Advances in lithium recovery from complex and dilute brines: a comprehensive review
Bibliographic record
Abstract
Global demand for lithium continues to rise due to its essential role in energy storage systems and battery-powered technologies, reaching an estimated 240,000 metric tons in 2024 (US Geological Survey). Canada, particularly Western Canada, holds significant potential for future lithium production, with brine resources enriched in lithium associated with oil and gas reservoirs. These deep formation waters, unlike conventional lithium-rich sources such as salt lake brines, contain only 10–150 ppm of lithium, and are thus considered unconventional, dilute lithium-bearing resources. In recent years, several review articles have summarised lithium extraction technologies, often focusing on high-salinity brines and the separation of lithium from magnesium. This review presents a comprehensive overview of current technologies and recent developments aimed at extracting lithium from such diluted brines. Common methods, including solvent extraction, ion exchange type inorganic sorbents, and membrane processes, are reviewed based on literature published recently. Emerging hybrid approaches including ionic liquid membranes and adsorbent-membrane combinations are also discussed. By examining the strengths and limitations of each technology, this review guides future research toward more sustainable and efficient lithium recovery from unconventional dilute lithium-bearing waters.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".