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Record W1759277842 · doi:10.1139/cjc-2014-0247

High-resolution Raman imaging of bundles of single-walled carbon nanotubes by tip-enhanced Raman spectroscopy

2014· article· en· W1759277842 on OpenAlexafffundvenue
Nastaran Kazemi‐Zanjani, Pierangelo Gobbo, Ziyan Zhu, Mark S. Workentin, François Lagugné‐Labarthet

Bibliographic record

VenueCanadian Journal of Chemistry · 2014
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRaman spectroscopyCarbon nanotubeRaman microscopeChemistryMicroscopeMicroscopyOpticsResolution (logic)PlasmonSpectroscopyWavelengthAnalytical Chemistry (journal)Materials scienceRaman scatteringNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

Bundles of single-walled carbon nanotubes (SWCNTs) prepared by plasma torch method and further purified, are deposited over a glass coverslip to estimate the spatial resolution of tip-enhanced Raman spectroscopy measurements. For this purpose, near-field Raman maps and spectra of isolated bundles of carbon nanotubes are collected using optimized experimental conditions such as a tightly focused beam using a 1.4 numerical aperture oil immersion microscope objective and a gold coated atomic force microscope probe illuminated by a radially polarized 632.8 nm wavelength to selectively excite the localized surface plasmon confined at the extremity of the tip. The near-field nature of the collected Raman signals is evaluated through measuring the decay of the Raman signal with respect to the tip-sample separation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.178
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2014
Admission routes3
Has abstractyes

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