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Record W2060422538 · doi:10.5539/apr.v6n5p45

Investigation of Hot Cathode and Hollow Anode of Argon Glow Discharge Plasma

2014· article· en· W2060422538 on OpenAlexvenueno aff
Shamoo K. Al-Hakary, Luqman Dosky, Shamil K. Talal

Bibliographic record

VenueApplied Physics Research · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAnodeCathodeMaterials scienceGlow dischargePlasmaProtein filamentAtomic physicsArgonCurrent (fluid)Direct currentElectron temperatureVoltageAnalytical Chemistry (journal)ElectrodeChemistryElectrical engineeringComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Hot cathode and hollow anode argon DC glow discharge plasma at different pressures of (0.04, 0.05, 0.06, 0.07, 0.09, 0.2, 0.4, and 0.8 mbar) has been investigated. The experiments were carried out under the influence of pressure and filament cathode current on voltage – current characteristics of glow discharge and its breakdown voltage. Plasma parameters have been measured and obtained using single probe method at fixed discharge current (Id=1.88 mA) and hollow anode diameter. A computer MATLAB program is performed for this purpose. It was shown that the discharge voltage – current characteristics curve has a positive resistance and represents an abnormal glow region at pressure (0.04 and 0.06) mbar for different filament current. The breakdown voltage increases as the filament current is increased. In different pressure, electron temperature shows different behavior with increasing filament current. Electron density varies nearly inversely with the filament current, but it is increase due to increase of pressure from (1 to 3 mbar), then tends to decrease for the higher pressure. There are two groups of electrons according to the two peaks of (EEDFs), and the peaks amplitude decrease, with the increases of both filament current and gas pressure.

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 score0.162
Threshold uncertainty score0.353

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 head0.047
GPT teacher head0.282
Teacher spread0.235 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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