Electrophoresis in the presence of gradients: I. Viscosity gradients
Bibliographic record
Abstract
In many cases, the resolution provided by capillary electrophoresis systems approaches that predicted for diffusion-limited separations. Once all device-related sources of band broadening have been eliminated or minimized, only thermal diffusion remains. In principle, peaks can be sharpened using gradients of various system characteristics such as gel concentration, buffer viscosity and electric field. However, it is not clear whether this can actually increase the resolution of the system. In this article, we focus our attention on viscosity gradients and we examine both continuous and step-like variations. Our results indicate that the performance of electrophoretic systems cannot be improved by viscosity gradients. They may provide extra stacking, and thus improve the resolution, when the injection width is non-negligible. However, for the systems considered here, the best resolution is obtained when the viscosity is uniform and the stacking is entirely performed at injection. We conclude by discussing the link between these results, the fundamental laws of thermodynamics, the nature of the detection process and the importance of having nonlinear effects in nonuniform systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".