Individual Nanoflakes of Two Dimensional Materials Harmonic Generation with Ultralow Pump Power
Bibliographic record
Abstract
Two dimensional materials with nonlinear optical response are of interest for bioimaging and optical information processing. However, achieving a measurable second order nonlinear signal in thin films and two dimensional materials has relied on using pulsed lasers and intense optical focusing, which limits potential applications require nonlinear response by using low laser power or from nanoscale materials. Here we achieved second harmonic generation from nanoflakes of two dimensional materials with lateral size smaller than the diffraction limit by using a double nanohole plasmonic optical tweezer with a low-power continuous-wave laser. The plasmonic double nanohole aperture enhances the local field intensity and allows for single nanoflake trapping and significant second harmonic generation at the nanoscale. The two dimensional property of nanoflakes allows for positioning in an area with high local field intensity and achieving higher nonlinear response than bulk nonlinear nanoparticlesWe observed an increase in second harmonic generation power two orders of magnitude higher than. other bulk materials such as lithium niobate nanoparticles. This allows for having strong nonlinear generation at the nanoscale for applications such as subwavelength nonlinear imaging or information processing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".