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Record W2066761614 · doi:10.1021/nl060530e

Photoluminescence Imaging of Suspended Single-Walled Carbon Nanotubes

2006· article· en· W2066761614 on OpenAlexaff
J. Lefebvre, D. G. Austing, Jeffery Bond, Paul Finnie

Bibliographic record

VenueNano Letters · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsInstitute for Microstructural SciencesUniversity of Ottawa
FundersCore Research for Evolutional Science and Technology
KeywordsPhotoluminescenceCarbon nanotubeWavelengthEmission intensityMaterials scienceAbsorption (acoustics)MillisecondCoherence (philosophical gambling strategy)Intensity (physics)Field electron emissionOptoelectronicsOpticsNanotechnologyPhysicsElectron

Abstract

fetched live from OpenAlex

Single-walled carbon nanotubes (SWNTs) suspended in air over trenches are imaged using their intrinsic near-infrared (NIR) photoluminescence (1.0-1.6 microm). Far-field emission from extended suspended lengths (approximately 50 microm) is both spatially and spectrally resolved, and SWNTs are classified based on the spatial uniformity of their emission intensity and emission wavelength. In a few cases, emission assigned to different (n,m) species is observed along the same suspended segment. Most SWNTs imaged on millisecond time scales show steady emission, but a few fluctuate and suffer a reduction of intensity. The quantum efficiency is dramatically higher than that in previous reports and is estimated at 7%, a value that is precise but subject to corrections because of assumptions about absorption and coherence.

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 score0.002

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 integrity0.0000.000
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations214
Published2006
Admission routes1
Has abstractyes

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