Bioseparation using membrane chromatography: Innovations, and challenges
Bibliographic record
Abstract
The resin-based column continues to be the dominant incumbent in bioprocess chromatography. While alternative formats such as membrane-, monolith- and fiber-based chromatography are more visible than before, each still plays minor roles. The reasons for this are complex and some of these are explained in this paper. However, the fact remains that membrane chromatography has come a long way since its early days of development. The main advantage of membrane chromatography continues to be its convection dominant transport mechanism, the resultant benefit being fast and scalable separation. Also, resolution obtained with properly designed devices could be comparable or even better than resin-based chromatography. Significant progress has been made in new membrane development, membrane characterization, device design and novel applications development. A wider range of new membrane matrices, ligands, and ligand-matrix linking chemistries are now available. New membrane modules, formats, and process configurations have also helped improve membrane performance. However, some significant challenges still exist, and these need to be addressed if membrane chromatography is to become more mainstream in the field of bioprocessing. Also, membrane chromatography has significant potential for application in analytical separations and this space has hardly been explored. In this paper, the advances in the areas of membrane preparation, device design and process development are reviewed. A high-level cost analysis is presented and the role of process design in membrane chromatography is discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".