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Record W2082036377 · doi:10.1088/0022-3727/39/15/023

Nonlinear Fowler–Nordheim plots of the field electron emission from graphitic nanocones: influence of non-uniform field enhancement factors

2006· article· en· W2082036377 on OpenAlexafffund
X. Lu, Q. Yang, C. Xiao, Akira Hirose

Bibliographic record

VenueJournal of Physics D Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsField electron emissionNonlinear systemField (mathematics)Materials scienceElectronCondensed matter physicsNanotechnologyPhysicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Well-aligned graphitic nanocones were synthesized on polished pristine p-type (100) silicon wafers through plasma enhanced hot filament chemical vapour deposition. The field electron emission properties were investigated using anode probes of different diameters. Non-linearity was observed in the field electron emission (FEE) Fowler–Nordheim (FN) plots at high electric field. The numerical calculations based on the superposition of two types of characteristic emission sites are consistent with the experimental data. The non-linearity in the FN plot may be attributed to the non-uniform field enhancement factor (FEF) of the graphitic nanocones. At low electric field, electrons are emitted mainly from nanocones with large FEF, corresponding to a small slope magnitude in the FN plots. With the increasing electric field, the nanocones with small FEFs also contribute to the emission current, which results in a reduced average FEF and so a larger slope magnitude. If the difference in FEFs of the two types of emission sites is large, the emission sites with smaller FEF may not be able to participate in emission even at the highest electric field tested in the experiments and the FN plot remains a straight line determined presumably by the emission sites with larger FEF.

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 score0.016
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.252
Teacher spread0.245 · 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

Citations25
Published2006
Admission routes2
Has abstractyes

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