Liquid DEP actuation and precision dispensing of variable volume droplets
Bibliographic record
Abstract
Droplet based microfluidic systems, in recent years, have demonstrated numerous advantages and exciting potential for Lab-On-a Chip applications. In order to fully realize the potential benefits of this technology, one requires precision dispensing and manipulation of droplets of known volume and sample concentration, in a rapid and controlled manner. In this article, we demonstrate the rapid and controlled microactuation of aqueous samples and subsequent dispensing of variable volume droplets in nanolitre to picolitre regime by using a coplanar tapered electrode structure that leverages the phenomena of liquid dielectrophoresis (L-DEP). The transient behavior of the tapered liquid jet departs significantly from that of a uniform liquid jet case and is not adequately explained in terms of a simplified lumped capacitance model as in the case of the uniform jet, during the L-DEP actuation. A more generalized numerical model is developed for the tapered actuation scheme to account for the experimental observations. We furthermore demonstrate that the density of the dispensed droplets can be proactively controlled by the judicious placement of electrode bumps and pinches in the electrode structure thus overcoming the limitations imposed by Rayleigh's instability criterion. The proposed droplet dispensing schemes are superior to existing L-DEP based dispensing schemes which are restricted in size and spacing of the dispensed droplets by Rayleigh's instability criteria and furthermore mostly restricted to equi-volume droplets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".