Droplet charge-to-mass ratio measurement in an EHD liquid-liquid extraction system
Bibliographic record
Abstract
An experimental investigation has been carried out in a rectangular lucite cell equipped with parallel electrode plates along the two sides of the cell. The droplets are formed at a grounded hollow electrode. Distilled water is used as the droplet phase and a viscous mineral oil is used as the continuous phase. Experiments have been conducted at various flow rates of the dispersed phase with quiescent continuous phase; the applied DC voltage is from 0 to 15 kV. Charge acquired on droplets both at the hollow electrode and downstream near the bottom of the cell was observed from hollow electrode current waveforms and by a Faraday-cup through direct sampling, respectively. The results reported from the present investigation, extending from the single discrete droplet regime (at low applied voltage) to the dispersed multidroplet regime (at high applied voltage), indicate that the modified Rayleigh instability model and the Vonnegut and Neubauer model can predict the maximum droplet charge acquired in liquid-liquid systems. The modified Vonnegut model can predict most of the experimental results when the applied electric field is high enough and EHD (electrohydrodynamic) forces become dominant.>
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 | 0.000 |
| 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".