Welcome to the IEEE Journal of Selected Topics in Quantum Electronics Issue on Metamaterial Photonics and Integration
Bibliographic record
Abstract
The objective of this special issue is to highlight the recent progress and trends in the development of nanostructured engineered optical materials, with an emphasis on integrated photonic structures. The novel properties found in these structures, along with ability to control these properties with unprecedented precision through advanced fabrication techniques, has opened new prospects for manipulating light at subwavelength scale. In the papers included in this Special Issue you will find some of the latest leading-edge developments in this exciting field of research and insights on how optical metamaterials can enhance the performance of integrated photonic devices. An emphasis is placed on metamaterials for opticalwaveguide technologies and on-chip architectures that can provide new degrees of freedom to control the flow of light in photonic integrated circuits. The papers cover a broad scope of metamaterial-based integrated photonic structures, ranging from subwavelength grating nanostructures in silicon photonic waveguide for applications in telecom, datacom and sensing, to various dielectric and plasmonic metasurfaces for the visible and infrared wavelength ranges, including resonant and electrically tunable structures. This issue contains 13 invited and 14 contributed papers from well-established research groups in academia and industry and prominent scientists in the field. The papers include extended overviews on developments in metamaterial-based integrated photonics and the most recent trends and topics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.046 |
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".