Preparation of doped spinel LiMn2O4 cathode using α-MnO2 for high-performance Li-ion batteries
Bibliographic record
Abstract
The spinel LiMn2O4 (LMO) has become one of the most promising candidate cathode materials for lithium-ion batteries (LIBs). This is due to its low cost, resulting from the earth-abundant manganese (Mn). However, LMO often shows low robustness in terms of cycle life. Its activity is also irreversible due to Mn dissolution, Jahn–Teller distortion, and phase changes that normally occur within its crystal lattice. In this work, to enhance the electrochemical performance of LIBs, spinel LiMn2O4 nanorods and LiMn2O4 doped with Al, Co, and Fe (LiAl0.1Mn1.9O4, LiCo0.1Mn1.9O4, and LiFe0.1Mn1.9O4, respectively) were synthesized, characterized, and electrochemically investigated. These materials were obtained from the as-synthesized λ-MnO2 nanorod precursor using hydrothermal method followed by a heat treatment process. Among them, LiAl0.1Mn1.9O4 was further studied as it showed better electrochemical behaviors than its LiCo0.1Mn1.9O4 and LiFe0.1Mn1.9O4 counterparts. The LiAl0.1Mn1.9O4 synthesized at 800°C delivered a discharge-specific capacity of 106 mAh g−1 for the first cycle and 105 mAh g−1 after 200 cycles at 1C under ambient temperature. On the other hand, LiCo0.1Mn1.9O4 and LiFe0.1Mn1.9O4 delivered a discharge capacity of 92 and 82 mAh g−1, respectively, after 200 cycles at 1C under room temperature. The excellent cycling stability of LiAl0.1Mn1.9O4 could be attributed to the partial substitution of Mn3+ by Al3+ ions, leading to a reduction in lattice constant. This, in turn, increased the electron conductivity of spinel LiMn2O4 at 800°C. Our research findings shed light on facile doping strategies of spinel LiMn2O4 cathode material from LIB λ-MnO2 precursor, aiming to enhance capacity and cycling stability.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".