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Record W2082099113 · doi:10.1063/1.3663438

Field-emission-assisted approach to dry micro-electro-discharge machining of carbon-nanotube forests

2011· article· en· W2082099113 on OpenAlexafffund
Tanveer Saleh, Masoud Dahmardeh, Anas Bsoul, Alireza Nojeh, Kenichi Takahata

Bibliographic record

VenueJournal of Applied Physics · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
FundersJordan University of Science and TechnologyNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceCarbon nanotubeMachiningElectrical discharge machiningAnodeCathodePolarity (international relations)Field electron emissionMicrostructureMicropatterningNanotechnologyCarbon fibersVoltageOptoelectronicsComposite materialMetallurgyElectrodeElectrical engineeringElectronChemistry

Abstract

fetched live from OpenAlex

This work investigates dry micro-electro-discharge machining (μEDM) of vertically aligned carbon nanotube (CNT) forests that are used as cathodes in the process, as opposed to conventional μEDM where the material to be machined forms the anode, toward achieving higher precision in the patterned microstructures. The new configuration with the reversed polarity is observed to generate higher discharge currents in the process, presumably due to effective field-emission from CNTs. This effect allows the process to be performed at very low discharge energies, approximately 80× smaller than in the conventional normal-polarity case, with the machining voltage and tolerance down to 10 V and 2.5 μm, respectively, enabling high-precision high-aspect-ratio micropatterning in the forests. The new approach is also demonstrated to make the process faster, cleaner, and more stable than conventional processing. Spectroscopic analyses of the forests processed by reverse μEDM show no evidence of significant crystalline deterioration or contamination in the CNTs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.020
GPT teacher head0.245
Teacher spread0.224 · 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 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

Citations25
Published2011
Admission routes2
Has abstractyes

Explore more

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