Adsorption of lithium ions on lithium‐aluminum hydroxides: Equilibrium and kinetics
Bibliographic record
Abstract
Abstract The rapid development of rechargeable lithium batteries has promoted the demand of primary lithium products obtained from lithium‐bearing resources, especially salt lakes. Layered lithium‐aluminum hydroxides connecting with ion exchange resin were used for the adsorption of lithium ions from aqueous resources. Batch experiments were conducted to determine the effects of pH, initial lithium concentration, and contact time on lithium adsorption. The optimal conditions for lithium adsorption were found to be pH = 7, and the equilibrium time is approximately 600 minutes. The selectivity experiment indicated that the adsorbent showed selectivity toward lithium ion, so the adsorbent could be used in the separation of lithium ion with other metal ions, especially the divalent magnesium ions. The experiment showed that the existence of the magnesium chloride enhanced the lithium adsorption onto the adsorbent greatly. The kinetic data were analyzed by several kinetic models, and the best result was achieved with a pseudo‐second‐order model. The commonly used adsorption isotherms were used to fit the experimental data by nonlinear regression. Both Langmuir and Temkin isotherm models could describe the isotherm well. The thermodynamic parameters ( ΔG , ΔS , and ΔH ) were also calculated subsequently and the results showed the lithium adsorption process is exothermic with the decrease of randomness. Breakthrough curves demonstrated the cyclic stability of the adsorbent and the influence of the feed flow rate. Lithium ions were effectively adsorbed from the aqueous solution by the adsorbent, demonstrating its feasibility for lithium recovery and providing the fundamental data for further column design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".