Performance Comparison of a Laterally-Fed Membrane Chromatography (LFMC) Device with a Commercial Resin Packed Column
Bibliographic record
Abstract
The use of conventional membrane adsorbers such as radial flow devices is largely restricted to flow-through applications, such as virus and endotoxin removal, as they fail to give acceptable resolution in bind-and-elute separations. Laterally-fed membrane chromatography or LFMC devices have been specifically developed to combine high-speed with high-resolution. In this study, an LFMC device containing a stack of strong cation exchange membranes was compared with an equivalent resin packed column. Preliminary characterization experiments showed that the LFMC device had a significantly greater number of theoretical plates per metre than the column. These devices were used to separate a ternary model protein mixture consisting of ovalbumin, conalbumin and lysozyme. The resolution obtained with the LFMC device was better than that obtained with the column. For instance, the LFMC device could resolve lysozyme dimer from lysozyme monomer, which was not possible using the column. In addition, the LFMC device could be operated at lower pressure and at significantly higher flow rates. The devices were then compared based on an application case study, i.e., preparative separation of monoclonal antibody charge variants. The LFMC device gave significantly better separation of these variants than the column.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".