Revisiting nonlinear optical trapping of a single nanoparticle using generalized Lorentz-Mie theory
Bibliographic record
Abstract
The elusive role of femtosecond pulsed excitation in enhancing the efficiency of optical trapping of nanoparticles over continuous-wave excitation is revisited. Using generalized Lorentz-Mie theory, optical trapping force on nanoparticles composed of materials having a wide range of linear refractive indices, including metamaterials having negative refractive indices, is investigated. It is shown how the incorporation of optical nonlinearity, fine-tuned by the laser parameters (for example, average power, pulse width, etc.), leads to the emergence of novel phenomena such as trap splitting facilitating trapping of multiple particles that were not captured in a previous study on nanoparticles with varying refractive indices using dipole approximation. Intriguingly, we show that the trap becomes highly stable due to negative optical scattering force, known as ``Fano resonance.'' Furthermore, we observe the disappearance and reappearance of trapping wells with increasing refractive index under both continuous-wave and pulsed excitations. These findings show promising applications in the field of photonics through nanoscale optical manipulation controlled by optical nonlinearity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".