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Record W2793691879 · doi:10.1038/s41467-018-03120-3

Reshaping the phonon energy landscape of nanocrystals inside a terahertz plasmonic nanocavity

2018· article· en· W2793691879 on OpenAlexafffund
Xin Jin, Andrea Cerea, Gabriele C. Messina, Andrea Rovere, Riccardo Piccoli, Francesco De Donato, Francisco Palazón, A. Perucchi, Paola Di Pietro, Roberto Morandotti, S. Lupi, Francesco De Angelis, Mirko Prato, Andréa Toma, Luca Razzari

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologies1000 Talents Sichuan ProgramCompagnia di San Paolo
KeywordsPhononPlasmonMaterials scienceNanomaterialsOptoelectronicsTerahertz radiationRaman spectroscopyNanoscopic scaleNanocrystalCondensed matter physicsNanotechnologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Phonons (quanta of collective vibrations) are a major source of energy dissipation and drive some of the most relevant properties of materials. In nanotechnology, phonons severely affect light emission and charge transport of nanodevices. While the phonon response is conventionally considered an inherent property of a nanomaterial, here we show that the dipole-active phonon resonance of semiconducting (CdS) nanocrystals can be drastically reshaped inside a terahertz plasmonic nanocavity, via the phonon strong coupling with the cavity vacuum electric field. Such quantum zero-point field can indeed reach extreme values in a plasmonic nanocavity, thanks to a mode volume well below λ 3 /10 7 . Through Raman measurements, we find that the nanocrystals within a nanocavity exhibit two new “hybridized” phonon peaks, whose spectral separation increases with the number of nanocrystals. Our findings open exciting perspectives for engineering the optical phonon response of functional nanomaterials and for implementing a novel platform for nanoscale quantum optomechanics.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.274
Teacher spread0.253 · 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 designNot applicable
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

Citations41
Published2018
Admission routes2
Has abstractyes

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