Plasmonic color generation in silver nanocrystal‐over‐mirror films by thermal embedment into a polymer spacer
Bibliographic record
Abstract
Abstract The production of colors by plasmonic nanostructures is an attractive prospect over dyes as they allow ultra‐high resolution, non‐fading colors. Typical techniques for producing plasmonic color patterns such as by electron beam or ion beam lithography are expensive, slow and not well scalable. This work demonstrates a simple, lithography‐free technique for producing plasmonic colors using a silver nanocube (AgNC) based nanoparticle‐over‐mirror (NPoM) system with thermally‐generated colors. AgNC's are deposited over a metal (Au or Ag) film with a polystyrene (PS) dielectric spacer. Upon heating the system past the glass‐transition temperature of PS, the AgNC embed into the polymer, reducing the AgNC/metal film distance. This results in a strong gap‐plasmon that can shift over 200 nm across the visible spectrum during the process. The thermal embedment of AgNC in NPoM systems is tunable across the visible range, producing wide, distinct color palettes depending on the metal film used. This technique can potentially be applied to plasmonic color‐patterning systems to produce high‐resolution microscale or nanoscale patterns over a large area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".