Zonal rate model for stacked membrane chromatography part II: Characterizing ion‐exchange membrane chromatography under protein retention conditions
Bibliographic record
Abstract
The Zonal Rate Model (ZRM) has previously been shown to accurately account for contributions to elution band broadening, including external flow nonidealities and radial concentration gradients, in ion-exchange membrane (IEXM) chromatography systems operated under nonbinding conditions. Here, we extend the ZRM to analyze and model the behavior of retained proteins by introducing terms for intra-column mass transfer resistances and intrinsic binding kinetics. Breakthrough curve (BTC) data from a scaled-down anion-exchange membrane chromatography module using ovalbumin as a model protein were collected at flow rates ranging from 1.5 to 20 mL min(-1). Through its careful accounting of transport nonidealities within and external to the membrane stack, the ZRM is shown to provide a useful framework for characterizing putative protein binding mechanisms and models, for predicting BTCs and complex elution behavior, including the common observation that the dynamic binding capacity can increase with linear velocity in IEXM systems, and for simulating and scaling separations using IEXM chromatography. Global fitting of model parameters is used to evaluate the performance of the Langmuir, bi-Langmuir, steric mass action (SMA), and spreading-type protein binding models in either correlating or fundamentally describing BTC data. When combined with the ZRM, the bi-Langmuir, and SMA models match the chromatography data, but require physically unrealistic regressed model parameters to do so. In contrast, for this system a spreading-type model is shown to accurately predict column performance while also providing a realistic fundamental explanation for observed trends, including an observed increase in dynamic binding capacity with flow rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".