Flow force augmented 3D suspended polymeric microfluidic (SPMF<sup>3</sup>) platform
Bibliographic record
Abstract
Detection and study of bioelements using microfluidic systems has been of great interest in the biodiagnostics field. Microcantilevers are the most used systems in biodetection due to their implementation simplicity which have been used for a wide variety of applications ranging from cellular to molecular diagnosis. However, increasing further the sensitivity of the microcantilever systems have a great effect on the cantilever based sensing for chemical and bio applications. In order to improve further the performance of microcantilevers, a flow force augmented 3D suspended microchannel is proposed using which microparticles can be conveyed through a microchannel inside the microcantilever to the detection area. This innovative microchannel design addresses the low sensitivity issue by increasing its sensitivity up to 5 times than the earlier reported similar microsystems. Moreover, fabricating this microsystem out of Polydimethylsiloxane (PDMS) would eliminate external exciter dependency in many detection applications such as biodiagnostics. In this study, the designed microsystem has been analyzed theoretically, simulated and tested. Moreover, the microsystem has been fabricated and tested under different conditions, the results of which have been compared with simulation results. Finally, its innovative fabrication process and issues are reported and discussed.
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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 | 0.001 | 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".