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Record W1992327080 · doi:10.1088/0957-4484/15/5/028

Enhanced Raman microprobe imaging of single-wall carbon nanotubes

2004· article· en· W1992327080 on OpenAlexaff
Viktor G. Hadjiev, Sivaram Arepalli, Pavel Nikolaev, S. Jandl, Leonard Yowell

Bibliographic record

VenueNanotechnology · 2004
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de Sherbrooke
FundersNational Aeronautics and Space Administration
KeywordsMaterials scienceRaman spectroscopyMicroprobeCarbon nanotubePerpendicularRaman scatteringPolarization (electrochemistry)AnisotropyOpticsMolecular physicsNanotechnology

Abstract

fetched live from OpenAlex

We explore Raman microprobe capabilities to visualize single wall carbon nanotubes (SWCNTs). Although this technique is limited to a micron scale, we demonstrate that images of individual SWCNTs, bundles or their agglomerates can be generated by mapping Raman active elementary excitations. We measured the Raman response from carbon vibrations in SWCNTs excited by confocal scanning of a focused laser beam. Carbon vibrations reveal key characteristics of SWCNTs as nanotube diameter distribution (radial breathing modes, RBM, 100-300 cm(exp -1)), presence of defects and functional groups (D-mode, 1300-1350 cm(exp -1)), strain and oxidation states of SWCNTs, as well as metallic or semiconducting character of the tubes encoded in the lineshape of the G-modes at 1520-1600 cm(exp - 1). In addition, SWCNTs are highly anisotropic scatterers. The Raman response from a SWCNT is maximal for incident light polarization parallel to the tube axis and vanishing for perpendicular directions. We show that the SWCNT bundle shape or direction can be determined, with some limitations, from a set of Raman images taken at two orthogonal directions of the incident light polarization.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 studies0.0000.001
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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.

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

Citations21
Published2004
Admission routes1
Has abstractyes

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