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Record W4402645240 · doi:10.1021/acs.nanolett.4c03588

Cascade Enhancement and Efficient Collection of Single Photon Emission under Topological Protection

2024· article· en· W4402645240 on OpenAlexaff
Yali Jia, Zhaohua Tian, Qi Liu, Zongcheng Mou, Zihan Mo, Yu Tian, Qihuang Gong, Ying Gu

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsCascadeTopology (electrical circuits)PhotonMaterials scienceNanotechnologyPhysicsOptoelectronicsChemical physicsChemistryOpticsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide High emission rate, high collection efficiency, and immunity to defects are the requirements of implementing on-chip single photon sources. Here, we theoretically demonstrate that both cascade enhancement and high collection efficiency of emitted photons from a single emitter can be achieved simultaneously in a topological photonic crystal containing a resonant dielectric nanodisk. The nanodisk excited by a magnetic emitter can be regarded as a large equivalent magnetic dipole. The near-field overlapping between this equivalent magnetic dipole and edge state enables achieving a cascade enhancement of single-photon emission with a Purcell factor exceeding 4 × 10 3 . These emitted photons are guided into edge states with a collection efficiency of more than 90%, which is also corresponding to quantum yield due to topological antiscattering and the absence of absorption. The proposed mechanism under topological protection has potential applications in on-chip light–matter interactions, quantum light sources, and nanolasers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2024
Admission routes1
Has abstractyes

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