Ultrahigh‐speed, ultrahigh‐resolution preparative separation of protein biopharmaceuticals using membrane chromatography
Bibliographic record
Abstract
This paper discusses ultrahigh-speed, ultrahigh-resolution preparative protein separation using an in-house designed membrane chromatography device. The performance of the membrane chromatography device was systematically compared with an equivalent resin-packed preparative column. Experiments carried out using model proteins showed that membrane chromatography gave more than four times greater resolution than the preparative column, while at the same time being more than 19 times faster. Membrane chromatography was therefore a better option, not only in terms of higher productivity but also in terms of higher product purity. Membrane chromatography was also superior in terms of resolving and presenting tracer impurity peaks in the chromatogram. Experiments carried out using monoclonal antibody samples showed that membrane chromatography was suitable for ultrahigh speed, and ultrahigh resolution fractionation of charge variants. This paper highlights and explains the need for proper device design for enabling the use of membrane chromatography for the efficient purification of protein biopharmaceuticals.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".