MétaCan
Menu
Back to cohort
Record W2901067236 · doi:10.1109/ivnc.2018.8520137

The role of lateral confinement in the localized heating of thermionic emitters based on carbon nanotube forests

2018· article· en· W2901067236 on OpenAlexafffund
M. M. Chowdhury, Harrison D. E. Fan, Mike Chang, Kais Dridi, Kevin Voon, G. A. Sawatzky, Alireza Nojeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsThermionic emissionCarbon nanotubeMaterials scienceThermal conductionElectrical conductorJoule heatingWork (physics)OptoelectronicsPerpendicularNanotubeNanotechnologyElectronComposite materialPhysicsThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

When vertically aligned carbon nanotube arrays (CNT forests) are heated by optical, electrical, or any other means, heat confinement in the lateral directions (i.e. perpendicular to the CNTs' axes), which stems from the anisotropic structure of the forest, is expected to play an important role. It has been found that, in spite of being primarily conductive along the CNTs' axes, focusing a laser beam on the sidewall of a CNT forest can lead to a highly localized hot region-an effect known as “Heat Trap”-and efficient thermionic emission. This unusual heat confinement phenomenon has applications where the spread of heat has to be minimized, but electrical conduction is required, notably in energy conversion (e.g. vacuum thermionics and thermoelectrics). However, despite its strong scientific and practical importance, the existence and role of the lateral heat confinement in the Heat Trap effect have so far been elusive. In this work, for the first time, by using a rotating elliptical laser beam, we directly observe the existence of this lateral heat confinement and its corresponding effects on the unusual temperature rise during the Heat Trap effect.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.127

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2018
Admission routes2
Has abstractyes

Explore more

Same topicThermal Radiation and Cooling TechnologiesFrench-language works237,207