Plasmon mediated polymerization on the surface of silver nanoparticles for advancements in photolithographic patterning
Bibliographic record
Abstract
Metal nanoparticles and their plasmon excitation have been used to enhance spectroscopic transitions and chemistry on metal nanoparticle surfaces. The size and shape of the enhancement area around the nanoparticles is dependant on the size, shape, dielectric constant of the matrix and the metal. We have recently reported on the use of plasmon excitation to induce acrylic polymerization on the surface of silver nanoparticles and have made ~10 nm polymer features far below the diffraction limit using visible LED irradiation. The acrylic polymerization takes advantage of plasmon enhanced excitation of azo photoinitiators in the vicinity of nanoparticles, causing cross-linking only in the enhancement region. The formation of a cross-linked polymer on the surface of the particles causes a solubility switch, where the regions unaffected by irradiation remain soluble and can be selectively washed away leaving behind the AgNP with a polymer coating. Plasmon excitation also generates a large local temperature gradients on the surface of nanoparticles and a measureable macroscopically amount of heat. The heat generated near the surface of particles can also be used to induce thermal processes with high spatial control. This spatial and temporal control over localized heating can also be used to initiate chemistry on the surface of particles relevant to the next generation of photolithography.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".