Migration behaviour of discontinuous buffers in capillary electrophoresis during protein enrichment
Bibliographic record
Abstract
Capillary electrophoresis (CE) is not only an effective separation technique, but can also serve as a sample preparation tool for enrichment and purification at sub-microliter sample volumes. Our approach is based on the use of a discontinuous buffer system consisting of an acid and a base (acetate and ammonium). Proteins and/or peptides with isoelectric points between the pH values of these two buffers will become stacked at the neutralization reaction boundary (NRB). To understand the mechanism of the NRB formation and the electrophoretic migration of various ions during the enrichment, we performed experiments using myoglobin and mesityl oxide to reveal the ion migration patterns at the buffer junction, and utilized Simul 5 to computer simulate the process. The simulated results closely resembled the experimental data, and together, they effectively revealed the characteristics of the discontinuous buffers. Importantly, the discovery allowed the manipulation of NRB behaviours by controlling the discontinuous buffer composition. To illustrate this, the removal of urea as an unwanted background molecule from the enriched protein sample was achieved based on the acquired information.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".