Advances in Lithium Extraction from Spodumene: Alternatives to Sulfuric Acid Digestion
Bibliographic record
Abstract
Conventional lithium extraction from spodumene via sulfuric acid roasting can achieve up to 98% recovery but suffers from high energy use, acidic residues, and purification complexity. This review evaluates alternative methods for both α- and β-spodumene, aiming for improved sustainability. For α-spodumene, Na2SO4–CaO salt roasting achieved >95% recovery at 900 °C via water leaching. Sodium carbonate roasting–NaOH leaching and mechanical activation–Na2SO4 roasting reached 95.9% and ~92%, respectively. High-pressure NaOH leaching reached 95.8%, while alkaline decomposition–acid leaching yielded 53.2–84.1%. Microwave-assisted calcination achieved up to 97% recovery, and fluoride-based roasting gave 93–98% but raised environmental concerns. Bioleaching is eco-friendly but slow, with <3.5% recovery. For β-spodumene, high-pressure leaching using sodium carbonate (>94%), sodium sulfate (90.7%–93.3%), sodium chloride (~93%), and nitric acid (~95%) provided high recoveries. Atmospheric leaching with HF and H3PO4 yielded ~90% and ~40%, respectively, while carbonic acid leaching reached 75% after multiple stages. Chlorination roasting achieved near-complete recovery. The Metso-Outotec high-pressure sodium carbonate leaching process is set for industrial-scale implementation at the Keliber project in 2025, confirming its scalability. Overall, these emerging processes have the potential to reduce energy input, reagent demand, and waste generation, offering practical pathways toward more sustainable lithium production from spodumene.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".