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Record W3008852980 · doi:10.1139/cjp-2019-0547

Influence of cathode material type on the electrical breakdown behaviors of DC discharge

2020· article· en· W3008852980 on OpenAlexvenueno aff
F. Diab, W. H. Gaber, M. E. Abdel-kader, B.A. Soliman, M. A. Abd Al-Halim

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCathodeWork functionAtomic physicsIonizationTorrElectronGraphiteAnalytical Chemistry (journal)PhysicsArgonIonRange (aeronautics)Materials scienceChemistryElectrodeComposite materialThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

Paschen curves were studied using different cathode materials such as magnesium, zinc, and carbon graphite by discharge in argon gas of a pressure range between 0.08 and 3 Torr using a parallel plates configuration. The first and second Townsend coefficients (α and γ, respectively) and the ionization efficiency (η) of different cathode materials were deduced from Paschen curves as a function of the reduced field (E/P). The minimum breakdown voltage was found to be about 242 V for Mg material, which has the lowest work function, while carbon graphite has a higher breakdown voltage of 283 V due to its higher work function. The second coefficient γ was increased as a function of E/P and has higher values for materials of lower work functions, and a similar trend of γ is obtained as a function of the ion mean energy. On the other hand, the first coefficient α has a reverse behavior with both E/P and the work function of the cathode materials compared with the second coefficient. The ionization efficiency of the three cathode materials is identical, as η depends only on the gas properties and not the cathode material. η has a maximum value of about 0.025 V −1 for an E/P of about 185 Vcm −1 Torr −1 , corresponding to the maximum ionizing ability of electrons. The validation of the breakdown results has been confirmed by conferring with other published experimental measurements.

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 score0.386
Threshold uncertainty score0.169

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 head0.202
Teacher spread0.191 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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