Review: Sustainable electrochemical lithium extraction from brine and seawater
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lithium (Li) is a critical element driving the transition toward a decarbonized environment by enabling sustainable energy storage and use in modern infrastructure. Over the past decades, the widespread exploitation of electronic devices and electric vehicles (EVs) has significantly driven global demand for Li. Although Li is primarily extracted from ore deposits, the increasing depletion of mineral resources has shifted focus toward other sources, such as seawater and salt lake brines. Several methods have been employed to recover Li from saline water (brine and seawater) at laboratory and pilot scales, which can be categorized into conventional and direct lithium extraction (DLE) approaches. Conventionally, the lime‐soda evaporation method has been widely applied for extracting Li from brine; however, this approach limits brine with low Mg/Li ratios and low efficiency. On the other hand, this review focuses on the DLE approaches, particularly electrochemical techniques, via electrolysis, electrodialysis, and capacitive dialysis for Li recovery. The advancements in the synthesis of working electrode materials (i.e., lithium iron phosphate [LiFePO 4 ]), electrode modifications, and membrane modifications for enhancing Li recovery and selectivity are highlighted. Current challenges and future perspectives, particularly on scaling these innovations from bench‐scale to industrial applications, and the technical challenges relating to the process scale of electrochemical extraction approaches are discussed, together with future research directions. In short, this review provides useful insights into the potential of electrochemical approaches as sustainable, effective, and cost‐efficient processes that facilitate the discovery of novel approaches to satisfy the growing worldwide need for Li.
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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 it