Understanding of Adsorption and Desorption Mechanisms of Anthocyanins and Proanthocyanidins on Heterogeneous and Homogeneous Cation-Exchange Membranes
Bibliographic record
Abstract
The presence of membrane fouling is the main drawback in membrane processes, and it is related to the premature use and high cost for the replacement of membranes. Polyphenols in cranberry juice are associated with ion-exchange membrane fouling, and it results in a loss of these beneficial compounds in the juice when treated by membrane processes such as electrodialysis. In the present work, four heterogeneous or pseudohomogeneous cation-exchange membranes (CSE-fg, MK-40, CEM Type-II, and CJMC-5), different in terms of the polymer matrix (aromatic, aliphatic), exchange capacity, size, and location of meso and macropores, were studied to understand the impact of the membrane structure and physico-chemical properties on adsorption and desorption of phenolic compounds (anthocyanins and proanthocyanidins) from cranberry juice. It appeared from these results that MK-40, CEM Type-II, and CSE-fg were more prone to fouling due to their high ion-exchange capacity, their thickness, and the presence of meso and macropores in their structure. Indeed, electrostatic interactions occurred between fixed groups of membranes and polyphenolic ions. Desorption of the entire membrane and cryogenic grinding with pH adjusted to 10 allowed a better recovery of anthocyanins and proanthocyanidins (PACs), respectively, since hydroxide ions competed with polyphenols and membrane that induced desorption of polyphenols. In the future, this new knowledge will become the basis for a more sensible choice of membranes and for the development of protocols for extending their life cycle.
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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) | 0.000 | 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".