Bioleaching of lithium from jadarite, spodumene, and lepidolite using Acidiothiobacillus ferrooxidans
Bibliographic record
Abstract
Lithium (Li) is becoming increasingly important due to its use in clean technologies that are required for the transition to net zero. Although acidophilic bioleaching has been used to recover metals from a wide range of deposits, its potential to recover Li has not yet been fully explored. In this study, we used a model Fe(II)- and S-oxidising bacterium, Acidiothiobacillus ferrooxidans (At. Ferrooxidans), to extract Li from three different minerals and kinetic modelling to predict the dominant reaction pathways for Li release. Bioleaching of Li from the aluminosilicate minerals lepidolite (K(Li,Al) 3 (Al,Si,Rb) 4 O 10 (F,OH) 2 ) and spodumene (LiAl(Si 2 O 6 )) was slow, with only up to 14% (approximately 12 mg/L) of Li released over 30 days. By contrast, At. ferrooxidans accelerated Li leaching from a Li-bearing borosilicate clay (jadarite, LiNaB 3 SiO 7 OH) by over 50% (over 120 mg/L) in 21 days of leaching, and consistently enhanced Li release throughout the experiment compared to the uninoculated control. Biofilm formation and flocculation of sediment occurred exclusively in the experiments with At. ferrooxidans and jadarite. Fe(II) present in the jadarite-bearing clay acted as an electron donor. Chemical leaching of Li from jadarite using H2SO4 was most effective, releasing approximately 75% (180 mg/L) of Li, but required more acid than bioleaching for pH control. Kinetic modelling was unable to replicate the data for jadarite bioleaching after primary abiotic leaching stages, suggesting additional processes beyond chemical leaching were responsible for the release of Li. A new crystalline phase, tentatively identified as boric acid, was observed to form after acid leaching of jadarite. Overall, the results demonstrate the potential for acidophilic bioleaching to recover Li from jadarite, with relevance for other Li-bearing deposits.
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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) | 0.000 | 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 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".