A Mass-Producible and Versatile Sensing System: Localized Surface Plasmon Resonance Excited by Individual Waveguide Modes
Bibliographic record
Abstract
A plasmonic sensing system that allows the excitation of localized surface plasmon resonance (LSPR) by individual waveguide modes is presented conceptually and experimentally. Any change in the local environment of the gold nanoparticles (AuNPs) alters the degree of coupling between LSPR and a polymer slab waveguide, which then modulates the transmission-output signal. In comparison to conventional LSPR sensors, this system is less susceptible to optical noise and positional variation of signals. Moreover, it enables more freedom in the exploitation of plasmonic hot spots with both transverse electric (TE) and transverse magnetic (TM) modes. Through real-time measurement, it is demonstrated that the current sensing system is more sensitive than comparable optical fiber plasmonic sensors. The highest normalized bulk sensitivity (7.744 RIU –1 ) is found in the TM 1 mode. Biosensing with the biotin–streptavidin system shows that the detection limit is on the order of 10 –14 M of streptavidin. With further optimization, this sensing system can easily be mass-produced and incorporated into high throughput screening devices, detecting a variety of chemical and biological analytes via immobilization of the appropriate recognition sites.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".