High-sensitivity nanometamaterial near-infrared biosensor for label-free early cancer detection via exosomal biomarkers
Bibliographic record
Abstract
This study presents a novel, to the best of our knowledge, ultra-wideband nanobiosensor based on a double-negative (DNG) metamaterial perfect absorber for early cancer detection through exosomal biomarker analysis. Our biosensor operates across a broad frequency range from 70 THz to 3 PHz, exhibiting near-unity absorption, i.e., exceeding 99%, and angular and polarization insensitivity, i.e., providing polarization-independent absorption across the full spectrum of polarization angles (0° to 90°), ensuring stable performance under both transverse electric (TE) and transverse magnetic (TM) polarized waves. Of particular interest is its performance in the near-infrared (NIR) region (70–400 THz), where the sensor’s DNG characteristics manifest through simultaneously negative permittivity and permeability, enhancing field confinement and sensitivity. This spectral window is especially conducive to label-free, non-invasive detection of circulating exosomes, critical indicators of early stage oncogenesis. The sensor is constructed using a tri-layer metal–insulator–metal (MIM) architecture comprising nickel (Ni) layers and a silicon dioxide (SiO 2 ) dielectric spacer. The design leverages the plasmonic and thermal stability properties of Ni and the low optical attenuation of SiO 2 to achieve optimal absorption and structural robustness. Electromagnetic simulations demonstrate strong electric and magnetic resonances, producing significant near-field enhancements. These improve the detection of subtle dielectric changes associated with exosomal binding events. The sensor maintains high absorption efficiency across oblique incidence angles and various polarization states, making it suitable for real-world biomedical diagnostic applications. By focusing on the NIR regime where tissue transparency and molecular vibrational modes intersect, the proposed biosensor enables the discrimination between cancer-derived exosomes and their normal counterparts, as confirmed through spectral and field distribution analyses. The demonstrated performance highlights the sensor’s promise for next-generation photonic platforms targeting early cancer diagnostics, with potential extension to environmental monitoring and energy harvesting technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".