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Record W4412604512 · doi:10.1021/acs.nanolett.5c02170

Enhanced Photon-Pair Generation from a van der Waals Metasurface

2025· article· en· W4412604512 on OpenAlexaff
Tongmiao Fan, Yilin Tang, Shaun Lung, Maximilian A. Weissflog, Jinyong Ma, Saniya Shinde, Sina Saravi, Mudassar Nauman, Wenkai Yang, Hao Qin, Shuyao Qiu, Andrey A. Sukhorukov, Yuerui Lu, Frank Setzpfandt

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsDouglas College
FundersDeutsche ForschungsgemeinschaftAustralian Research CouncilCarl-Zeiss-Stiftung
Keywordsvan der Waals forcePhotonVan der Waals strainPhysicsMaterials scienceAtomic physicsMolecular physicsNanotechnologyOptoelectronicsChemical physicsQuantum mechanicsVan der Waals radiusMolecule

Abstract

fetched live from OpenAlex

Quantum photon pairs play a pivotal role in many quantum applications. Metasurfaces, two-dimensional arrays of nanostructures, have been studied intensively to enhance and control pair generation via spontaneous parametric downconversion (SPDC). Van der Waals (VdW) layered materials have emerged as promising candidates for nonlinear materials in quantum light sources, owing to their high nonlinear susceptibility and compatibility with on-chip integration. In this work, we present the first demonstration of SPDC from a metasurface composed of the VdW material 3R-MoS 2 . The nanoresonators support quasi-bound states in the continuum (qBIC) with a quality factor of up to 120, enhancing light-matter interactions. This design achieves a 20-fold increase in SPDC rate compared to an unstructured film and significantly higher brightness, resulting in enhanced quantum photon-pair generation. This work establishes a new approach for utilizing van der Waals metasurfaces in the generation of quantum photon pairs, opening avenues for advanced quantum applications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.001

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2025
Admission routes1
Has abstractyes

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