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Mechanism of the Far-Infrared Absorption of Carbon-Nanotube Films

2008· article· en· W2047502946 on OpenAlexaff
Tobias Kampfrath, Konrad von Volkmann, Carla M. Aguirre, P. Desjardins, Richard Martel, M. Krenz, Christian Frischkorn, Martin Wolf, L. Perfetti

Bibliographic record

VenuePhysical Review Letters · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceInfraredConductivityAbsorption (acoustics)Far infraredExcited statePlasmonTerahertz radiationMolecular physicsNanotubeAtomic physicsOptoelectronicsNanotechnologyOpticsPhysicsChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The far-infrared conductivity of single-wall carbon-nanotube ensembles is dominated by a broad absorption peak around 4 THz whose origin is still debated. We observe an overall depletion of this peak when the nanotubes are excited by a short visible laser pulse. This finding excludes optical absorption due to a particle-plasmon resonance and instead shows that interband transitions in tubes with an energy gap of approximately 10 meV dominate the far-infrared conductivity. A simple model based on an ensemble of two-level systems naturally explains the weak temperature dependence of the far-infrared conductivity by the tube-to-tube variation of the chemical potential.

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 score0.002
Threshold uncertainty score0.415

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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

Citations82
Published2008
Admission routes1
Has abstractyes

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