Selective leaching and recovery of lithium ions from lithium slag with low lithium content
Bibliographic record
Abstract
Abstract Hard rock lithium ore has become a significant natural lithium resource, which is second only to brine lithium resources. However, the recovery process generates a considerable quantity of tailings with very low lithium content. At present, there is no established process for efficient recovery of lithium ions from low‐grade lithium ores. In this study, a novel recovery method was established to extract lithium ions from a tailing slag with lithium content of 0.8% by using sulphuric acid solution as leaching reagent. Furthermore, the mechanism of the recovery process was deeply explored. In accordance with the optimal leaching conditions with hydrogen–lithium ratio as 2:1, leaching temperature as 70°C, and leaching time as 60 min, the leaching rate of lithium ion reached up to approximately 100%. The obtained low lithium‐ion solution was concentrated to yield a lithium‐rich solution, which was then used to produce the lithium products. Lithium phosphate product was obtained by precipitation of lithium ion with sodium dihydrogen phosphate with the addition of sodium hydroxide and oxalic acid to remove impurities. The recovery and purity of lithium phosphate were 94.5% and 99.7%, respectively. This study demonstrated the successfully selective recovery of lithium ions from low‐grade lithium tailing slag through a novel and efficient recovery method.
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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) | 0.000 | 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".