Topography Tuning for Plasmonic Color Enhancement via Picosecond Laser Bursts
Bibliographic record
Abstract
Abstract The tuning of 3D topographical features on silver for the production of plasmonic colors is reported. The topography is produced by applying closely time‐spaced laser bursts. Using laser bursts increases the Chroma of the colors produced by up to 100% compared to the nonburst coloring method. By adjusting the energy distribution of the laser pulses in a burst, while maintaining the total burst energy constant, significantly different color palettes and topographical structures are produced. Scanning electron microscope analysis of the surfaces produced reveals the creation of three distinct sets of laser‐induced periodic‐like surface structures (LIPSS): low spatial frequency LIPSS (LSFL), high spatial frequency LIPSS (HSFL), and large LIPSS that have a period about 7× that of the laser wavelength. Two‐temperature model simulations of silver irradiated by a laser burst show a significant increase in the electron–phonon coupling which is mainly responsible for the creation of LIPSS. Finite‐difference time‐domain simulations of a model of the surface, consisting of nanoparticles arranged on a sinusoidal‐modulated surface of varying amplitude (0 to 150 nm) and period (200 and 1000 nm), elucidate the importance of the HSFL and LSFL structures for color formation, including the increase in Chroma (saturation) observed experimentally.
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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".