Silver Nanoparticles on a Plastic Platform for Localized Surface Plasmon Resonance Biosensing
Why this work is in the frame
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Bibliographic record
Abstract
A cost-effective fabrication method for the preparation of localized surface plasmon resonance (LSPR) biosensors supported on plastics is described. The silver-nanoparticles-on-plastic sensor (SNOPS) was fabricated by chemically modifying the surface of a common plastic, polyethylene terephthalate (PET) to allow the efficient immobilization of Ag NPs. The LSPR of the SNOPS strip showed good sample-to-sample reproducibility. The analytical performance of the sensor strips for monitoring both thiol and protein adsorption, including bioaffinity, was examined. The limit of quantification to the adsorption of 11-mercaptoundecanoic acid was 500 nM and for the detection of streptavidin was approximately 9.5 nM. SNOPS can then be used as a cheap, versatile, and yet sensitive LSPR biosensor.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 | 0.002 |
| 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 it