Model of separation performance of bilinear gradients in scanning format counter‐flow gradient electrofocusing techniques
Bibliographic record
Abstract
Counter-flow gradient electrofocusing allows the simultaneous concentration and separation of analytes by generating a gradient in the total velocity of each analyte that is the sum of its electrophoretic velocity and the bulk counter-flow velocity. In the scanning format, the bulk counter-flow velocity is varying with time so that a number of analytes with large differences in electrophoretic mobility can be sequentially focused and passed by a single detection point. Studies have shown that nonlinear (such as a bilinear) velocity gradients along the separation channel can improve both peak capacity and separation resolution simultaneously, which cannot be realized by using a single linear gradient. Developing an effective separation system based on the scanning counter-flow nonlinear gradient electrofocusing technique usually requires extensive experimental and numerical efforts, which can be reduced significantly with the help of analytical models for design optimization and guiding experimental studies. Therefore, this study focuses on developing an analytical model to evaluate the separation performance of scanning counter-flow bilinear gradient electrofocusing methods. In particular, this model allows a bilinear gradient and a scanning rate to be optimized for the desired separation performance. The results based on this model indicate that any bilinear gradient provides a higher separation resolution (up to 100%) compared to the linear case. This model is validated by numerical studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".