Effect of pulsed electric field parameters on peptide migration selectivity and efficiency in whey protein hydrolysate separation by electrodialysis with ultrafiltration membrane
Bibliographic record
Abstract
In this study, we investigated the influence of pulsed electric field (PEF) conditions during electrodialysis with ultrafiltration membrane (EDUF) on the separation of peptides present in a whey protein hydrolysate (WPH). Six PEF conditions, defined by different pulse/pause durations (1s/1s, 5s/1s, 5s/5s, 10s/1s, 10s/5s, 10s/10s) were compared. Peptide migration efficiency (MR charge ) and selectivity were evaluated in both cationic and anionic recovery compartments. The duty cycle (pulse duration divided by total cycle time) emerged as a key parameter, with lower values consistently associated with higher peptide migration efficiency. These effects arise from the combined action of concentration polarization (CP) relaxation during pauses and short-lived electroconvective vortices (ECVs) generated at the start of each pulse. Furthermore, PEF conditions affected peptide selectivity, likely due to the dynamic competition among charged peptides within the diffusion boundary layer (DBL), since repeated pulse-pause cycles affect DBL extension and concentration profile formation, favoring the transport of larger or less mobile peptides. Altogether, these results provide new insights into how mechanisms well established in conventional electrodialysis (ED) also govern peptide transport in EDUF and that tuning the duty cycle can strategically improve both migration efficiency and peptide selectivity.
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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 | 0.000 | 0.000 |
| 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".