An Ultrasensitive MOEMS Platform Relied on an Adjustable Defect Width for Label-Free Bioparticle Detection
Bibliographic record
Abstract
This article presents a novel optomechanical surface stress-based biosensor. The proposed device employs a functionalized microelectromechanical systems (MEMS) transducer to capture the target bioparticles in the sample. The interaction of the bioreceptors immobilized on the transducer surface with the target bioparticles modifies the transducer’s surface stress, which results in its deformation. The accurate measurement of the transducer deformation allows for label-free detection of the target bioparticles and their concentration. In order to measure the transducer deformation, an optical system consisting of a defective 1-D photonic crystal with adjustable defect width is utilized, which significantly enhances the sensor sensitivity. This BioMEMS platform exhibits improved functional characteristics, including a bandwidth (BW) of 5 kHz, full-width at half-maximum (FWHM) of 0.8 nm, quality factor of 1822.5, mechanical sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.5~\mu $ </tex-math></inline-formula>m/Nm<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}^{-{1}}$ </tex-math></inline-formula>, optical sensitivity of 1.02, and total sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.775~\mu $ </tex-math></inline-formula>m/Nm<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}^{-{1}}$ </tex-math></inline-formula>. These functional characteristics demonstrate the capability of the proposed sensor to detect various diseases in their early stages or progression, which is highly desirable in point-of-care testing (POCT) applications.
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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".