Effect of Joule heating and temperature‐dependent zeta potential on electroosmotic flow measurements in calorimetric flow sensors
Bibliographic record
Abstract
This work reports a theoretical investigation of the effect of Joule heating and temperature‐dependent zeta potential on the electroosmotic flow measurements in calorimetric flow sensing. Joule heating resulting from the applied electric field in electroosmotic flow increases the temperature of the liquid inside the sensor and, consequently, modifies the sensor performance. The model presented in this paper considers temperature dependence of the wall zeta potential on the sensor characteristics. Additionally, all liquid properties such as density, viscosity, relative permittivity, specific heat, thermal conductivity, and electrical conductivity are taken as temperature‐dependent properties. A comparison between the characteristics of the modelled sensor in the presence and absence of Joule heating is presented. The effect of heater power on sensor characteristics is also discussed. Simulation results reveal that Joule heating and temperature dependence of zeta potential have a significant effect on the behaviour of calorimetric flow sensors, which must be considered when this type of sensor is used to measure electroosmotic flow. Temperature dependence of zeta potential, in particular, affected the velocity distribution inside the sensor considerably.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".