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Record W1969384705 · doi:10.1002/smll.200801357

Fabrication and Characterization of Graphitic Carbon Nanostructures with Controllable Size, Shape, and Position

2009· article· en· W1969384705 on OpenAlexafffund
Rongbing Du, Solomon Ssenyange, Mirwais Aktary, Mark T. McDermott

Bibliographic record

VenueSmall · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsApplied Nanotools (Canada)National Institute for NanotechnologyUniversity of Alberta
FundersUniversity of Alberta
KeywordsMaterials scienceNanotechnologyFabricationCarbon fibersNanostructureCarbon nanotubeCharacterization (materials science)Nanoscopic scaleNanometreAmorphous carbonNanolithographyCarbonizationCarbide-derived carbonAmorphous solidElectrical conductorElectron-beam lithographyResistComposite materialLayer (electronics)Carbon nanofiberScanning electron microscope

Abstract

fetched live from OpenAlex

The incorporation of carbon materials in micro- and nanoscale devices is being widely investigated due to the promise of enhanced functionality. Challenges in the positioning and addressability of carbon nanotubes provide the motivation for the development of new processes to produce nanoscale carbon materials. Here, the fabrication of conducting, nanometer-sized carbon structures using a combination of electron beam lithography (EBL) and carbonisation is reported. EBL is used to directly write predefined nanometer-sized patterns in a thin layer of negative resist in controllable locations. Careful heat treatment results in carbon nanostructures with the size, shape, and location originally defined by EBL. The pyrolysis process results in significant shrinkage of the structures in the vertical direction and minimal loss in the horizontal direction. Characterization of the carbonized material indicates a structure consisting of both amorphous and graphitized carbon with low levels of oxygen. The resistivity of the material is similar to other disordered carbon materials and the resistivity is maintained from the bulk to the nanoscale. This is demonstrated by fabricating a nanoscale structure with predictable resistance. The ability to fabricate these conductive structures with known dimensions and in predefined locations can be exploited for a number of applications. Their use as nanoband electrodes is also demonstrated.

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 score0.296
Threshold uncertainty score0.294

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 head0.004
GPT teacher head0.189
Teacher spread0.184 · 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

Citations45
Published2009
Admission routes2
Has abstractyes

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