Grating Bio-Microelectromechanical Platform Architecture for Multiple Biomarker Detection
Bibliographic record
Abstract
A label-free biosensor based on a tunable MEMS metamaterial structure is proposed in this paper. The adopted structure is a one-dimensional array of metamaterial gratings with movable and fixed fingers. The moving unit of the optical detection system is a component of the MEMS structure, driven by the surface stress effect. Thus, these suspended optical nanoribbons can be moved and change the grating pattern by the biological bonds that happened on the modified cantilever surface. Such structural variations lead to significant changes in the optical response of the metamaterial system under illuminating angled light and subsequently shift its resonance wavelength spectrum. As a result, the proposed biosensor shows appropriate analytical characteristics, including the mechanical sensitivity of Sm = 11.55 μm/Nm−1, the optical sensitivity of So = Δλ/Δd = 0.7 translated to So = Δλ/Δσ = 8.08 μm/Nm−1, and the quality factor of Q = 102.7. Also, considering the importance of multi-biomarker detection, a specific design of the proposed topology has been introduced as an array for identifying different biomolecules. Based on the conducted modeling and analyses, the presented device poses the capability of detecting multiple biomarkers of disease at very low concentrations with proper precision in fluidic environments, offering a suitable bio-platform for lab-on-chip structures.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".