Deep ultra-violet plasmonics: exploiting momentum-resolved electron energy loss spectroscopy to probe germanium
Bibliographic record
Abstract
Germanium is typically used for solid-state electronics, fiber-optics, and infrared applications, due to its semiconducting behavior at optical and infrared wavelengths. In contrast, here we show that the germanium displays metallic nature and supports propagating surface plasmons in the deep ultraviolet (DUV) wavelengths, that is typically not possible to achieve with conventional plasmonic metals such as gold, silver, and aluminum. We measure the photonic band spectrum and distinguish the plasmonic excitation modes: bulk plasmons, surface plasmons, and Cherenkov radiation using a momentum-resolved electron energy loss spectroscopy. The observed spectrum is validated through the macroscopic electrodynamic electron energy loss theory and first-principles density functional theory calculations. In the DUV regime, intraband transitions of valence electrons dominate over the interband transitions, resulting in the observed highly dispersive surface plasmons. We further employ these surface plasmons in germanium to design a DUV radiation source based on the Smith-Purcell effect. Our work opens a new frontier of DUV plasmonics to enable the development of DUV devices such as metasurfaces, detectors, and light sources based on plasmonic germanium 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 | 0.001 |
| 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".