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Field emission calculations revisited with Murphy and Good theory: a new interpretation of the Fowler–Nordheim plot

2008· article· en· W2011837558 on OpenAlexafffund
M. DIONNE, Sylvain Coulombe, J-L Meunier

Bibliographic record

VenueJournal of Physics D Applied Physics · 2008
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsPlot (graphics)Interpretation (philosophy)Field electron emissionField (mathematics)Theoretical physicsField theory (psychology)Statistical physicsPhysicsMathematical physicsMathematicsPhilosophyQuantum mechanicsStatisticsPure mathematicsLinguistics

Abstract

fetched live from OpenAlex

The Murphy and Good (M–G) theory for thermo-field emission [1] was used to calculate the emission current densities over the 300–5000 K temperature range in order to bridge the gap with the simpler zero temperature version of the Fowler–Nordheim (F–N) equation. A comparison between the two sets of data reveals that the F–N equation as it is used in the analysis of F–N plots underestimates the true current density by a factor of 102 for all fields known to produce significant current in the case of carbon nanotubes arrays. We propose a new equation for field emission that is consistent with experimental current densities and that can still provide the field enhancement factor β of field emitter arrays. This equation is obtained over the 300–3000 K temperature range by fitting the temperature and field dependences of JM–G using a f(Ts, Es, ϕ0) function using work function values ϕ0 of 4.5 and 2.6 eV. Our complete parametric equation for the fitted M–G current density is found to apply within a 1% error margin for f(Ts, Es, ϕ0) functions computed for each value of ϕ0 and for Es > 1.2 × 109 V m−1.

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 score0.030
Threshold uncertainty score0.345

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations16
Published2008
Admission routes2
Has abstractyes

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