The influence of grafted polymer architecture and fluid hydrodynamics on protein separation by entropic interaction chromatography
Bibliographic record
Abstract
Entropic interaction chromatography (EIC) provides efficient size-based separation of protein mixtures through the entropy change associated with solute partitioning into a layer of hydrophilic homopolymer that has been end-grafted within the pores of a macroporous chromatography support. In this work, surface-initiated atom-transfer radical polymerization (ATRP) is used to prepare a library of EIC stationary phases covering a wide range of grafted-chain densities and molecular weights. Exhaustive chain cleavage and analysis by saponification and GPC-MALLS, respectively, show that the new ATRP synthesis procedure allows for excellent control over graft molecular weight and polydispersity. The method is used to prepare high-density grafts (up to 0.164 +/- 0.005 chains/nm(2)) that extend the range of EIC applications to include efficient buffer-exchange and desalting of protein preparations. Reducing the graft density allows for greater partitioning of high molecular weight solutes, extending the linear range of the selectivity curve. Increasing graft molecular weight also alters selectivity, but more directly affects column capacity by increasing the volume of the grafted layer. Protein partitioning in high-density EIC columns is found to decrease with mobile-phase velocity (u). Although solute mass transfer resistances leading to an increase in plate height can explain this effect, pressure drop data across the column are indicative of weak convective flow through at least a fraction of the grafted architecture. Modeling of the grafted brush properties in the presence of solvent flow by subjecting a self-consistent-field theory representation of the brush to a viscous shear force predicts that the grafted chains will tilt and elongate in the direction of flow. The shear force may therefore act to reduce the number of conformations available to chains, increasing their rigidity without significantly altering the thickness of the grafted layer. A reduction in protein partitioning is then predicted when the dependence on u of the solute entropy loss is stronger than that of the grafted polymer, a condition met at high graft densities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".