Ultrafast dynamics of nanoplasmonic stopped-light lasing
Bibliographic record
Abstract
We study the spatio-temporal dynamics of coherent amplification and lasing in planar gain-enhanced nanoplasmonic structures and show that a singularity in the density of optical states leads to a stopped-light feedback mechanism that allows for cavity-free photonic and surface-plasmon polariton nanolasing. We reveal that in the absence of cavity-induced feedback a phase-locked superposition of a quasi dispersion-free waveguide mode promotes the dynamic formation of a subwavelength lasing mode. Simulations on the basis of a full-time domain Maxwell-Bloch Langevin approach uncover a high spontaneous emission factor β≈0.9 and demonstrate that the stopped-light lasing/spasing mechanism is remarkably robust against interface roughness. Stopped-light surface-plasmon polariton lasing is shown to be stable for gain sections of a width of down to 200 nm but in wider gain structures of the order of 1 μm the dynamics is characterised by spatio-temporally oscillating lasing surface-plasmon polaritons with typical temporal and spatial periods of smaller than 5 fs and smaller than 100 nm. Stopped-light lasing thus provides opportunities for ultrafast nanolasing and the realization of ultra-thin lasing surfaces and offers a new route to ultrafast spasing and cavity-free active quantum plasmonics.
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".