Fano Resonance Sensor Based on Microhole Array Waveguides for Ultrasensitive Label-Free Biosensing
Bibliographic record
Abstract
Enhancing the sensitivity of surface plasmon resonance (SPR) sensors is of paramount importance for the detection of trace biomolecules. In this study, we innovatively developed a Fano resonance biosensing platform based on photon–plasmon coupling enhancement. The sensor consists of a poly(methyl methacrylate) (PMMA) microhole array waveguide, an MY-131-MC (MY) dielectric layer, and a silver-based plasmonic layer. This structural design enables the precise modulation of the spatial coupling between the localized photonic field and the microhole membrane, allowing the electromagnetic field of the probe light to effectively overlap with the microhole array waveguide. Furthermore, the three-dimensional interconnected microstructure enhances the light–matter interaction strength. Experimental results demonstrate that the sensor achieves an ultrahigh sensitivity of 56.24 μm/RIU in refractive index (RI) detection, representing an 11.4-fold improvement over traditional Fano-type sensors (4.9 μm/RIU). The figure of merit (FOM) is elevated to 2998.94 RIU –1, surpassing conventional SPR sensors by 2 orders of magnitude. The platform was employed to detect the tumor biomarker carcinoembryonic antigen (CEA), achieving a measurement sensitivity of 4.03 nm/(ng/mL) and a limit of detection (LOD) of 81.8 pg/mL. Additionally, the proposed method exhibits excellent selectivity, repeatability, and stability. This simple and cost-effective approach provides a novel strategy for developing high-performance SPR sensors for biosensing applications.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".