Surface-Plasmon Properties of Noble Metals with Exotic Phases
Bibliographic record
Abstract
Noble-metal nanoparticles have been the industry standard for plasmonic applications due to their highly populated plasmon generations. Despite their remarkable plasmonic performance, their widespread use in plasmonic applications is commonly hindered due to limitations on the available laser sources and relatively low operating temperatures needed to retain mechanical strength in these materials. Motivated by recent experimental works, in which exotic hexagonal-closed-packed (HCP) phases have been identified in gold (Au), silver (Ag), and copper (Cu), we present the plasmonic performance of two HCP polytypes in these materials using high-accuracy first-principles simulations. The isolated HCP phases commonly reach thermal and mechanical stability at high temperatures due to monotonically decreasing Gibbs free-energy differences compared to face-centered cubic (FCC) phases. We find that several of these polytypes are harder and produce bulk plasmons at lower energies with comparable lifetimes than their conventional FCC counterparts. It also leads to the localized surface-plasmon resonance (LSPR) in perfectly spherical HCP-phased nanoparticles, embedded onto dielectric matrices, at substantially lower energies with comparable lifetimes to their FCC counterparts. LSPR peak locations and lifetimes can be tuned by controlling the operational temperature, the dielectric permittivity of the hosting matrix, and the grain size. Our work suggests that noble-metal nanoparticles can be tailored to develop exotic HCP phases to obtain novel plasmonic properties.
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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".