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Record W2090770472 · doi:10.1063/1.4863781

Plasmon-enhanced terahertz emission in self-assembled quantum dots by femtosecond pulses

2014· article· en· W2090770472 on OpenAlexaff
F. Carreño, M. A. Antón, Sonia Melle, Óscar G. Calderón, E. Cabrera, Joel D. Cox, Mahi R. Singh, Ana Egatz-Gómez

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsWestern University
FundersMinisterio de Ciencia e Innovación
KeywordsTerahertz radiationFemtosecondLaserQuantum dotPlasmonMaterials scienceDipoleDiscrete dipole approximationOptoelectronicsSurface plasmonOpticsPhysics

Abstract

fetched live from OpenAlex

A scheme for terahertz (THz) generation from intraband transition in a self-assembled quantum dot (QD) molecule coupled to a metallic nanoparticle (MNP) is analyzed. The QD structure is described as a three-level atom-like system using the density matrix formalism. The MNP with spherical geometry is considered in the quasistatic approximation. A femtosecond laser pulse creates a coherent superposition of two subbands in the quantum dots and produces localized surface plasmons in the nanoparticle which act back upon the QD molecule via dipole-dipole interaction. As a result, coherent THz radiation with a frequency corresponding to the interlevel spacing can be obtained, which is strongly modified by the presence of the MNP. The peak value of the terahertz signal is analyzed as a function of nanoparticle's size, the MNP to QD distance, and the area of the applied laser field. In addition, we theoretically demonstrate that the terahertz pulse generation can be effectively controlled by making use of a train of femtosecond laser pulses. We show that by a proper choice of the parameters characterizing the pulse train a huge enhancement of the terahertz signal is obtained.

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 score0.050
Threshold uncertainty score0.604

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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