MétaCan
Menu
Back to cohort
Record W3171581625 · doi:10.1021/acsphotonics.1c00525

Single Nanoflake Hexagonal Boron Nitride Harmonic Generation with Ultralow Pump Power

2021· article· en· W3171581625 on OpenAlexafffund
Ghazal Hajisalem, Mirali Seyed Shariatdoust, Rana Faryad Ali, Byron D. Gates, Paul E. Barclay, Reuven Gordon

Bibliographic record

VenueACS Photonics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of CalgarySimon Fraser UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSecond-harmonic generationMaterials scienceOptoelectronicsPlasmonNonlinear opticsLaserDiffractionOpticsNanoscopic scaleHigh harmonic generationNanophotonicsOptical tweezersLithium niobateNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The strong nonlinear optical response of two-dimensional materials has applications in bioimaging and integrated optical information processing; however, past experiments were on diffraction limited samples or required intense pulsed lasers, which is a detriment to potential applications due to cost, power and complexity. Here we show that second harmonic generation can be achieved from single subwavelength two-dimensional material nanoflakes smaller than the diffraction limit using a plasmonic optical tweezer with a low-power (down to 3 mW) laser diode operating in continuous-wave mode. A double nanohole plasmonic tweezer enhances the local field and the local density of optical states to allow for trapping and significant nonlinear generation at the nanoscale. Also, the fact that it is a two-dimensional material means that it can be positioned closer to the highest field regions, realizing 2 orders of magnitude higher power second harmonic generation than other nonlinear materials like lithium niobate. The ability to have simple high efficiency nonlinear generation at the nanoscale will benefit future nonlinear optics applications of these emerging materials.

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 score0.786

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueACS PhotonicsSame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207