A New Approach for Increasing Speed, Loading Capacity, Resolution, and Scalability of Preparative Size-Exclusion Chromatography of Proteins
Bibliographic record
Abstract
Low speed, low capacity, and poor scalability make size-exclusion chromatography (SEC) unattractive for use in the preparative separation of proteins. We discuss a novel z2 cuboid SEC device that addresses these challenges. A z2 cuboid SEC device (~24 mL volume) was systematically compared with a conventional SEC column having the same volume and packed with the same resin. The primary objective of this study was to use the same volume of SEC medium in a much more efficient way by using the novel device. At any given flow rate, the pressure drop across the z2 cuboid SEC device was lower by a factor of 6 to 8 due to its shorter bed height and greater cross-sectional area. Under overloaded conditions, the peaks obtained during protein separation with the conventional column were poorly resolved and showed significant fronting, while those obtained with the z2 cuboid SEC device were much better resolved and showed no fronting. At any given flow rate, better resolution was obtained with the z2 cuboid SEC device, while for obtaining a comparable resolution, the flow rate that could be used with the z2 cuboid SEC device was higher by a factor of 2 to 3. Hence, productivity in SEC could easily be increased by 200 to 300% using the z2 cuboid SEC device. The scalability of the z2 cuboid SEC device was also demonstrated based on a device with a 200 mL bed volume.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".