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Record W2976869784 · doi:10.1021/acsanm.9b01514

Sonication-Enhanced Alignment Relay Technique for the Orientation of Single-Walled Carbon Nanotubes

2019· article· en· W2976869784 on OpenAlexafffund
Monika R. Snowdon, Serxho Selmani, Derek J. Schipper

Bibliographic record

VenueACS Applied Nano Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCarbon nanotubeSonicationMaterials scienceMiniaturizationNanotechnologyNanotubeMicrofluidicsRelayOptoelectronicsChemical engineering

Abstract

fetched live from OpenAlex

The high demand for thin, lightweight yet fast and efficient devices is a driving force behind the miniaturization trend in the electronics industry. Specifically, the advancement of semiconducting single-walled carbon nanotubes (SWNTs) can continue to revolutionize transistors, although there are still many challenges ahead. We have previously reported an alignment relay technique (ART) that is capable of simultaneously controlling the alignment, length, and diameter of surface deposited SWNTs. However, the current technique yields inconsistencies in orientation, lengths of tubes, and their density. Here, we present a reviewed ART protocol that includes sonication for improved selectivity. We show that the SWNTs average alignment increased from 40% to 77% within a 10° range in orientation with sonication times as low as 5 min. Sonication generated larger diameter nanotubes on the surface, with a preference for semiconducting chiral tubes in the range of 1.44–1.61 nm in diameter. Consequently, simple alterations to the standard alignment relay technique can prove to be prosperous in improving selectivity and orientational control of single-walled carbon nanotubes. This work has direct impact for the simultaneous control of nanotube alignment and nanotube chiralities.

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)0.001
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 score0.007
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

Explore more

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