How Molecular Weight Cut-Offs and Physicochemical Properties of Polyether Sulfone Membranes Affect Peptide Migration and Selectivity during Electrodialysis with Filtration Membranes
Bibliographic record
Abstract
Filtration membranes (FMs) are an integral part of electrodialysis with filtration membranes (EDFM), a green and promising technology for bioactive peptide fractionation. Therefore, it is paramount to understand how physicochemical properties of FMs impact global and selective peptide migration to anionic (A−RC) and cationic (C+RC) peptide recovery compartments during their simultaneous separation by EDFM. In this context, six polyether sulfone (PES) membranes with molecular weight cut-offs (MWCO) of 5, 10, 20, 50, 100 and 300 kDa were characterized and used during EDFM to separate peptides from a complex whey protein hydrolysate. Surface charge, roughness, thickness and surface/pores nature of studied PES membranes were similar with small differences in conductivity, porosity and pore size distribution. Interestingly, global peptides migration to both recovery compartments increased linearly as a function of MWCO. However, peptide selectivity changed according to the recovery compartments and/or the peptide’s charge and MW with an increase in MWCO of FMs. Indeed, in A−RC, the relative abundance (RA) of peptides having low negative charge and MW (IDALNENK and VLVLDTDYK) decreased (45% to 19%) with an increase in MWCO, while the opposite for peptides having high negative charge and MW (TPEVDDEALEK, TPEVDDEALEKFDK & VYVEELKPTPEGDLEILLQK) (increased from 16% to 43%). Concurrently, in C+RC, regardless of MWCO used, the highest RA was observed for peptides having low positive charge and MW (IPAVFK & ALPMHIR). It was the first time that the significant impact of charge, MWCO and pore size distribution of PES membranes on a wide range of MWCO was demonstrated on EDFM performances.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".