Carving Plasmon Modes in Silver Sierpiński Fractals
Bibliographic record
Abstract
The surface plasmon resonance (SPR) modes of the first three generations of a Sierpiński fractal triangle are investigated using electron energy loss spectroscopy (EELS) complemented with finite difference time domain simulations. The Sierpiński fractal geometry is created in a subtractive manner, by carving triangular apertures into the triangular prism of the previous fractal generation. The ability of the fractal antenna to efficiently utilize space in coupling to long wavelength excitations is confirmed on the single nanostructure level via redshifting of the primary dipole mode as the fractal generation is increased. Through application of the Babinet principle, it is demonstrated that this spectral shift is caused by coupling of two degenerate orthogonal dipolar modes of a single triangle with two degenerate orthogonal dipole modes of the triangular aperture occupying the center of the first generation fractal. It is also shown that the spectral position and strength of the dipole mode can be tuned by altering the size of the central aperture, and thus the capacitance of the equivalent circuit, and the width of the conductive channels joining different fractal building blocks, thereby altering the circuit inductance. Importantly, placing the aperture on a node of the SPR mode causes a shift in energy of this mode without changing the charge configuration; placing the aperture on an antinode of the SPR mode causes no shift in energy, but changes the field configuration, as revealed through EELS measurements. These fractal-specific properties provide new strategies to design, predict, and effectively exploit highly tunable SPR modes using simple building blocks.
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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) | 0.000 | 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".