A plasmonic optical fiber patterned by template transfer as a high-performance flexible nanoprobe for real-time biosensing
Bibliographic record
Abstract
Surface plasmon resonance (SPR) on metal nanostructures offers a promising route for manipulation and interrogation of light in the subwavelength regime. However, the utility of SPR structures is largely limited by currently used complex nanofabrication methods and relatively sophisticated optical components. Here to relieve these restrictions, plasmonic optical fibers are constructed by transferring periodic metal nanostructures from patterned templates onto endfaces of optical fibers using an epoxy adhesive. Patterned metal structures are generally extended from two-dimensional (2D) nanohole arrays to one-dimensional (1D) nanoslit arrays. By controlling the viscosity of the adhesive layer, diverse surface topographies of metal structures are realized with the same template. We design a special plasmonic fiber that simultaneously implements multimode refractive index sensing (transmission and reflection) with remarkably narrow linewidth (6.6 nm) and high figure of merit (60.7), which are both among the best reported values for SPR sensors. We further demonstrate a real-time immunoassay relying on our plasmonic fiber integrated with a special flow cell. Plasmonic optical fibers also take advantages of excellent stability during fiber bending and capability of spectrum filtering. These features enable our plasmonic fibers to open up an alternative avenue for the general community in biosensing and nanoplasmonics.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".