Wavelength-Tunable Optical Fiber Localized Surface Plasmon Resonance Biosensor <i>via</i> a Diblock Copolymer-Templated Nanorod Monolayer
Bibliographic record
Abstract
Well-dispersed and dense layers of gold nanorods (AuNRs) on optical fibers are shown to regulate the longitudinal peak wavelength and enhance the sensing performances of localized surface plasmon resonance (LSPR) biosensors. A simple self-assembly method relying on a brush-like monolayer of poly(styrene)- b -poly(acrylic acid) (PS- b -PAA) diblock copolymer was used to immobilize AuNRs with various aspect ratios from 2.33 to 4.60 on optical fibers. Both the experimental and simulation results illustrated that the particle aspect ratio, deposition time (related to the coverage of AuNRs), and interparticle gap significantly affected the optical properties of the fiber-based LSPR biosensors. The highest refractive index (RI) sensitivity of the sensor was 753 nm/RIU, while the limit of detection for human IgG was as low as 0.8 nM. Compared with standard nanoparticle deposition methods of polyelectrolytes or alkoxysilanes, the RI sensitivity of the PS- b -PAA dip-coating method was approximately 3-fold better, a consequence of the higher particle coverage and fewer AuNR aggregates. The presented AuNR-based LSPR sensors could regulate the detection range by tuning the aspect ratios of AuNRs. Applicability is demonstrated via quantitative analysis of antigen–antibody interactions, DNA sensing, and surface-enhanced Raman scattering.
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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 | 0.000 |
| 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 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".