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Nanotube Encoders

2007· article· en· W4234766824 on OpenAlexaff
Lixin Dong, Arunkumar Subramanian, Bradly J. Nelson, Yu Sun

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceScanning electron microscopeField electron emissionCarbon nanotubeAnodeNanotechnologyCommon emitterNanoscopic scaleChemical vapor depositionResolution (logic)LithographyOptoelectronicsPlasma-enhanced chemical vapor depositionOpticsElectronElectrodeComposite material

Abstract

fetched live from OpenAlex

Linear encoders for nanoscale position sensing based on vertical arrays of single multi-walled carbon nanotubes (MWNTs) are investigated from experimental, theoretical, and design perspectives. Vertically aligned single MWNTs are realized using a combination of e-beam lithography and plasma-enhanced chemical vapor deposition (PECVD) growth. Field emission properties of the array are investigated inside a scanning electron microscope (SEM) equipped with a 3-DOF nanorobotic manipulator with nanometer resolution functioning as a scanning anode. Lateral position of the scanning anode is sensed from the emission distribution. High resolution (best: 12.9 nm; practical: 38.0 nm) for lateral position sensing around an emitter has been realized.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0060.012
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.035
GPT teacher head0.313
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicCarbon Nanotubes in CompositesFrench-language works237,207