Flexible, fibre-addressable surface-plasmon-resonance chip
Bibliographic record
Abstract
Surface plasmon resonance (SPR) sensors exploit optical coupling to surface plasmons, light waves bound to a metal surface. In the most common configuration, a SPR sensor is used with an external light source, optical components to polarize incident light and guide light to and from a metal surface, a coupling device to convert free-space light into surface plasmons and back into free-space light, and a light detector. The light source, the optical components, and the light detector are external to the SPR device, and the coupling structure is often integrated directly with the surface-plasmon-sustaining metal surface. The requirement of several external components restricts the miniaturization of SPR devices and prohibits low-cost implementation. To address these limitations, we design, fabricate, and test a new SPR device chip that is fibre-addressable, does not require a discrete coupling structure, and integrates light delivery, light polarization control, surface plasmon coupling onto a thin, flexible substrate. Our SPR chip is constructed from a thin gold layer deposited on top of a clear plastic sheet, which is then optically connected from the bottom surface onto a plastic linear polarizer sheet. Two cleaved fibres, one to input light and the other to collect reflected light, are then optically attached to SPR device. We experimentally characterize the SPR device and find good agreement between our measurements and a theoretical model based on transfer matrix formalism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".