Enhanced Optical Trapping and Arrangement of Nano-Objects in a Plasmonic Nanocavity
Bibliographic record
Abstract
Gentle manipulation of micrometer-sized dielectric objects with optical forces has found many applications in both life and physical sciences. To further extend optical trapping toward the true nanometer scale, we present an original approach combining self-induced back action (SIBA) trapping with the latest advances in nanoscale plasmon engineering. The designed resonant trap, formed by a rectangular plasmonic nanopore, is successfully tested on 22 nm polystyrene beads, showing both single- and double-bead trapping events. The mechanism responsible for the higher stability of the double-bead trapping is discussed, in light of the statistical analysis of the experimental data and numerical calculations. Furthermore, we propose a figure of merit that we use to quantify the achieved trapping efficiency and compare it to prior optical nanotweezers. Our approach may open new routes toward ultra-accurate immobilization and arrangement of nanoscale objects, such as biomolecules.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".