Recovery of lithium from salt‐lake brine by liquid–liquid extraction using <scp>TBP‐FeCl<sub>3</sub></scp> based mixture solvent
Bibliographic record
Abstract
Abstract In this study, a complete cyclic process was developed for the lithium extraction from high‐concentration salt‐lake brine by using a high flash point, low water solubility, and low toxicity diluent mixed with the TBP‐FeCl3‐based organic solvent. The process was composed of four sections, including extraction, scrubbing, stripping, and regeneration. The optimal conditions for each section were determined first. Then, a 13‐stage cyclic extraction process was designed and validated experimentally for the high lithium concentration brine, which reached 8.612 g/L. By using the diluent at the optimized conditions, the overall recovery of lithium was higher than 96%. The stripping liquid contained 38.87 g/L Li+, while all the ratios of impurities of sodium, potassium, and magnesium to lithium were less than 0.1. The aims of enrichment and purification of Li+ from the complicated brine system were achieved by the developed extraction process with little Fe loss, which provided the technical support for the development of the salt‐lake lithium resource.
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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".