologyScanning electron microscopeElectric fieldComposite materialOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

The nebulization of colloidal suspensions is analyzed by dynamic light scattering, scanning, and transmission electron microscopies. While primary agglomeration can be important for many nanoparticle‐solvent couples, our results indicate that for TiO 2 nanoparticles dispersed in water, secondary agglomeration also occurs during nebulization. When nebulization is realized immediately after sustaining a plane‐to‐plane dielectric barrier discharge at atmospheric pressure, the collection efficiency of TiO 2 nanoparticles increases due to the presence of a remanent electric field between the two electrodes. Finally, these findings are used to deposit SiO 2 –TiO 2 nanocomposite thin films by alternating the deposition of dense silica‐like layers in a Townsend discharge and the collection of TiO 2 nanoparticles through nebulization of the nanocolloidal suspension.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.192
Teacher spread0.187 · 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