Review: Sustainable electrochemical lithium extraction from brine and seawater
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".