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Record W2022593424 · doi:10.1063/1.3496486

Middle-ultraviolet laser photoelectron emission from vertically aligned millimeter-long multiwalled carbon nanotubes

2010· article· en· W2022593424 on OpenAlexaff
Parham Yaghoobi, Mario Michan, Alireza Nojeh

Bibliographic record

VenueApplied Physics Letters · 2010
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceMillimeterUltravioletX-ray photoelectron spectroscopyNanotubeQuantum efficiencyOptoelectronicsSemimetalField electron emissionNanotechnologyOpticsPhysicsElectronBand gap

Abstract

fetched live from OpenAlex

We demonstrate photoelectron emission from millimeter-long forests of vertically aligned multiwalled carbon nanotubes using 266 nm light, which illuminates the forests from the side. We have measured quantum efficiencies in the order of ∼10−5 at low fields (pure photoemission) and ∼10−3 at an applied field of 0.3 V μm−1, which are 2–4 orders of magnitude higher than those obtained from films of randomly oriented nanotubes, and approach the quantum efficiency of semimetal photocathodes. Through optical simulations we show that 266 nm light is absorbed within the first few layers of the nanotube forest.

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 score0.002

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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