A new polydimethylsiloxane (PDMS) microcantilever with integrated optical waveguide for biosensing application
Bibliographic record
Abstract
This paper reports a novel biosensor monolithically integrate optical waveguide into PDMS microcantilever. The sensor consists of buried optical fibers, integrated optical waveguide and horizontal PDMS microcantilever suspended into microfluidic channel. The thin PDMS layer involves microcantilever, microfluidic cannels and optical channels fabricated using soft lithography technique. The thin layer is covered by semi-bonding of a glass slide and a PDMS layer to enable introducing the material of waveguide core into the waveguide channel embedded into PDMS microcantilever. The covering layers are then replaced by other PDMS layers which have hollow features to release the microcantilever for free deflection and to seal microfluidic network. The input and output multimode fibers are horizontally inserted into the optical channels. The light received at the input fiber is conducted through the optical waveguide microcantilever and is delivered to the output fiber. Numerical model is presented to simulate the optical performance of the optical waveguide PDMS microcantilever under fluid flow testing and to find the proper dimensions and waveguide material. The deflection of microcantilever under flow loading distorts the light and causes power loss at the output fiber. COMSOL Multiphysics 3.5 is used to perform fluid structure interaction analysis to assess the cantilever defection due to fluid flow and the optical simulation to estimate the power loss due to cantilever deflection. The proposed biosensor can be used to measure the force within the range of living cell growth force and to be integrated within bio-sensing microdevices to carefully measure the fluid flow rate.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".