Nanoaperture optical tweezer with magnetic force characterization of magnetic nanoparticles (Conference Presentation)
Bibliographic record
Abstract
Double nanohole optical tweezers allow for trapping of nanoparticles down to single digit nanometer range, including individual proteins, viruses, DNA fragments and quantum dots. Here we demonstrate dual magnetic force / optical force analysis for the characterization of magnetic nanoparticles. From this single platform we can isolate individual nanoparticles and determine their size, permeability, remanence and permittivity. This is of interest for characterizing magnetic nanoparticles in mixtures, isolating ones of desired characteristics and pick-and-place assembly of magnetic nanoparticles in nanoscale magnetic devices. The magnetic nanoparticle is characterized by analysis of the optical transmission through a double-nanohole aperture with an applied magnetic gradient force. The optical transmission step at trapping, autocorrelation of transmission intensity, distribution of transmission values and variations with applied magnetic field amplitude provide information of individual magnetic nanoparticles that allows for determining their individual material characteristics. The values obtained agree well with past published values for iron oxide, and the size distribution over repeated measurements matches well with scanning electron microscope characterization (and manufacturer specifications).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".