Multiplexed biosensors based on interference of surface plasmons in multimode nanoslits
Bibliographic record
Abstract
Multiplexed biosensors enable the simultaneous detection of multiple analytes within a single sample—a capability that holds significant importance in various fields, including environmental monitoring, food safety, and medical diagnostics. In medical diagnostics, detecting multiple biomarkers simultaneously is crucial for enhancing the diagnostic accuracy of conditions such as infectious diseases, cancer, and metabolic disorders. Biosensors based on surface plasmon resonance (SPR) are remarkable due to their high sensitivity compared to other technologies. However, current multiplexed SPR-based biosensors are bulky, expensive, and difficult to integrate in lab-on-a-chip configurations. Here, we propose a multiplexed biosensor as a periodic array of plasmonic biosensor unit cells, consisting of a plasmonic interferometer located on the top of the substrate, excited by a pair of grating couplers such that the surface plasmons converge to a multimode nanoslit that produces the output signal emerging through the substrate. Microfluidic channels are integrated into the structure, thereby defining the sensing regions of each interferometer. The biosensor unit cells can be monitored individually and simultaneously by imaging their output onto a camera. Absorbing shadow elements are integrated into the structure to minimize crosstalk and background light, thereby enabling excitation of the entire array by a single large monochromatic Gaussian beam. The array can be scaled lithographically, and its interrogation is scaled by increasing the size and power of the Gaussian beam and the size of the monitoring camera. We demonstrate the concept via electromagnetic simulations and predict resolutions of R b =6.3×10−6RIU and R s =10pm for bulk and surface sensing.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".