Nonlinear charge density fluctuation driven plasmonic enhancement in Au–Ag thin films
Bibliographic record
Abstract
Abstract Bimetallic alloys of gold and silver have gained considerable attention due to their broad utility in catalysis, biomedicine, and optical technologies, owing to their enhanced surface plasmon polariton (SPP) quality factor compared to the corresponding pure metals. Earlier attempts to understand the central reason for this improved performance, despite their noncommensurate stoichiometries, have not yielded a clear picture. To address this, we performed spectroscopic ellipsometry together with Hall measurements on composition-controlled, equal-thickness Au–Ag alloy films. By examining the interdependence of plasma frequency, carrier concentration, and effective mass, we found that a nonlinear rise in carrier density is the dominant factor behind the improved optical response. To further understand this anomalous carrier density behavior, the Mayadas–Shatzkes (MS) model was applied, revealing how polycrystallinity and grain-boundary scattering influence charge-transport dynamics in the alloys. Additionally, the composition-dependent nonlinear trends in the extracted physical parameters were modeled using a bowing-type polynomial mixing approach. This combined experimental–theoretical framework not only aligns the measured results with a consistent physical model but also accounts for discrepancies based on the MS formalism. By establishing a direct link between microstructural effects, electronic properties, and optical response, this study resolves the origin of the nonlinear optical behavior observed in stoichiometric ratios of bimetallic thin films.
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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) | 0.002 | 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".