Electrochemical and adsorption behaviour of Li<sup>+</sup>, Na<sup>+</sup>, K<sup>+</sup>, Ca<sup>2+</sup>, and Mg<sup>2+</sup> in LiMn<sub>2</sub>O<sub>4</sub>/<i>λ</i>‐MnO<sub>2</sub> structures
Bibliographic record
Abstract
Abstract Manganese dioxide ion‐sieves are known to be highly efficient lithium adsorbents, owing to their high adsorption capacity and selectivity. However, their manganese dissolution rate during acid desorption is high. Therefore, electrically switched ion exchange (ESIX) was proposed to recover Li+ from brine. Cyclic voltammetry results indicated that ESIX could be applied to capture lithium from the Li+‐containing solutions. Besides, the electrochemical behaviour of Li+ in LiMn2O4/λ‐MnO2 structures was studied, wherein Na+, K+, Ca2+, and Mg2+ naturally coexisted with Li+ in the brine. No redox peaks were observed between 0.3–1.2 V in the MCl solution (MCl = NaCl, KCl, CaCl2, and MgCl2), and the positions of the redox peaks in the LiCl solution were similar to those in the LiCl and MCl mixed solution, indicating that Na+, K+, Ca2+, and Mg2+ barely interacted with the λ‐MnO2 electrode under the experimental conditions. Based on the experimental results, the Li+ adsorption capacity was determined to be ∼2 mmol · g−1, and the selectivity coefficients of Li+ for Na+, K+, Ca2+, and Mg2+ were 38.78, 35.63, 29.04, and 120.08, respectively. Furthermore, by using ESIX, the Li+ adsorption capacity of the λ‐MnO2 electrode was 82.8 % of its initial value after 50 adsorption‐desorption cycles. Thus, we concluded that ESIX involving an λ‐MnO2 electrode can be used to separate Li+ from brine with excellent selectivity coefficients.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".