Lithium recovery from pretreated <i>α</i>‐spodumene residue through acid leaching at ambient temperature
Bibliographic record
Abstract
Abstract A novel alkaline hydrothermal approach for low‐temperature conversion of α‐spodumene into Li2SiO3 residue was proposed, providing a promising method for extracting lithium from α‐spodumene as a pretreatment process. This work proposed a systematic investigation for extracting lithium from the residue by acid leaching and preparing lithium carbonate. The reaction feasibility between Li2SiO3 and acids (HCl and H2SO4) was first evaluated through thermodynamic calculation. Compared with the leaching effects of hydrochloric acid and sulphuric acid, sulphuric acid is the preferred leaching agent due to its higher extraction efficiency for lithium and lower acid consumption. Lithium extraction efficiency from the residue achieved up to 87.48% under the following optimized conditions: 0.75 mol/L H2SO4, 0.4 times the theoretical amount of acid, 10 min, 30°C, and 100 rpm. Based on the optimized conditions, the lithium‐containing solution was concentrated through three consecutive cycles of leaching, which obtained a concentration of 17.78 g/L for lithium. The leaching solution was purified by CaO‐Na2CO3, resulting in the removal rates of SiO32−, Mg2+, and Ca2+ of 84.22%, 95.51%, and 90.55%, respectively. Finally, the solution was precipitated with sodium carbonate to prepare Li2CO3. This paper facilitates the development of an economical process for efficient lithium extraction from spodumene at low temperatures.
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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) | 0.000 | 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) | 0.001 | 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".