3D-Printed Photocathodes for Resonant, Terahertz-Field-Driven Ultrafast Electron Emission
Bibliographic record
Abstract
Ultrashort photoemitted electron bunches can provide high electron currents within sub-picosecond timeframes, enabling time-resolved investigations of ultrafast physical processes with nanoscale resolution. Non-resonant conductive nanotips are typically employed to realize nanoscale photoelectron sources with high brightness. However, such emitters require complex non-scalable fabrication procedures featuring poor reproducibility. Planar resonant antennas fabricated via photolithography have been recently investigated, also because of their superior field enhancement properties. Nevertheless, the electron emission from these structures is parallel to the substrate plane, which limits their practical use as electron sources. In this work, we present an innovative out-of-plane, resonant nanoantenna design for field-driven photoemission enabled by high-resolution 3D printing. Numerical and experimental evidences demonstrate that gold-coated, terahertz resonant nanocones provide large local electric fields at their apex, automatically ensuring out-of-plane coherent electron emission and acceleration. We show that the resonant structures can be conveniently arranged in an array form, for a further significant electron extraction enhancement via a collective terahertz response. Remarkably, such collective behaviour can also be harvested to boost photoemission from an individual nano-source. Our approach opens the path for a new generation of photocathodes that can be reproducibly fabricated and designed at will, significantly relaxing the requirement for intense terahertz drivers.
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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 | 0.001 |
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".