Are plasmonic optical biosensors ready for use in point-of-need applications?
Bibliographic record
Abstract
Plasmonics has drawn significant attention in the area of biosensors for decades due to the unique optical properties of plasmonic resonant nanostructures. While the sensitivity and specificity of molecular detection relies significantly on the resonance conditions, significant attention has been dedicated to the design, fabrication, and optimization of plasmonic substrates. The adequate choice of materials, structures, and functionality goes hand in hand with a fundamental understanding of plasmonics to enable the development of practical biosensors that can be deployed in real life situations. Here we provide a brief review of plasmonic biosensors detailing most recent developments and applications. Besides metals, novel plasmonic materials such as graphene are highlighted. Sensors based on Surface Plasmon Resonance (SPR), Localized Surface Plasmon Resonance (LSPR), and Surface Enhanced Raman Spectroscopy (SERS) are presented and classified based on their materials and structure. In addition, most recent applications to environment monitoring, health diagnosis, and food safety are presented. Potential problems related to the implementation in such applications are discussed and an outlook is presented.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".