Detection of eccentricity in silver nanotubes by means of induced optical forces and torques
Bibliographic record
Abstract
In previous works (Abraham et al 2011 Plasmonics 6 435 ; Abraham Ekeroth and Lester 2012 Plasmonics 7 579 ; Abraham Ekeroth and Lester 2013 Plasmonics 8 1417 ; Abraham Ekeroth R M and Lester M 2015 Plasmonics 10 989–98 ), we have conducted an exhaustive study about optical properties of metallic realistic two-dimensional (2D) nanotubes, using an experimental-interpolated dielectric function (Palik 1985 Handbook of Optical Constants of Solids (Toronto: Academic Press)). In the case of non-homogeneous metallic shells, we suggested (in a theoretical form) a procedure to detect the non-uniformity of shells in parallel, disperse and randomly oriented long nanotubes (2D system). This detection is based exclusively on the plasmonic properties of the response (Abraham Ekeroth and Lester 2012 Plasmonics 7 579 ). Here we consider exact calculations of forces and torques, exerted by light on these kinds of nanostructures, illustrating the mechanical effects of plasmonic excitations with one example of silver shell under p -polarized incidence. This study continues with the methodology implemented in the previous paper (Abraham Ekeroth R M and Lester M 2015 Plasmonics 10 989–98 ), for homogeneous nanotubes. The features of the electromagnetic interaction in these structures, from the point of view of mechanical magnitudes, make it possible to conceive new possible interesting applications. Particularly, we point out some results regarding detection of eccentricity in nanotubes in vacuum (when Brownian movement is not taken into account). We interpret the optical response of the realistic shells in the framework of plasmon hybridization model (PHM), which is deduced from a quasi-static approximation. Our integral formalism provides for retardation effects and possible errors is only due to its numerical implementation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".