Polyaniline Covered Lithium Mangenues Oxide As High-Performance Active Material Designed for High Capacity and Selectivity Capacitive Deionization Lithium Extraction from Brine
Bibliographic record
Abstract
Selective lithium extraction from brines has emerged as a crucial technology for addressing the global lithium shortage and meeting increasing demand. Capacitive deionization (CDI), with low energy consumption, simple operation, environmentally friendly, high selectivity, and strong recyclability, is considered a new tech for efficiently extracting lithium resources from low-concentration brine and has broad technical prospects. LiMn₂O₄ (LMO) is an effective redox material for lithium recovery in CDI due to high capacity and abundancy. However, the inevitable dissolution of Mn in aqueous solutions during adsorption and desorption of the redox process results in significant capacity degradation and reduced cycle performance. This study uses a facile method to wrap the LMO with conductive polymer polyaniline (PN), forming passivation layers that improve the Mn dissolution problem and advance surface adsorption kinetics. In addition, the composite material shows high selectivity and better charge transfer efficiency, allowing ion removal without ion-exchange membranes. As a result, the optimized composite demonstrates an exceptional Li+ adsorption capacity of 23.40 mg/g with a minimum Mn dissolution rate of 0.019 wt% per cycle and performs higher capacity and lower Mn dissolution rate compared to bare LMO (capacity only 18.19 mg/g). The CDI achieves high charge efficiency (77.80 %) and fast adsorption rate (the adsorption process can be stabilized in 20 minutes). Moreover, the energy consumption of the designed CDI cell is relatively low, only 1.54 Wh/mol Li+ or 2.96 Wh/g. Thus, this high selectivity and efficiency, coupled with long cycle life CDI cell, is promising for extracting lithium ions from lithium-containing solutions. Figure 1
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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".