Optical modulation using strain tunable metallo‐dielectric films
Bibliographic record
Abstract
Significant scientific and technological advances in photonics have facilitated the development of plasmonic and metallo‐dielectric devices that rely on metal thin‐films. Metal based devices, however, are limited by the inability to actively tune the optical properties of metals. Recent work has demonstrated the ability to highly strain metal thin‐films on elastomeric substrates without losing electrical continuity in the film. Herein, we examine the optical properties of metal thin‐films deposited on elastomeric substrates under strain. We demonstrate that reversible modulation of the mechanical structure of the pliable metallo‐dielectric film results in a combination of scattering and surface plasmon excitations, that alter the film transmittance and reflectance. Further, it is shown that control over the wavelength of plasmonic resonances of the film can be achieved through variation in metal film thickness and modification of the local refractive index. The ability to actuate the plasmonic resonances of the metal thin‐films using strain represent a novel method of actively tuning the properties of metal thin‐films and will help facilitate the development of strain‐tunable plasmonic and metallo‐dielectric devices.
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".