Study of Joule heating effects on temperature gradient in diverging microchannels for isoelectric focusing applications
Bibliographic record
Abstract
IEF is a high-resolution separation method taking place in a medium with continuous pH gradients, which can be set up by applying electrical field to the liquid in a diverging microchannel. The axial variation of the channel cross-sectional area will induce nonuniform Joule heating and set up temperature gradient, which will generate pH gradient when proper medium is used. In order to operationally control the thermally generated pH gradients, fundamental understanding of heat transfer phenomena in microfluidic chips with diverging microchannels must be improved. In this paper, two 3-D numerical models are presented to study heat transfer in diverging microchannels, with static and moving liquid, respectively. Through simulation, the temperature distribution for the entire chip has been revealed, including both liquid and solid regions. The model for the static liquid scenario has been compared with published results for validation. Parametric studies have showed that the channel geometry has significant effects on the peak temperature location, and the electrical conductivity of the medium and the wall boundary convection have effects on the generated temperature gradients and thus the generated pH gradients. The solution to the continuous flow model, where the medium convection is considered, shows that liquid convection has significant effects on temperature distribution and the peak temperature location.
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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 | 0.000 |
| 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".