Harnessing the Unique Nature of Evanescent Waves: Optimizing FOEW LSPR Sensors with Absorption-Focused Nanoparticle Design
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Bibliographic record
Abstract
This work presents a novel and comprehensive framework for optimizing fiber optic evanescent wave (FOEW) localized surface plasmon resonance (LSPR) sensors by investigating the unique interaction between evanescent waves and plasmonic nanoparticles. Unlike propagating light, the evanescent wave is a localized, non-propagating field that interacts exclusively with absorbing media near the fiber surface. This characteristic highlights the importance of prioritizing nanoparticle absorption over total extinction in FOEW sensor design. The optical response of silver nanoparticles was modeled across a size range of 10–100 nm, showing that absorption increases with particle number. Among the sizes tested, 30 nm silver nanoparticles exhibited the highest absorption efficiency, which was confirmed experimentally. An analytical adsorption kinetics model based on diffusion transport further predicted that smaller nanoparticles yield higher surface coverage, a result validated through atomic force microscopy (AFM) and scanning electron microscopy (SEM) imaging. Refractive index (RI) sensitivity tests conducted on sensors fabricated with 10 nm, 20 nm, and 30 nm silver nanoparticles revealed that while smaller nanoparticles produced higher initial absorption due to greater surface density, the 30 nm particles ultimately provided superior RI sensitivity due to their enhanced absorption efficiency. These findings underscore the significance of absorption-centered nanoparticle design in maximizing FOEW LSPR sensor performance.
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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 | 0.000 |
| 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) | 0.000 | 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