Peak-Shape Correction\nto Symmetry for Pressure-Driven\nSample Injection in Capillary Electrophoresis
Bibliographic record
Abstract
Pressure-driven sample injection in capillary electrophoresis\nresults\nin asymmetric peaks due to difference in shapes between the front\nand the back boundaries of the sample plug. Uneven velocity profile\nof fluid flow across the capillary gives the front boundary a parabolic\nshape. The back side, on the other hand, has a flat interface with\nthe electrophoresis run buffer. Here, we propose a simple means of\ncorrecting this asymmetry by pressure-driven “propagation”\nof the injected plug, with the parabolic sample–buffer interface\nestablished at the back. We prove experimentally that such a propagation\nprocedure corrects peak asymmetry to the level comparable to injection\nthrough electroosmosis. Importantly, the propagation-based correction\nprocedure also solves a problem of transferring the sample into the\nefficiently cooled zone of the capillary for capillary electrophoresis\n(CE) instruments\nwith active cooling. The suggested peak correction procedure will\nfind applications in all CE methods that rely on peak shape analysis,\ne.g., nonequilibrium capillary electrophoresis of equilibrium mixtures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| 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) | 0.023 | 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 teacher head, 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".