Harmonic Generation up to Fifth Order from Al/Au/CuS Nanoparticle Films
Bibliographic record
Abstract
Dual heterostructures integrating noble-metal and copper chalcogenide nanoparticles have attracted a great deal of attention in nonlinear optics, because coupling of their localized surface plasmon resonances (LSPRs) substantially enhances light-matter interactions through local-field effects. Previously, enhanced cascaded third-harmonic generation was demonstrated in Au/CuS heterostructures mediated by harmonically coupled surface plasmon resonances. This suggests a promising approach for extending nonlinear enhancement to higher harmonics by adding an additional nanoparticulate material with higher-frequency harmonic resonances to the hybrid films. Here we report the first observation of enhanced cascaded fourth- and fifth-harmonic generation in Al/Au/CuS driven by coupled LSPRs at the fundamental (1050 nm), second harmonic (525 nm), and third harmonic (350 nm) of the pump frequency. An analytical model based on incoherent dipole-dipole interactions among plasmonic nanoparticles accounts for the observed enhancements. The results suggest a novel design for efficiently generating higher harmonics in resonant plasmonic structures by means of multiple sum-frequency cascades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".