High-order harmonic generation from<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mtext>C</mml:mtext><mml:mrow><mml:mn>60</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-rich plasma
Bibliographic record
Abstract
We performed systematic investigation of high-order harmonic generation from fullerene-rich laser-produced plasmas. We studied harmonic generation by varying several experimental parameters, such as the delay between the ablation and driving pulses, and divergence and polarization of the pump laser. Enhancement of harmonic yield is observed near 20 eV, which is attributed to the influence of a broadband plasmon resonance of ${\text{C}}_{60}$ on the nonlinear optical response of fullerene-rich plasma. This increase in the harmonic intensity occurs despite the increased absorption by ${\text{C}}_{60}$ at these wavelengths. Using simulations based on time-dependent density-functional theory, we confirm that this effect is due to the influence of collective excitations. We compare harmonic generation from fullerenes using lasers with 793 nm and 396 nm wavelengths, which show the influence of plasmon resonance on the conversion efficiency of high-order harmonics for different laser wavelengths.
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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) | 0.003 | 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".