Optical trapping of an encapsulated quantum dot using a double nanohole aperture in a metal film
Bibliographic record
Abstract
Optical trapping is a promising technique which involves holding and manipulating small particles in a non-destructive way. Conventional trapping methods are able to trap dielectric particles with size greater than 100 nm. Using a double-nanohole in a metal film (with sharp tips where the holes meet) has enabled us to trap dielectric particles such as quantum dots and single proteins. This has been achieved even while using low laser power. Since the refractive index of the particle is larger than the surrounding environment, the aperture appears larger when the particle enters the aperture. This allows for more light transmitted through the aperture. The change in transmission changes the light momentum, and by Newton’s third law, there will be a force which will push back the particle to the equilibrium position. The change in light transmission also allows for facile detection of the trapping event. In this work, we use the double-nanohole to trap encapsulated quantum dots. Quantum dots are practically useful for several purposes including computing, biology and electronic devices. The ability to manipulate these particles with precision is critical to development of quantum dots usage in these fields. The CdS quantum dots, which are used in this work, are coated with a polymer shell, with a total size between 20 nm to 22 nm. The trapping and manipulation of quantum dots is promising for nanofabrication technologies that seek to place a quantum dot at a specific location in a plasmonic or nanophotonic structure. The next step in this research will be imaging of quantum dots using their fluorescence while trapping is occurring, so that a clear indication of trapping event will be available.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".