Band‐broadening in capillary zone electrophoresis with axial temperature gradients
Bibliographic record
Abstract
It is widely accepted that Joule heating effects yield radial temperature gradients in capillary zone electrophoresis (CZE). The resultant parabolic profile of electrophoretic velocity of analyte molecules is believed to increase the band-broadening via Taylor-Aris dispersion. This typically insignificant contribution, however, cannot explain the decrease in separation efficiency at high electric fields. We show that the additional band-broadening due to axial temperature gradients may provide the answer. These axial temperature variations result from the change of heat transfer condition along the capillary, which is often present in CZE with thermostating. In this case, the electric field becomes nonuniform due to the temperature dependence of fluid conductivity, and hence the induced pressure gradient is brought about to meet the mass continuity. This modification of the electroosmotic flow pattern can cause significant band-broadening. An analytical model is developed to predict the band-broadening in CZE with axial temperature gradients in terms of the theoretical plate height. We find that the resultant thermal plate height can be very high and even comparable to that due to molecular diffusion. This thermal plate height is much higher than that due to radial temperature gradients alone. The analytical model explains successfully the phenomena observed in previous experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".