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Record W2994777978 · doi:10.1063/1.5125143

A combination of plasma diagnostics and Raman spectroscopy to examine plasma-graphene interactions in low-pressure argon radiofrequency plasmas

2019· article· en· W2994777978 on OpenAlexafffund
Pierre Vinchon, X. Glad, Germain Robert‐Bigras, Richard Martel, A. Sarkissian, Luc Stafford

Bibliographic record

VenueJournal of Applied Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsPlasmionique (Canada)Université de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsArgonLangmuir probeAtomic physicsPlasmaIonRaman spectroscopyMaterials sciencePlasma diagnosticsPlasma parametersChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Graphene films were exposed to low-pressure capacitively coupled (E-mode) and inductively coupled (H-mode) argon radio frequency plasmas to investigate damage formation by very-low-energy ion irradiation. In the H-mode, plasma parameters were assessed by a Langmuir probe and plasma sampling mass spectrometry to determine the conditions of fixed ion fluence but with different average ion energies. The populations of argon metastable and resonant argon atoms were also measured by optical absorption spectroscopy to determine their contribution to the total energy flux during plasma treatment. In the H-mode, in which plasma-graphene interactions are dominated by ion irradiation effects, Raman spectroscopy reveals a significant rise in the D/G ratio and full width at half maximum of the G peak as well as the onset of graphene amorphization, even at very low ion energies (between 7 and 13 eV). In the E-mode characterized by comparable ion energy but much lower ion density, significant damage is also observed, a feature ascribed to the additional energy flux linked to the de-excitation of metastable argon species on the graphene surface.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.536

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 head0.011
GPT teacher head0.267
Teacher spread0.255 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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