Selective modification of nanoparticle arrays by laser-induced self assembly (MONA-LISA): putting control into bottom-up plasmonic nanostructuring
Bibliographic record
Abstract
Nano-structuring of metals is one of the greatest challenges for the future of plasmonic and photonic devices. Such a technological challenge calls for the development of ultra-fast, high-throughput and low-cost fabrication techniques. Laser processing, accounts for the aforementioned properties, representing an unrivalled tool towards the anticipated arrival of modules based in metallic nanostructures, with an extra advantage: the ease of scalability. In the present work we take advantage of the ability to tune the laser wavelength to either match the absorption spectral profile of the metal or to be resonant with the plasma oscillation frequency, and demonstrate the utilization of different optical absorption mechanisms that are size-selective and enable the fabrication of pre-determined patterns of metal nanostructures. Thus, we overcome the greatest challenge of Laser Induced Self Assembly by combining simultaneously large-scale character with atomic-scale precision. The proposed process can serve as a platform that will stimulate further progress towards the engineering of plasmonic devices.
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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".