(Invited) Near-Field Optics and Its Applications in Nanophotonic Devices: A Review
Why this work is in the frame
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Bibliographic record
Abstract
In our presentation, we will offer an overview of aperture-type scanning near field optical microscopy (SNOM) – a family of nano-optical imaging techniques derived from scanning probe microscopy which are capable of subwavelength resolution, and the development of three dimensional (3D) SNOM methods undertaken by our group to locally image the distribution of the electromagnetic radiation in the proximity of nanoparticles and nano-objects.[1] We will discuss a few applications in which we took advantage of 3D-SNOM to design specific optical nanosystems for light harvesting device applications. Specific case studies that will be presented include the design of plasmonic thin-film solar cells enhanced by random arrays of copper nanoparticles,[2] and the use of 3D-SNOM for characterizing evanescent waveguides self-assembled from of copper nanoparticles assembled on thin films of graphene.[3] In the final part of our talk, we will we present near-field scanning thermoreflectance imaging (NeSTRI), a new pump-probe technique invented in our group (see Figure 1) in which an aperture-type SNOM is used to contactlessly determine the thermal conductivity of inhomogeneous thin films and low-dimensional systems at the nanoscale for heat-spreading and thermoelectric applications.[4,5] These examples well represent the versatility of SNOM imaging and its potential for designing an even wider family of nano-optical devices. [1] P Bazylewski, S Ezugwu, G Fanchini, Applied Sciences 7 (2017) 973 [2] S Ezugwu, H Ye, G Fanchini, Nanoscale 7 (2016) 252-260 [3] T Ouyang, A Akbari-Sharbaf, J Park, R Bauld, MG Cottam, G Fanchini, RSC Advances 5 (2015) 98814-98821 [4] S Ezugwu, S Kazemian, DYW Choi, G Fanchini, Nanoscale 9 (2017) 4097-4106 [5] S Kazemian, S Ezugwu, G Fanchini, Proc. SPIE 10926, Quantum Sensing and Nano Electronics and Photonics XVI, 109260L (1 February 2019); doi: 10.1117/12.2509828 Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it