Surface microfluidics—high-speed DEP liquid actuation on planar substrates and critical factors in reliable actuation
Bibliographic record
Abstract
Analysis of chemical and biological samples requires one or more of the following sequential steps: sampling, sample transport, sample pretreatment and sample processing. As a result of miniaturization, such total analysis systems offer manifold advantages such as mass production, portability and hence on-site operation, ease of use, low sample consumption and high stability. In this regard, dielectrophoretic (DEP) liquid actuation, in recent years, has emerged as an attractive technique for microfluidic systems since it provides simple, robust sample handling capabilities. This study experimentally examines the impact of more critical device structural features and material properties on the performance and reliability of the liquid DEP actuation. Specifically, we investigated the impact of electrode material (gold-chrome versus aluminum), various dielectric materials, and thicknesses on the DEP actuation voltage (minimum), DEP actuated finger transport dynamics and subsequent droplet formation. Both the voltage requirements for DEP liquid finger actuation and subsequent liquid finger transport are in good agreement with the theoretical predictions of the lumped-parameter dynamic model proposed by Jones (2001 Proc. 4th Int. Conf. on Applied Electrostatics ). Furthermore, the dynamics of the finger is influenced by the radius of finger, which is controlled by the width and spacing of the electrodes. For the smaller electrode geometry, the finger dynamics is viscosity dominated; exhibiting t 1/2 dependence however for larger finger radius inertia appears to dominate the finger dynamics. The utility of DEP in actuating protein ( Taq enzyme) samples was examined and observed to be limited by the specific protein adsorption.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".