Quantification of High-Efficiency Trapping of Nanoparticles in a Double Nanohole Optical Tweezer
Bibliographic record
Abstract
We measure the dynamics of 20 nm polystyrene particles in a double nanohole trap to determine the trap stiffness for various laser powers. Both the autocorrelation analysis of Brownian fluctuations and the trapping transient analysis provide a consistent value of ∼ 0.2 fN/nm stiffness for 2 mW of laser power, which is similar to theoretical calculations for aperture trapping. As expected, the stiffness increases linearly with laser power. This is comparable to the stiffness obtained for conventional optical traps for trapping, but for ten times smaller dielectric particles and less power. This approach will allow us to quantitatively evaluate future aperture-based optical traps, with the goal of studying the folding dynamics of smaller proteins (∼ 10 kDa) and small-molecule interactions.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".