Electrodialytic Processes: Market Overview, Membrane Phenomena, Recent Developments and Sustainable Strategies
Bibliographic record
Abstract
In the context of preserving and improving human health, electrodialytic processes are very promising perspectives. Indeed, they allow the treatment of water, preservation of food products, production of bioactive compounds, extraction of organic acids, and recovery of energy from natural and wastewaters without major environmental impact. Hence, the aim of the present review is to give a global portrait of the most recent developments in electrodialytic membrane phenomena and their uses in sustainable strategies. It has appeared that new knowledge on pulsed electric fields, electroconvective vortices, overlimiting conditions and reversal modes as well as recent demonstrations of their applications are currently boosting the interest for electrodialytic processes. However, the hurdles are still high when dealing with scale-ups and real-life conditions. Furthermore, looking at the recent research trends, potable water and wastewater treatment as well as the production of value-added bioactive products in a circular economy will probably be the main applications to be developed and improved. All these processes, taking into account their principles and specificities, can be used for specific eco-efficient applications. However, to prove the sustainability of such process strategies, more life cycle assessments will be necessary to convince people of the merits of coupling these technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".