Performance and Impact of Crosslinking Level of Hierarchical Anion-Exchange Membranes on Demineralization of a Complex Food Solution by Electrodialysis
Bibliographic record
Abstract
Promising results were recently reported for hierarchical ion-exchange membranes, fabricated by the UV crosslinking of a thin functional coating on a porous substrate, on model NaCl solution demineralization by electrodialysis (ED). Hierarchical anion-exchange membranes (hAEMs) have never been tested with complex solutions to demonstrate their potential use in the biofood industry. The impact of three different crosslinking densities of the ion-exchange coating (EbN-1, EbN-2 and EbN-3) on the performances of whey demineralization by ED was investigated and compared with commercial AMX. The results showed that by increasing the coating crosslinking density, the membrane conductivity decreased, leading to an increase in the global system resistance during whey demineralization (from +28% to +64%). However, 18% sweet whey solutions were successfully treated until 70% demineralization for all membranes. The energy consumption (averaged EbN value of 14.8 vs. 15.1 Wh for AMX) and current efficiency (26.0 vs. 27.4%) were similar to the control. Potential fouling by non-protein nitrogen was detected by ATR-FTIR for hAEMs impacting some membranes properties and ED performances. Overall, EbN-1 obtained results were comparable with the benchmark and can be considered as an alternative membrane for whey demineralization by ED and other applications in the demineralization of complex products from the food industry.
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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) | 0.001 | 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".