Generation of fast photoelectrons in strong‐field emission from metal nanoparticles
Bibliographic record
Abstract
Abstract We investigated the generation and control of fast photoelectrons (PEs) by exposing plasmonic nanoparticles (NPs) to short infrared (IR) laser pulses with peak intensities between 10 12 and 3 × 10 13 W/cm 2 . Our measured and numerically simulated PE momentum distributions demonstrate the extent to which PE yields and cutoff energies are controlled by the NP size, material, and laser peak intensity. For strong‐field photoemission from spherical silver, gold, and platinum NPs with diameters between 10 and 100 nm our results confirm and surpass extremely high PEs cutoff energies, up to several hundred times the incident laser‐pulse ponderomotive energy, found recently for gold nanospheres [Saydanzad et al., Nanophotonics 12 , 1931 (2023)]. As reported previously for dielectric NPs [Rupp et al., J. Mod. Opt. 64 , 995 (2017)], at higher intensities the cutoff energies we deduce from measured and simulated PE spectra tend to converge to a metal‐independent limit. We expect these characteristics of light‐induced electron emission from prototypical plasmonic metallic nanospheres to promote the understanding of the electronic dynamics in more complex plasmonic nanostructures and the design of nanoscale light‐controlled plasmonic electron sources for photoelectronic devices of applied interest.
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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".