Selective separation of lithium using concentration polarization near ion exchange membranes: A proof-of-concept study
Bibliographic record
Abstract
The growing demand for lithium worldwide requires efficient and sustainable recovery strategies to reduce reliance on resource-intensive primary sources like conventional mining and brine extraction. Secondary sources, such as wastewater from spent lithium-ion batteries and industrial wastewater have emerged as viable alternatives for lithium extraction. This study investigated the concentration polarization (CP) near ion-exchange membranes (IEMs) for selective separation of Li + over K + using the difference in the diffusion coefficient. The smaller diffusion coefficient of Li + (1.03 × 10 −9 m 2 /s) than that of K + (1.96 × 10 −9 m 2 /s) allowed the selective Li + separation in shock electrodialysis (SED). A lab-scale SED reactor was built with two cation-exchange membranes (CEMs) to evaluate the effect of the applied voltage, flow rate, and intermembrane distance on Li + selectivity. A successful Li + selection (up to a 40.8% increase in the Li + concentration) was demonstrated in the effluent of the concentrating boundary layer while the effluent of the diluting boundary layer showed a depletion of Li + (up to a 30.4% decrease in the Li + concentration). Mathematical model simulations of a ternary system (Li + , K + , Cl − ) confirmed the selective Li + separation in SED due to the diffusivity difference. In the model simulations, the Li + selection was substantially affected by the selectivity of CEM, applied voltage and boundary layer thickness. These findings highlight the potential of SED as a scalable and energy-efficient approach for lithium recovery from secondary sources contributing to resource recycling in the circular economy. • Diffusivity-driven separation was demonstrated using shock electrodialysis. • Concentration polarization in electrodialysis was used for selective Li + separation. • Lower diffusivity of Li + resulted in higher concentration in the concentrating boundary layer. • Applied voltage, flow rate, inter-IEM distance governed the separation efficiency in experiments. • In modeling, IEM selectivity, electric current, boundary layer thickness governed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".