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Record W4306638482 · doi:10.1088/1361-6463/ac9ac2

Kinetics driving nanocomposite thin-film deposition in low-pressure misty plasma processes

2022· article· en· W4306638482 on OpenAlexafffund
Simon Chouteau, Maria Mitronika, A. Goullet, Mireille Richard‐Plouet, Luc Stafford, A. Granier

Bibliographic record

VenueJournal of Physics D Applied Physics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversité de Montréal
FundersUniversité de NantesUniversité de MontréalCentre National de la Recherche Scientifique
KeywordsNanocompositeNanoparticleMaterials scienceThin filmChemical engineeringDeposition (geology)PlasmaKineticsEllipsometryArgonEvaporationNanotechnologyAnalytical Chemistry (journal)ChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Mist-assisted methods have recently attracted much attention for plasma deposition in high-quality (multi)functional thin films. However, very little is known on plasma interactions with misted colloidal solutions and their role in plasma process kinetics. Time-resolved optical diagnostics have been carried out to study the deposition of TiO 2 –SiO 2 nanocomposite thin films in low-pressure oxygen-argon plasmas with organosilicon precursors and TiO 2 suspensions. Each pulsed injection of the dispersion was followed by a pressure rise due to solvent evaporation. This caused a significant reduction in the electron temperature and density, which mitigated matrix precursor fragmentation and SiO 2 deposition as TiO 2 nanoparticles were supplied to the film. Comparing injections with and without nanoparticles, misty plasma effects were dominated by plasma droplets rather than plasma-nanoparticle interactions. Successive matrix-rich and nanoparticle-rich deposition steps were confirmed by in situ spectroscopic ellipsometry.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.806

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 science0.0010.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 teacher head, 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

Citations11
Published2022
Admission routes2
Has abstractyes

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