Parallel Congruent Electrode Sensor to Detect and Characterize Droplets in a T-Junction Droplet Generator
Bibliographic record
Abstract
In this article, we present a simple capacitive droplet sensing method that works with an on-chip droplet generator with real-time sensing capability of droplet composition and size. The sensing system works with the aid of a simple pair of electrodes along the channel wall forming a parallel congruent electrode (PCE)-based capacitor sensor. Using a PCE capacitor, we were able to characterize the droplet based on the material and its size. The droplet generator was classical T-junction-based and was regulated by our latest pneumatic-based control process. We present a numerical model that can simultaneously solve droplet generation and real-time capacitive sensing. The numerical model solves laminar two-phase flow, phase field, and electrostatics multiphysics simultaneously to predict droplet generation and capacitive sensing. We utilized the model to predict the best operating and geometric parameters. Our simulation showed that the electrode width to droplet size ratio of 1:0.95 was the best proportion for sensing droplet movement. This was verified experimentally. The two electrodes in the PCE position outperformed five electrodes in the coplanar interdigital electrode (IE) in the position for the same set of droplet generation. The change in capacitance value was observed using the PCE sensor for the variation in dispersed material and droplet size. The resolution of the sensing was 0.012 pF. The multilayer droplet generation, with simple and simultaneous sensing as well as regulation capability presented in this article, can be useful for the development of controls and sensing for precision droplet generators.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".