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Record W3048279997 · doi:10.1088/1361-6528/abaf22

Field emission from AlGaN nanowires with low turn-on field

2020· preprint· en· W3048279997 on OpenAlexaff
Filippo Giubileo, Antonio Di Bartolomeo, Yun Zhong, Songrui Zhao, M. Passacantando

Bibliographic record

VenueNanotechnology · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsField electron emissionNanowireMaterials scienceTungstenAnodeCathodeElectronField (mathematics)OptoelectronicsScanning electron microscopeElectrodeCurrent densityHomogeneousField emission microscopyNanotechnologyOpticsPhysicsDiffractionChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract We fabricate AlGaN nanowires by molecular beam epitaxy and we investigate their field emission properties by means of an experimental setup using nano-manipulated tungsten tips as electrodes, inside a scanning electron microscope. The tip-shaped anode gives access to local properties, and allows collecting electrons emitted from areas as small as 1 µ m 2 . The field emission characteristics are analysed in the framework of Fowler–Nordheim theory and we find a field enhancement factor as high as β = 556 and a minimum turn-on field E t u r n − o n = 17 V µ m −1 for a cathode–anode separation distance d = 500 nm. We show that for increasing separation distance, E t u r n − o n increases up to about 35 V µ m −1 and β decreases to ∼100 at d = 1600 nm. We also demonstrate the time stability of the field emission current from AlGaN nanowires for several minutes. Finally, we explain the observation of modified slope of the Fowler–Nordheim plots at low fields in terms of non-homogeneous field enhancement factors due to the presence of protruding emitters.

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 score0.001
Threshold uncertainty score0.004

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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