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Record W4409027224 · doi:10.1002/pssb.202400616

Exploring Quantum Emission in Bilayer WSe<sub>2</sub>: Strain Effects from Nanopillars

2025· article· en· W4409027224 on OpenAlexafffund
Palwinder Singh, Grant R. Wilbur, Edith Yeung, Jasleen Kaur Jagde, Megha Jain, David B. Northeast, J. Lapointe, Dan Dalacu, Kimberley C. Hall

Bibliographic record

Venuephysica status solidi (b) · 2025
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsNational Research Council CanadaUniversity of OttawaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanopillarBilayerStrain (injury)Materials scienceNanotechnologyCondensed matter physicsOptoelectronicsPhysicsChemistryNanostructureBiologyMembrane

Abstract

fetched live from OpenAlex

2D semiconductors subject to localized strain represent an emerging, scalable platform for the generation of bright single‐photon emitters. In this study, the observation of quantum emission from bilayer (BL) WSe 2 under varying 3D strain induced by dielectric nanopillars is reported. Spectrally narrow and bright photoluminescence along with antibunched photon statistics is observed, comparable to those of monolayer WSe 2 , consistent with strain‐mediated quantum emission via defect states. The results indicate that the brightest emitters are created with pillar diameters ranging from 175 to 195 nm. A robust second‐order correlation function of g (2) (0) = 0.139 confirms strong antibunching, indicative of high‐quality single‐photon emission. These findings highlight the potential of using nanopillar arrays to manipulate the electronic states and quantum emission in transition metal dichalcogenide BLs, paving the way for future applications in quantum technologies.

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 score0.014
Threshold uncertainty score0.913

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 head0.034
GPT teacher head0.267
Teacher spread0.234 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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