(Invited) Near-Field Optics and Its Applications in Nanoscale Materials: A Review
Bibliographic record
Abstract
In this paper, we first offer an overview of aperture-type scanning near field optical microscopy –a family of super-resolution imaging techniques based on evanescent waves, which can be combined with atomic force microscopy and are capable of subwavelength resolution nano-optical imaging. In the second part of this review, we will discuss a few applications in which our group capitalized on the super-resolution resolving power of SNOM to design specific nano-optical and nano-photonic systems for light harvesting, resistive memory device applications and nanoscale thermo-optical management. Specific case studies that will be presented include the characterization of weakly photoluminescent and curved carbon dots for memory device applications, the three-dimensional characterization of plasmon-enhanced nanophotonic devices, as well as the development of near-field thermoreflectance imaging for nanophotonic-based thermal management applications. Collectively, our study well represents the versability of SNOM as a unique super-resolution nanophotonic tool for the investigation of light-matter interaction at the nanoscale.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".