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Record W2523321221 · doi:10.1021/acsnano.6b05077

Real Time Hyperspectroscopy for Dynamical Study of Carbon Nanotubes

2016· article· en· W2523321221 on OpenAlexaff
J. Lefebvre

Bibliographic record

VenueACS Nano · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceNanotechnologyCarbon fibersChemical physicsPhysicsComposite numberComposite material

Abstract

fetched live from OpenAlex

Photoluminescence excitation mapping used routinely to characterize semiconducting single-walled carbon nanotubes (sc-SWCNTs) is extended to provide time-dependent data. A hyperspectral method based on a supercontinuum light source and an imaging detector is demonstrated with excitation and emission wavelengths spanning 600-1000 and 1050-1650 nm, respectively, at time scales <100 ms. An example of a titration experiment is given to highlight the potential offered by the technique. Specifically, the dependence of luminescence yield on acceptor concentration is tracked for 18 different chirality assigned sc-SWCNT species. Dopant-induced quenching presents a simple dependence on nanotube diameter, with larger diameter being most affected, while chirality does not appear to be a dominant factor. In a second example, the successive addition of acceptor and donor molecules leads to a modulation of luminescence intensity upon hole doping and dedoping.

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 score0.001
Threshold uncertainty score0.483

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 head0.010
GPT teacher head0.265
Teacher spread0.255 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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