Cation Exchange Membranes and Process Optimizations in Electrodialysis for Selective Metal Separation: A Review
Bibliographic record
Abstract
The selective separation of metal species from various sources is highly desirable in applications such as hydrometallurgy, water treatment, and energy production but also challenging. Monovalent cation exchange membranes (CEMs) show a great potential to selectively separate one metal ion over others of the same or different valences from various effluents in electrodialysis. Selectivity among metal cations is influenced by both the inherent properties of membranes and the design and operating conditions of the electrodialysis process. The research progress and recent advances in membrane development and the implication of the electrodialysis systems on counter-ion selectivity are extensively reviewed in this work, focusing on both structure-property relationships of CEM materials and influences of process conditions and mass transport characteristics of target ions. Key membrane properties, such as charge density, water uptake, and polymer morphology, and strategies for enhancing ion selectivity are discussed. The implications of the boundary layer at the membrane surface are elucidated, where differences in the mass transport of ions at interfaces can be exploited to manipulate the transport ratio of competing counter-ions. Based on the progress, possible future R&D directions are also proposed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".