Accessible high-performance double nanohole tweezers
Bibliographic record
Abstract
Nanohole optical tweezers have been used by several groups to trap and analyze proteins. In this work, we demonstrate that it is possible to create high-performance double nanohole (DNH) substrates for trapping proteins without the need for any top-down approaches (such as electron microscopy or focused-ion beam milling). Using polarization analysis, we identify DNHs as well as determine their orientation and then use them for trapping. We are also able to identify other hole configurations, such as single, trimers and other clusters. We explore changing the substrate from glass to polyvinyl chloride to enhance trapping ability, showing 7 times lower minimum trapping power, which we believe is due to reduced surface repulsion. Finally, we present tape exfoliation as a means to expose DNHs without damaging sonication or chemical methods. Overall, these approaches make high quality optical trapping using DNH structures accessible to a broad scientific community.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".