Selective recovery of lithium from <scp>Dead Sea</scp> end brines using <scp>UBK10</scp> ion exchange resin
Bibliographic record
Abstract
Abstract The Dead Sea, a live pool of minerals and elements, holds ~9% of the world's known lithium reserves. However, the low lithium concentrations (30–40 mg/L) in the end brine and the high divalent to lithium ratio (Mg +2 + Ca +2 to Li + ) were obstacles that must be overcome to extract the lithium. In our previous work, lithium concentrations in the Dead Sea end brine were enriched by chemical precipitation up to 1700 mg/kg in the produced solid precipitate. The obtained precipitate was decomposed by double‐distilled water, and about 66% of lithium was leached, producing an environmental liquor containing an elevated concentration of lithium. A sequential ion exchange technique was used to achieve selective lithium recovery in this study. The ability of the UBK 10 strong acid‐type cation exchange resin (Na type) to remove lithium from simulated and environmental lithium‐bearing solutions was investigated. Because of the complex matrix comprising components that may compete with lithium adsorption, a greater quantity of adsorbent was required to achieve the equilibrium state for the environmental solution (7 g) compared to (3.6 g) for the simulated solution. For both lithium‐bearing solutions, the kinetics investigation revealed a pseudo‐second‐order tendency. The interfering capacity was determined to be 0.405, confirming the UBK 10 challenge to selective lithium adsorption. The divalent to lithium ratio was decreased by more than 50 times, yielding encouraging findings for extracting lithium from the low lithium—high divalent to lithium sophisticated Dead Sea end brines.
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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) | 0.001 | 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".