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Cited by 179

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Cavity-Enhanced Transport of Charge

Open access
Hagenmüller, David, Schachenmayer, Johannes, Schütz, Stefan, Genes, Claudiu, Pupillo, Guido·2017·Physical Review Letters

Abstract

We theoretically investigate charge transport through electronic bands of a mesoscopic one-dimensional system, where interband transitions are coupled to a confined cavity mode, initially prepared close to its vacuum. This coupling leads to light-matter hybridization where the dressed fermionic bands interact via absorption and emission of dressed cavity photons. Using a self-consistent nonequilibrium Green's function method, we compute electronic transmissions and cavity photon spectra and demonstrate how light-matter coupling can lead to an enhancement of charge conductivity in the steady state. We find that depending on cavity loss rate, electronic bandwidth, and coupling strength, the dynamics involves either an individual or a collective response of Bloch states, and we explain how this affects the current enhancement. We show that the charge conductivity enhancement can reach orders of magnitudes under experimentally relevant conditions.

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Metadata

Authors
Hagenmüller, D., Schachenmayer, J., Schütz, S., Genes, C., & Pupillo, G.
Venue
Physical Review Letters
Published
2017-11-28
Volume
119
Issue
22
Pages
223601-223601
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2604227235
Cited by
179
References
6
FWCI
6.61
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Cavity-Enhanced Transport of Charge. https://app.cochat.ai/papers/10.1103/physrevlett.119.223601
The paper
Hagenmüller, D., Schachenmayer, J., Schütz, S., Genes, C., & Pupillo, G. (2017). Cavity-Enhanced Transport of Charge. Physical Review Letters. https://doi.org/10.1103/physrevlett.119.223601

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.