Embedded Metal Oxide Plasmonics Using Local Plasma Oxidation of AZO for Planar Metasurfaces
Bibliographic record
Abstract
New methods for achieving high-quality conducting oxide metasurfaces are of great importance for a range of emerging applications from infrared thermal control coatings to epsilon-near-zero nonlinear optics. This work demonstrates the viability of plasma patterning as a technique to selectively and locally modulate the carrier density in planar Al-doped ZnO (AZO) metasurfaces without any associated topographical surface profile. This technique stands in strong contrast to conventional physical patterning which results in nonplanar textured surfaces. The approach can open up a new route to form novel photonic devices with planar metasurfaces, for example, antireflective coatings and multi-layer devices. To demonstrate the performance of the carrier-modulated AZO metasurfaces, two types of devices are realized using the demonstrated plasma patterning. A metasurface optical solar reflector is shown to produce infrared emissivity equivalent to a conventional etched design. Second, a multiband metasurface is achieved by integrating a Au visible-range metasurface on top of the planar AZO infrared metasurface. Independent control of spectral bands without significant cross-talk between infrared and visible functionalities is achieved. Local carrier tuning of conducting oxide films offers a conceptually new approach for oxide-based photonics and nanoelectronics and opens up new routes for integrated planar metasurfaces in optical technology.
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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 | 0.000 |
| 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".