Feasibility and performances of an electrodialysis stack fully composed of hierarchical ion-exchange membranes for demineralization of a complex food solution
Bibliographic record
Abstract
Recent studies have highlighted hierarchical ion-exchange membranes (hIEMs), fabricated by coating a functional layer on a porous substrate, as promising alternatives to commercial ion-exchange membranes for whey demineralization. These innovative hierarchical anion-exchange membranes (hAEMs) and cation-exchange membranes (hCEMs) had never been evaluated in combination in an ED system for food matrices demineralization such as whey. In this study, the impacts of two membrane configurations (EbN-1/ UL6 and EbN-1/EbS-2) and their performances (energy consumption, current efficiency, …) for whey demineralization by ED were evaluated and compared to commercial membranes (AMX/CMX). Although the systems fully composed of hIEMs took longer to demineralize up to 70 % the 18 % sweet whey solution compared to the commercial reference (EbN-1/UL6: 86.0 ± 0.7 min vs. reference: 62.7 ± 1.7 min), demineralization was successful. For EbN-1/EbS-2, although the global system resistance increased significantly throughout the runs (25.5 Ω vs 53.3 Ω), the energy consumption was similar to the reference (15.00 ± 0.64 Wh vs 14.33 ± 0.27 Wh). One membrane configuration, the EbN-1/UL6 system, stood out and its performances were successful enough to challenge commercial ED set-ups. This study proved the applicability of an ED system fully composed of hIEMs for ED applications to complex food systems.
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 | 0.000 |
| 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 | 0.001 | 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".