Plasmonic Stamps Fabricated by Gold Dewetting on PDMS for Catalyzing Hydrosilylation on Silicon Surfaces
Bibliographic record
Abstract
In this work plasmonic stamps are harnessed to drive surface chemistry on silicon. The plasmonic stamps were prepared by sputtering gold films on PDMS, followed by thermal annealing to dewet the gold and form gold nanoparticles. By changing the film thickness of the sputtered gold, the approximate size and shape of these gold nanoparticles can be changed, leading to a shift of the optical absorbance maximum of the plasmonic stamp, from 535 nm to 625 nm. Applying the plasmonic stamp to a Si(111)-H surface using 1-dodecene as the ink, illumination with green light results in covalent attachment of 1-dodecyl groups to the surface. Of the dewetted gold films on PDMS used to make the plasmonic stamps, the thinnest three (5.0, 7.0, 9.2 nm) resulted in the most effective plasmonic stamps for hydrosilylation. The thicker stamps had lower efficacy due to the increased fraction of non-spherical particles, which have lower-energy LSPRs that are not excited by green light. Since the electric field generated by the LSPR should be very local, hydrosilylation on the silicon surface should only take place within close proximity of the gold particles on the plasmonic stamps.To complement AFM imaging of the hydrosilylated silicon surfaces, galvanic displacement of gold(III) salts on the silicon was carried out and the samples imaged by SEM - the domains of hydrosilylated alkyl chains would be expected to block the deposition of gold. The bright areas of metallic gold surround dark spots, with the sizes and spacing of these dark spots increasing with the size of the gold particles on the plasmonic stamps. These results underline the central role played by the LSPR in driving the hydrosilylation on silicon surfaces, mediated with plasmonic stamps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".