Modeling of Dielectrophoresis for Myoglobin Molecules in a Microchannel With Parallel Electrodes
Bibliographic record
Abstract
Myoglobin is one of the important cardiac markers, whose concentration increases from 90 pg/ml to over 5000 pg/ml in the blood serum of heart attack patients. Separation and detection of myoglobin play a vital role in deciding the cardiac arrest in advance, which is the challenging part of ongoing research. In the present study, one of the electrokinetic approach i.e., dielectrophoresis (DEP) is chosen to manipulate the myoglobin molecule in aqueous solution. A generalized theoretical expression is developed for the dielectrophoretic force acting on an arbitrary shape of the particle. Dielectric myoglobin model is developed by approximating the shape of the molecule as sphere, oblate and prolate spheroids. Mathematical model for simulating dielectrophoretic behavior of a myoglobin molecule in a microchannel is developed. The microchannel consists of parallel array of electrodes at the bottom wall. Finite element based approach is considered to solve the problem. The variation in the Clausius-Mossotti factor with respect to the applied electric field frequency is observed for aqueous solution of myoglobin. The crossover frequency is obtained as 30 MHz for given properties, for all the shapes of molecule. Shifting of crossover frequency with conductivity of medium is observed. The simulation results indicate that, the electric field and DEP forces are maximum at the edges of the electrodes and minimum elsewhere. The results also indicate that, DEP force exponentially decayed along the height of the channel.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".