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Record W2623435081 · doi:10.1088/1361-6595/aa7806

Characterization by laser-induced photodetachment of anions formed during dust particle growth in a magnetically confined very low-pressure argon–acetylene plasma

2017· article· en· W2623435081 on OpenAlexaff
G. Al Makdessi, Ahmad Hamdan, J. Margot, Richard Clergereaux

Bibliographic record

VenuePlasma Sources Science and Technology · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsAcetylenePlasmaAtomic physicsChemistryArgonIonCharged particleParticle (ecology)PopulationTorrLaserAtmospheric pressurePhysics

Abstract

fetched live from OpenAlex

Abstract Anions in low-pressure magnetically confined Ar–C 2 H 2 plasmas were investigated using the laser-induced photodetachment technique. In particular, the influence of the magnetic field ( B ) was studied. We observed that the density of negatively-charged species n – increases with B and slightly decreases when the C 2 H 2 percentage grows. These phenomena are the result of changes in the plasma kinetics. We also observed that n – decreases as the pressure increases from 2 to 10 mTorr due to the corresponding rise of the collision frequency, which leads to enhanced losses of the negatively-charged species. In parallel, the photodetachment cross-section was deduced from the energy dependence of the photodetachment signal and was found to be close to that of C 2 H − anions, which indicates that these anions are likely to dominate the negative ion population.

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 score0.002
Threshold uncertainty score0.003

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 head0.213
Teacher spread0.206 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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