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Record W2038501808 · doi:10.1116/1.4902016

Fiber tip-based electron source

2014· article· en· W2038501808 on OpenAlexaff
Albert Căsăndruc, Günther Kassier, Haider Zia, Robert Bücker, R. J. Dwayne Miller

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2014
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsUniversity of Toronto
FundersUniversität HamburgMax-Planck-Gesellschaft
KeywordsMaterials scienceElectric fieldOpticsOptical fiberTungstenFiberField electron emissionOptoelectronicsScanning electron microscopeTransmission electron microscopyElectronNanotechnologyPhysicsComposite material

Abstract

fetched live from OpenAlex

The authors report on the first experimental characterization of a fiber tip-based electron source, where electron emission can be triggered by both electric field and optical excitation. Our approach consists of coating the open aperture of a commercial 100 nm apex size near-field scanning optical microscopy fiber tip with a 10 nm thick tungsten (W) layer, which is back-illuminated by a 405 nm continuous-wave laser beam in the presence of an extraction electric field. Despite the very low optical transmission of the fiber due to the subwavelength aperture size, measurements show a clearly enhanced emission when photoexciting the W layer with respect to pure field emission. The emission response time is slower than the optical trigger time, suggesting that thermal effects are predominant in the studied regime. To back up this hypothesis, the authors fabricated a nanometric thermocouple probe based on a Pt/Au junction and measured the temporal response of the tip temperature. The measured switch-on time for the tip temperature is consistent with the switch-on time of the optically enhanced electron emission.

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 score0.003
Threshold uncertainty score0.009

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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
Published2014
Admission routes1
Has abstractyes

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