Capacitance-Based Droplet Position Sensing Optimization Through Digital Microfluidic Parameters
Bibliographic record
Abstract
Design of a closed-loop droplet position control is an essential step towards the development of fully automated digital microfluidic devices. However, the performance of any closed-loop controller is ultimately limited by the accuracy and precision of the feedback sensors. In this paper, an effective capacitance based droplet sensor was designed and optimized through simulation to reduce the droplet position error. A full factorial design was conducted on the droplet sensor simulation model to observe the behavior of the position error as a function of the parameters of a digital microfluidic device. An empirical model was then fitted to the data obtained from the designed simulations and optimized to reduce the position estimate error. Results suggest that the performance of the capacitance based droplet sensor studied in this work is most dependent on the dielectric thickness, droplet radius, electrode pitch, electrode separation, filler fluid permittivity, and plate gap. Isoperformance curves of the sensor performance were obtained using the empirical model to show the interaction between digital microfluidic parameters, as well as to aid in the design of digital microfluidic devices equipped with a similar capacitance based droplet sensor.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".