Terahertz emission enhancement of GaAs-based photoconductive antennas via the nanodecoration of their surface by means of pulsed-laser-deposition of gold nanoparticles
Bibliographic record
Abstract
We report a systematic study of free-space terahertz (THz) emission from photoconductive antennas (PCAs) nanodecorated with gold nanoparticles (Au-NPs) deposited by using the pulsed laser deposition (PLD) technique. H-shaped dipole micro-structured PCAs fabricated on semi-insulating GaAs substrates were decorated under various PLD conditions. Thus, by increasing the number of laser ablation pulses (NLP) of the Au target, both the average size of Au-NPs and the surface loading of PCAs increase. Compared with non-decorated PCAs, those decorated with Au-NPs exhibit significant enhancement in the radiated THz pulse amplitude. A maximum enhancement of ∼2.3 was achieved at NLP = 1250. Under this optimal NLP condition, not only is the average Au-NP size (of ∼15 nm) favorable for light absorption via localized surface plasmons, but also the inter-distance between NPs, the light reflectance, and facilitated transport of photocarriers, all combine to yield a stronger THz field emission. For higher NLP (≥2000), NPs coalesce and tend to form continuous film NPs, which not only significantly limits the light scattering toward the GaAs underlying substrate, but also electrically shorts the PCA. Finally, the Au-NP decoration of GaAs PCAs was also found to improve their overall thermal conductivity, making them much more thermally stable than their non-decorated counterparts.
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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) | 0.000 | 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".