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Record W2916179054 · doi:10.1088/2516-1067/ab021c

Atmospheric-pressure plasma by remote dielectric barrier discharges for surface cleaning of large area glass substrates

2019· article· en· W2916179054 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 Research Express · 2019
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPlasma cleaningContact angleMaterials scienceDielectric barrier dischargePlasmaDielectricAtmospheric pressureElectrodeAnalytical Chemistry (journal)Atmospheric-pressure plasmaComposite materialOptoelectronicsChemistryEnvironmental chemistryMeteorology

Abstract

fetched live from OpenAlex

Abstract A remote dielectric barrier discharge (RDBD) plasma system has been used to clean glass-surfaces under atmospheric pressure. To achieve a sustainable process, it is critical to optimize the parameters that affect the surface condition. Hence, the physical and chemical properties of the glass surface were investigated by changing the input voltage and the distance between the sample and electrodes. The optical emission spectroscopy (OES) characteristics were analyzed as a function of the gas concentration, and contact angles were measured before and after plasma treatments. The contact angle was reduced from 50 degrees (hydrophobic) to 5 degrees (hydrophilic) after the RDBD plasma treatment on the bare glass. The temperature of the samples was under 55 °C during the process without electrostatic charge. In this paper, we demonstrated the superior cleaning effectiveness of removing inorganic and organic contaminants on the glass surface using the RDBD without surface damage.

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)0.001
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 score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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