Paper‐PEG‐based membranes for hydrophobic interaction chromatography: Purification of monoclonal antibody
Bibliographic record
Abstract
This article discusses the preparation of novel Paper-PEG interpenetrating polymer network-based membranes as inexpensive alternative to currently available adsorptive membranes. The Paper-PEG membranes were developed for carrying out hydrophobic interaction membrane chromatography (HIMC). PEG is normally very hydrophilic but can undergo phase separation and become hydrophobic in the presence of high antichaotropic salt concentrations. Two variants of the Paper-PEG membranes, Paper-PEG 1 and Paper-PEG 2 were prepared by grafting different amounts of the polymer on filter paper and these were tested for their hydraulic properties and antibody binding capacity. The better of the two membranes (Paper-PEG 1) was then used for purifying the monoclonal antibody hIgG1-CD4 from simulated mammalian cell culture supernatant. The processing conditions required for purification were systematically optimized. The dynamic antibody binding capacity of the Paper-PEG 1 membrane was about 9 mg/mL of bed volume. A single step membrane chromatographic process using Paper-PEG 1 membrane gave high monoclonal antibody purity and recovery. The hydraulic permeability of the paper-based membrane was high and was maintained even after many runs, indicating that membrane fouling was negligible and the membrane was largely incompressible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".