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Record W3006563280 · doi:10.1021/acsnano.9b09466

Quantum Dot-Plasmon Lasing with Controlled Polarization Patterns

2020· article· en· W3006563280 on OpenAlexafffund
Jun Guan, Laxmi Kishore Sagar, Ran Li, Danqing Wang, Golam Bappi, Weijia Wang, Nicolas E. Watkins, Marc R. Bourgeois, Larissa Levina, Fengjia Fan, Sjoerd Hoogland, Oleksandr Voznyy, João M. Pina, Richard D. Schaller, George C. Schatz, Edward H. Sargent, Teri W. Odom

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Toronto
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaInternational Institute for Nanotechnology, Northwestern UniversityDivision of Electrical, Communications and Cyber SystemsMaterials Research Science and Engineering Center, Harvard UniversityU.S. Department of DefenseW. M. Keck FoundationNorthwestern UniversityNational Science Foundation
KeywordsQuantum dotLasing thresholdOptoelectronicsMaterials sciencePolarization (electrochemistry)LaserPlasmonNanophotonicsPhotonicsQuantum dot laserQuantum opticsOpticsSurface plasmonPhysicsSemiconductor laser theorySemiconductor

Abstract

fetched live from OpenAlex

The tailored spatial polarization of coherent light beams is important for applications ranging from microscopy to biophysics to quantum optics. Miniaturized light sources are needed for integrated, on-chip photonic devices with desired vector beams; however, this issue is unresolved because most lasers rely on bulky optical elements to achieve such polarization control. Here, we report on quantum dot-plasmon lasers with engineered polarization patterns controllable by near-field coupling of colloidal quantum dots to metal nanoparticles. Conformal coating of CdSe-CdS core-shell quantum dot films on Ag nanoparticle lattices enables the formation of hybrid waveguide-surface lattice resonance (W-SLR) modes. The sidebands of these hybrid modes at nonzero wavevectors facilitate directional lasing emission with either radial or azimuthal polarization depending on the thickness of the quantum dot film.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.548

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations116
Published2020
Admission routes2
Has abstractyes

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