Investigating peak dispersion in free‐flow counterflow gradient focusing
Bibliographic record
Abstract
Free-flow electrophoresis (FFE) enables the continuous separation and collection of charged solutes, and as a result, it has drawn interest as both a preparative and an analytical tool for biological applications. Recently, a free-flow counterflow gradient focusing (FF-CGF) mechanism has been proposed with the goal of improving the resolution and versatility of FFE. To realize this potential, the factors that influence solute dispersion deserve further attention, including the gradient strength and the parabolic profile of the counterflow. Therefore, the goal of this work is to develop a theoretical model to study the interplay between these factors and molecular diffusion. Overall, an asymmetric solute distribution emerges for a wide range of parameters, and this behavior can be characterized with an exponentially modified Gaussian function. Results show that FF-CGF can achieve high-resolution separations, with the potential for high-throughput protein purification. Moreover, this work provides a practical guide for optimizing experimental conditions, as well as a strong framework for understanding and developing FF-CGF further.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".