Low-voltage dielectrophoretic platform for Lab-on-chip biosensing applications
Bibliographic record
Abstract
We propose in this paper a platform for bioparticles mixing and detection in Lab-on-chip (LoC) based device dedicated to neurotransmitters analysis. The proposed biosensing device is characterized by a low voltage dielectrophoretic separation using novel L-shape electrodes. In addition to the mixing architecture enabling reaction between different particles, liquid was also sampled using the same electrodes. Our design works with different low voltages depending on particle size. It is tested with microspheres in the range of micrometers with an applied voltage less than 5V. The system dimensions ar e reduced to the minimum size such as it needs only few picolitre liquid samples. In addition to have a better control and separation, it is crucial to design many in-channel electrodes with minimum dimensions. Thus the microchannel includes 32 L-shape electrodes. The width of each electrode is 10 μm separated by 10 μm. Consequently, the width of the microchannel is 650 μm. The number of electrodes was choosen based on the number of outputs available on the monitoring electrical circuit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".