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Record W4312109008 · doi:10.1002/ppap.202200165

Soft polymerization of hexamethyldisiloxane by coupling pulsed direct‐liquid injections with dielectric barrier discharge

2022· article· en· W4312109008 on OpenAlexafffund
Laura Cacot, Guillaume Carnide, Myrtil L. Kahn, Nicolas Naudé, Luc Stafford, Richard Clergereaux

Bibliographic record

VenuePlasma Processes and Polymers · 2022
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de Montréal
FundersUniversité de MontréalCentre National de la Recherche Scientifique
KeywordsHexamethyldisiloxanePlasma polymerizationOrganosiliconMaterials sciencePolymerizationDielectric barrier dischargeDielectricThin filmDeposition (geology)Atmospheric pressurePlasmaEvaporationPolymerAnalytical Chemistry (journal)Chemical engineeringChemistryPolymer chemistryComposite materialNanotechnologyOrganic chemistryOptoelectronicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract This work examines the combination of pulsed direct‐liquid injections with dielectric barrier discharge at atmospheric pressure for the deposition of organosilicon coatings using hexamethyldisiloxane (HMDSO) as the precursor and nitrogen as the carrier gas. In such conditions, deposition relies on the charging of micrometer droplets and their transport toward the substrate by the Coulomb force. The thin‐film morphology and extent of precursor fragmentation are strongly linked to the amount of energy provided by the filamentary discharge to HMDSO droplets. While cross‐linked and smooth coatings were achieved at low energies as in standard gas phase plasma polymers, viscous and droplet‐like structured thin films were deposited at higher energies. The latter material is attributed to the soft polymerization of HMDSO droplets related to plasma–droplet interactions.

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 score0.005

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 head0.007
GPT teacher head0.225
Teacher spread0.219 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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