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Record W2028656055 · doi:10.1155/2014/879827

Carbon Nanotube Electron Sources: From Electron Beams to Energy Conversion and Optophononics

2014· article· en· W2028656055 on OpenAlexafffund
Alireza Nojeh

Bibliographic record

VenueISRN Nanomaterials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for InnovationBritish Columbia Innovation CouncilBCFRST Foundation
KeywordsField electron emissionThermionic emissionCarbon nanotubeElectronMaterials scienceElectron gunNanotechnologySecondary emissionOptoelectronicsAtomic physicsCathode rayPhysics

Abstract

fetched live from OpenAlex

Carbon nanotubes have a host of properties that make them excellent candidates for electron emitters. A significant amount of research has been conducted on nanotube-based field-emitters over the past two decades, and they have been investigated for devices ranging from flat-panel displays to vacuum tubes and electron microscopes. Other electron emission mechanisms from carbon nanotubes, such as photoemission, secondary emission, and thermionic emission, have also been studied, although to a lesser degree than field-emission. This paper presents an overview of the topic, with emphasis on these less-explored mechanisms, although field-emission is also discussed. We will see that not only is electron emission from nanotubes promising for electron-source applications, but also its study could reveal unusual phenomena and open the door to new devices that are not directly related to electron beams.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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