Bibliographic record
Abstract
Static plasmonic metal–insulator–nanohole (MIN) cavities have been shown to create high chromaticity spectral colors for display applications. While on–off switching of said devices has been demonstrated, introducing active control over the spectral color of a single cavity is an ongoing challenge. Electrochromic oxides such as tungsten oxide (WO 3 ) offer the possibility to tune their refractive index (2.1–1.8) and extinction (0–0.5) upon ion insertion, allowing active control over resonance conditions for MIN based devices. In combination with the dynamic change in the WO 3 layer, the utilization of a plasmonic superstructure allows creation of well-defined spectral reflection of the nanocavity. Here, we employ inorganic, electrochromic WO 3 as the tunable dielectric in a MIN nanocavity, resulting in a theoretically achievable resonance wavelength modulation from 601 to 505 nm, while maintaining 35% of reflectance intensity. Experimental values for the spectral modulation result in a 64 nm shift of peak wavelength with high reproducibility and fast switching speed. Remarkably, the introduced device shows electrochemical stability over 100 switching cycles while most of the intercalated charge can be regained (91.1%), leading to low power consumption (5.6 mW/cm –2 ).
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 | 0.000 | 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".