Improved Electron/Li-Ion Transport and Oxygen Stability of Mo-Doped Li<sub>2</sub>MnO<sub>3</sub>
Bibliographic record
Abstract
Li2MnO3 plays significant roles in stabilizing the structure and ensuring a high specific lithium storage capacity of the Li-rich layered xLi2MnO3·(1-x)LiMO2 (M = Ni, Co, Mn, etc.) composites (or solid solutions) as cathode materials for lithium ion batteries (LIBs)1. Nevertheless, it meets challenges in irreversible structural transition and low coulombic efficiency due to the loss of oxygen in the initial charge process. In addition, its insulating property deteriorates the rate performance of the xLi2MnO3·(1-x)LiMO2. These drawbacks hinder the commercialization and application of the composites as cathode materials for LIBs. Elemental substitution or atom doping has been commonly applied to improve the electrochemical performances of Li2MnO3 2-4. In an electrochemical viewpoint, as Mn4+ cannot be further oxidized in octahedral coordination in the pristine Li2MnO3, the charge compensation has to be conducted by the oxidation of the O2- ions alone upon Li-ion extraction, leading to irreversible structural change and safety issues. In contrast, as Mo4+ is potentially oxidized to Mo6+, the charge compensation will be largely declined from oxygen if multi-electron transfer occurs on the doped transition metal atoms in Li2Mn1-x Mo x O3. Therefore, substituting Mn with Mo is expected to reduce the loss of oxygen and improve the stability of the structure. In addition, considering the difference of Li2MnO3 (red) and its iso-structured Li2MoO3 (black) in color, Mo substitution for Mn might also improve the conductivity and rate performance of Li2MnO3and its related cathode materials. In this work, first-principles calculations are conducted to predict the impacts of molybdenum (Mo) doping on the physical and electrochemical properties of Li2MnO3. It demonstrates that Mo doping is indeed beneficial in improving both the dynamic and thermodynamic properties of Li2MnO3: (1) It decreases the band gap and increases the number of states around the Fermi level; (2) It enhances Li-ion diffusion especially between the lithium layer and the transition-metal layer; (3) Extra charge is transferred from Mo to O, accompanied with the reduction of the delithiation potential; (4) The charge of the removed Li-ion is compensated by both Mo and O in the Mo-doped Li2MnO3 during delithiation, and (5) Mo doping delays oxygen release and reinforces the stability of the structural oxygen based on the calculated reaction enthalpy. Therefore, Mo doping is expected to be an effective way in improving the structural stability and rate performance of Li2MnO3 and xLi2MnO3·(1-x)LiMO2 cathode materials. These findings will facilitate the investigation and promote the development of Li2MnO3 and xLi2MnO3·(1-x)LiMO2 cathode materials. M. M. Thackeray, S.-H. Kang, C. S. Johnson, J. T. Vaughey, R. Benedek and S. A. Hackney, J. Mater. Chem., 17, 3112-3125 (2007). M. Tabuchi, Y. Nabeshima, K. Ado, M. Shikano, H. Kageyama and K. Tatsumi, J. Power Sources, 174, 554-559 (2007). D. Mori, H. Sakaebe, M. Shikano, H. Kojitani, K. Tatsumi and Y. Inaguma, J. Power Sources, 196, 6934-6938 (2011). S. Kim, J.-K. Noh, S. Yu, W. Chang, K. Y. Chung and B.-W. Cho, J. Electroceram., 30, 159-165 (2013).
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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".