A Carrier Phase Ultrafiltration and Backflow Recovery Technique for Purification of Biological Macromolecules
Bibliographic record
Abstract
A simple carrier phase based ultrafiltration technique that is akin to liquid chromatography and is suitable for medium-to-large volume sample preparation in the laboratory is discussed in this paper. A membrane module was integrated with a liquid chromatography system in a "plug and play" mode for ease of sample handling, and recovery of species retained by the membrane. The sample injector and pump were used for feed injection and for driving ultrafiltration, while the sensors and detectors were used for real-time monitoring of the separation process. The concentration of retained species was enriched by utilizing controlled concentration polarization. The recovery of the retained and enriched species was enhanced by backflow of carrier phase through the membrane using appropriate combination of valves. The backflow of carrier phase also cleaned the membrane and limited the extent of membrane fouling. Proof-of-concept of the proposed technique was provided by conducting different types of protein ultrafiltration experiments. The technique was shown to be suitable for carrying out protein fractionation, desalting, buffer exchange and concentration enrichment. Adoption of this approach is likely to make ultrafiltration easier to use for non-specialized users in biological research laboratories. Other advantages include enhanced product recovery, significant reduction in the number of diavolumes of buffer needed for conducting desalting and buffer exchange, minimal membrane fouling and the potential for repeated use of the same module for multiple separation cycles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".