A nanorod polymer micro-array formed by micro-contact printing
Bibliographic record
Abstract
In previous work, we demonstrate a simple approach to creating a plasmonic polymer. Reflecting upon the need for greater spot density while still maintaining the objective of low cost analysis, the next generation of device is described where density up to 24000 sensing spots is achievable. A localized surface plasmon micro-array is described formed by single or multiple deposition of a nanorod plasmonic polymer by micro-contact printing. The structure of the polymer can be made micro-porous and thickness can be controlled by a cyclical deposition and rapid heat cure protocol. The consistency of feature deposition is assessed. The resulting micro-structure provides a large surface area for immobilization of biomolecules for assay development. Dark-field analysis of the polymer demonstrates complex microstructure and intense Mie Scattering as expected from gold nanorods. Using fluorescence confocal analysis images of the polymer demonstrates two independent photo-luminescent emission spectra. The two independent emission spectra are linked to the positions of the localized surface plasmons of the nanorods, using a pump source of 543nm excites the transverse plasmon (peak at 550nm)and it's commensurate emission, but doesn't excite the longer emission around 700nm that is linked to the longitudinal Plasmon around 737nm. The different emissions are demonstrated in the illumination of different portions of the polymer matrix under each pump source excitation. The potential for multiple spectroscopic biosensor analysis is discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".