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Record W2322953175 · doi:10.1021/la300770b

Controlled Orientation and Alignment in Films of Single-Walled Carbon Nanotubes Using Inkjet Printing

2012· article· en· W2322953175 on OpenAlexafffund
Simon Beyer, Konrad Walus

Bibliographic record

VenueLangmuir · 2012
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsUniversity of British Columbia
FundersCMC Microsystems
KeywordsMaterials scienceCarbon nanotubeScanning electron microscopeCrystallinityNanotechnologyCoffee ring effectMorphology (biology)EvaporationComposite materialElastomerChemical engineering

Abstract

fetched live from OpenAlex

An inkjet printing procedure for depositing films of carbon nanotubes (CNTs) that exhibit a very high degree of long-range mutual alignment as well as a controlled orientation with respect to the printed geometry is presented. CNT self-assembly was induced by the intrinsic lyotropic liquid crystallinity of CNT suspensions. Sufficient concentrations are reached by matching the inkjet deposition rate to the numerically modeled local evaporation rate of the printed feature and enable the CNT suspension to be printed using standard inkjet printing. Surface alignment was verified using scanning electron microscopy (SEM) and polarized light microscopy. In addition, the bulk morphology was investigated and found to be composed of stacked planar layers that did not necessarily have the same long-range orientation found on the surface. The bulk morphology was characterized by removing layers through an elastomeric peeling process and by observing cross sections of the films using SEM. CNT concentration and length were spanned experimentally, and it was found that very short and very long CNTs as well as low concentration suspensions did not yield long-range alignment.

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 score0.025
Threshold uncertainty score0.281

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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

Citations36
Published2012
Admission routes2
Has abstractyes

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