MOEMS based integrated microfluidic fiber-optic waveguides for biophotonic applications
Bibliographic record
Abstract
Fiber-optic waveguides based Micro-Opto-Electro-Mechanical Systems (MOEMS) form a significant class of biosensors which have notable advantages like light weight, low cost and more importantly, the ability to be integrated with bio-systems. In this work, integrated microfluidic fiber-optic waveguide biosensor is presented. The phenomenon of evanescence is employed for sensing mechanism of the device. Herein, the fiber-optic waveguide is integrated with bulk micromachined fluidic channel across which different chemical and biological samples are passed through. The significant refractive index change due to the presence of biological samples that causes the evanescent field condition in the waveguides leads to optical intensity attenuation of the transmitted light. The study of the modulation in optical intensity is used to detect the properties of the species used in the evanescent region. The intensity modulation of light depends upon the geometry of the waveguide, the length of evanescent field, the optical properties of specimen used for producing evanescence and the changes in the properties by their reaction with other specimen. Therefore, this device is proposed for biosensing applications. The Finite Element Analysis (FEA) has been carried out for wave propagation under the evanescent condition for different parametric variations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".