Fabrication of Dielectrophoretic Microfluidic Device
Bibliographic record
Abstract
Technological needs of the recent times require the improvement in micro-scale devices that manipulate the bioparticles like cells, bacteria, viruses, DNA, proteins, etc. Such devices have diverse and widespread applications in biomedical, drug delivery and diagnostics for separating, trapping, sorting and mixing of particles. Dielectrophoresis (DEP) is one of the techniques used for manipulating the particles in a nonuniform electric field. In the present study, fabrication and characterization of microfluidic device for DEP is analyzed and experimented. An overview of fabrication techniques which can be used for making of DEP device is provided with experimental details. DEP microfluidic device is fabricated by preparing channels and microelectrodes on PDMS and glass materials respectively. Oxygen plasma treatment has been used for bonding the PDMS channel and micro-electrode patterned glass substrate. Further experiments are conducted to demonstrate the DEP principle with polystyrene microbeads. The movement of microbeads towards the high electric field strengths at 12Vpp and 10 MHz frequency is observed. Characterizing equipments like ellipsometer, profilometer, scanning electron microscopy, contact angle measurement systems were used for measuring oxide layer thickness, width and depth of the channels, surface characteristics etc., during fabrication.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".