New opportunities in electromagnetics with nanotechnologies
Bibliographic record
Abstract
Electromagnetics is a mature field which is ubiquitous in everyday life. All communication systems rely, to a certain extent, on electromagnetic devices such as antennas and waveguides. Furthermore, the ever increasing demand for high capacity and small size systems calls for novel avenues for miniaturization and integration. Conventional electromagnetic devices suffer from a fundamental tradeoff between size and functionality, originating from the fact that the interaction between all classical materials and electromagnetic waves reduces as their size decreases. Therefore, new materials immune of these fundamental drawbacks must be explored. Nanotechnology, the study of materials and structures with at least one of dimension in the order of the nanometer [1], offers a potential solution. Conceptualized for the first time in 1956 by Richard Feynman in his famous talk “There's plenty of room at the bottom” [2], nanotechnology can now routinely produce nanomaterials with controlled structural and functional parameters in the nano or even atomic scale. Some of these nanomaterials are not only able to strongly interact with electromagnetic waves, despite their dimensions being much smaller than the wavelength, but they also exhibit totally new phenomena, not found in conventional materials. Here, we provide three illustrative examples from our recent work on nanoelectromagnetics, which show how the combination of electromagnetics and nanotechnology can lead to devices with unprecedented characteristics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".