Optical Trapping of Nanoparticles
Bibliographic record
Abstract
Optical trapping is a technique for immobilizing and manipulating small objects in a gentle way using light, and it has been widely applied in trapping and manipulating small biological particles. Ashkin and co-workers first demonstrated optical tweezers using a single focused beam. The single beam trap can be described accurately using the perturbative gradient force formulation in the case of small Rayleigh regime particles. In the perturbative regime, the optical power required for trapping a particle scales as the inverse fourth power of the particle size. High optical powers can damage dielectric particles and cause heating. For instance, trapped latex spheres of 109 nm in diameter were destroyed by a 15 mW beam in 25 sec, which has serious implications for biological matter. A self-induced back-action (SIBA) optical trapping was proposed to trap 50 nm polystyrene spheres in the non-perturbative regime. In a non-perturbative regime, even a small particle with little permittivity contrast to the background can influence significantly the ambient electromagnetic field and induce a large optical force. As a particle enters an illuminated aperture, light transmission increases dramatically because of dielectric loading. If the particle attempts to leave the aperture, decreased transmission causes a change in momentum outwards from the hole and, by Newton's Third Law, results in a force on the particle inwards into the hole, trapping the particle. The light transmission can be monitored; hence, the trap can become a sensor. The SIBA trapping technique can be further improved by using a double-nanohole structure. The double-nanohole structure has been shown to give a strong local field enhancement. Between the two sharp tips of the double-nanohole, a small particle can cause a large change in optical transmission, thereby inducing a large optical force. As a result, smaller nanoparticles can be trapped, such as 12 nm silicate spheres and 3.4 nm hydrodynamic radius bovine serum albumin proteins. In this work, the experimental configuration used for nanoparticle trapping is outlined. First, we detail the assembly of the trapping setup which is based on a Thorlabs Optical Tweezer Kit. Next, we explain the nanofabrication procedure of the double-nanohole in a metal film, the fabrication of the microfluidic chamber and the sample preparation. Finally, we detail the data acquisition procedure and provide typical results for trapping 20 nm polystyrene nanospheres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".