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Plasmonic control of nonlinear two-photon absorption in graphene nanocomposites

2013· article· en· W2097439947 on OpenAlex
Joel D. Cox, Mahi R. Singh, M. A. Antón, F. Carreño

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physics Condensed Matter · 2013
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsWestern University
Fundersnot available
KeywordsGrapheneQuantum dotPlasmonMaterials scienceOptoelectronicsPhotonAbsorption (acoustics)Surface plasmon resonanceGraphene nanoribbonsResonance (particle physics)Two-photon absorptionNanotechnologyLaserPhysicsOpticsAtomic physicsNanoparticle

Abstract

fetched live from OpenAlex

Nonlinear two-photon absorption in a quantum dot-graphene nanoflake nanocomposite system has been investigated. An external laser field is applied to the nanocomposite to simultaneously observe two-photon processes in the quantum dot and excite localized surface plasmons in the graphene nanodisk. This resonance condition can be achieved by tuning the plasmon resonance frequency in the graphene nanoflake via electrostatic gating. It is found that the strong local field of the graphene plasmons can enhance and control nonlinear optical processes in the quantum dot. Specifically, we show that the two-photon absorption coefficient in the quantum dot can be switched between single- and double-peaked spectra by modifying the graphene-quantum dot separation. Two-photon processes in the quantum dot can also be switched on or off by slightly changing the gate voltage applied to the graphene. Our findings indicate that this system can be used for nonlinear optical applications such as all-optical switching, biosensing and signal processing.

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.

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 score0.231
Threshold uncertainty score0.537

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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