Sensing nanoparticles using a double nanohole optical trap
Bibliographic record
Abstract
We use a double nanohole (DNH) optical trap to quantify the size and concentration of nanoparticles in solution. The time to trap shows a linear dependence with nanosphere size and a -2/3 power dependence with nanosphere concentration, which is in agreement with simple microfluidic considerations. The DNH approach has size-specificity on the order of a few nanometers, which was used to selectively quantify particles of a single size within a heterogeneous solution. By looking at individual trapping events, it is in principle possible to extend this approach to the ultimate limit of a single particle concentration, while also being able to operate at high concentrations in the same configuration. In addition, the DNH trap allows us to hold onto individual particles and thereby study constituents of a heterogeneous mixture. By repeating the trapping measurements on spherical particles of different refractive index, we found that the transmission step that indicates trapping scales empirically with the Clausius-Mossotti factor. This approach may be applied to several sensing applications, such as in the study of virus populations, where concentrations vary over many orders of magnitude.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".