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Record W2589042227 · doi:10.1088/1361-6528/aa613e

A novel field emission microscopy method to study field emission characteristics of freestanding carbon nanotube arrays

2017· article· en· W2589042227 on OpenAlexaff
Yunhan Li, Yonghai Sun, David A. Jaffray, John T. W. Yeow

Bibliographic record

VenueNanotechnology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Waterloo
Fundersnot available
KeywordsCommon emitterMaterials scienceCarbon nanotubeField electron emissionJoule heatingNanotechnologyOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Field emission (FE) uniformity and the mechanism of emitter failure of freestanding carbon nanotube (CNT) arrays have not been well studied due to the difficulty of observing and quantifying FE performance of each emitter in CNT arrays. Herein a field emission microscopy (FEM) method based on poly(methyl methacrylate) (PMMA) thin film is proposed to study the FE uniformity and CNT emitter failure of freestanding CNT arrays. FE uniformity of freestanding CNT arrays and different levels of FE current contributions from each emitter in the arrays are recorded and visualized. FEM patterns on the PMMA thin film contain the details of the CNT emitter tip shape and whether multiple CNT emitters occur at an emission site. Observation of real-time FE performance and the CNT emitter failure process in freestanding CNT arrays are successfully achieved using a microscopic camera. High emission currents through CNT emitters causes Joule heating and light emission followed by an explosion of the CNTs. The proposed approach is capable of resolving the major challenge of building the relationship between FE performance and CNT morphologies, which can significantly facilitate the study of FE non-uniformity, the emitter failure mechanism and the development of stable and reliable FE devices in practical applications.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0020.001
Research integrity0.0010.000
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 head0.020
GPT teacher head0.332
Teacher spread0.312 · 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.

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

Citations23
Published2017
Admission routes1
Has abstractyes

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