Plasmonic coloring of noble metals rendered by picosecond laser exposure
Bibliographic record
Abstract
We show the angle-independent coloring of metals in air arising from nanoparticle distributions on metal surfaces created via picosecond laser processing. Each of the colors is linked to a unique total accumulated fluence, rendering the process compatible with industry. We report the coating of the colored metal surfaces using atomic layer deposition which is shown to preserve colors and provide mechanical and chemical protection Laser bursts are composed of closely time-spaced pulses separated by 12.8 ns. The coloring of silver using burst versus non-burst is shown to increase the Chroma, or color saturation, by 50% and broaden the color Lightness range by up to 60%. The increase in Chroma and Lightness are accompanied by the creation of 3 kinds of different laser-induced periodic surface structures (LIPSS). One of these structures is measured to be 10 times the wavelength of light and are not yet explained by conventional theories. Two temperature model simulations of laser bursts interacting with the metal surface show a significant increase in the electron-phonon coupling responsible for the well-defined LIPSS observed on the surface of silver. Finite-difference time-domain simulations of nanoparticles distributed on the high-spatial frequency LIPSS (HSFL) explain the increase in color saturation (i.e. Chroma of the colors) by the enhanced absorption and enriched plasmon resonances.
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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".