Plasmon-Mediated Drilling in Thin Metallic Nanostructures
Bibliographic record
Abstract
Thin and ultraflat conductive surfaces are of particular interest to use as substrates for tip-enhanced spectroscopy applications. Tip-enhanced spectroscopy exploits the excitation of a localized surface plasmon resonance mode at the apex of a metallized atomic force microscope tip, confining and enhancing the local electromagnetic field by several orders of magnitude. This allows for nanoscale mapping of the surface with high spatial resolution and surface sensitivity, as demonstrated when coupled to local Raman measurements. In gap-mode tip-enhanced spectroscopy, the specimen of interest is deposited onto a flat metallic surface and probed by a metallic tip, allowing for further electromagnetic confinement and subsequent enhancement. We investigate here a geometry where a gold tip is used in conjunction with a silver nanoplate, thus forming a heterometallic platform for local enhancement. When irradiated, a plasmon-mediated reaction is triggered at the tip-substrate junction due to the enhanced electric field and the transfer of hot electrons from the tip to the nanoplate. This resulting nanoscale reaction appears to be sufficient to ablate the thin silver plates even under weak laser intensity. Such an approach may be further exploited for patterning metallic nanostructures or photoinduced chemical reactions at metal surfaces.
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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".