Tunable Plasmonic Colours by Atomic Layer Deposition of Alumina
Bibliographic record
Abstract
We report the tuning of plasmonic colours on silver by controlling the thickness of alumina films deposited via atomic layer deposition. The colours are observed to shift with increasing alumina film thickness. Colour palettes produced with periodic topographical features are observed to recover their original vibrancy and Hue range after the deposition of a film thickness of ~ 60 nm while colours devoid of such topographical features are observed to gradually fade and their colour intensities are never recovered collapsing into a small visually unappealing region of the LCH color space. Analysis of the surfaces identifies the periodic topographical features as responsible for this behavior. Finite-difference time-domain simulations of flat and sine-modulated surfaces covered with nanoparticles and covered by a conformal alumina film were conducted to unravel the role played by the ALD thickness on the colour formation, where colour rotations and recovery were also observed. The coloured surfaces were evaluated for applications in colourimetric and radiometric sensing showing large sensitivities of up to 3.06/nm and 3.19 nm/nm, respectively. The colourimetric and radiometric sensitivities are ob-served to be colour dependent.
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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".