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
Why this work is in the frame
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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 LiMn 2 O 4 / λ ‐MnO 2 structures was studied, wherein Na + , K + , Ca 2+ , and Mg 2+ 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, CaCl 2 , and MgCl 2 ), 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 + , Ca 2+ , and Mg 2+ barely interacted with the λ ‐MnO 2 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 + , Ca 2+ , and Mg 2+ were 38.78, 35.63, 29.04, and 120.08, respectively. Furthermore, by using ESIX, the Li + adsorption capacity of the λ ‐MnO 2 electrode was 82.8 % of its initial value after 50 adsorption‐desorption cycles. Thus, we concluded that ESIX involving an λ ‐MnO 2 electrode can be used to separate Li + from brine with excellent selectivity coefficients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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 it