A Scalable Approach to Synthesize Cobalt-Free LNMO Cathode Materials for High Energy Density Lithium Ion Batteries
Bibliographic record
Abstract
The rapid growing demand for electric vehicles (EVs) has highlighted the need for high-energy-density batteries. The cathode material plays a vital role in achieving this goal[1]. As the limitations of cathode capacity improvement are approached, the pursuit for high-voltage materials becomes a viable option[2]. Lithium nickel manganese oxide LNMO (LiNi0.5Mn1.5O4) stands out due to its remarkable attributes including a substantial reversible capacity, excellent thermal stability, cost-effectiveness, environmental friendliness, and a high energy density[3]. Moreover, its cobalt-free composition aligns with sustainability objectives. Despite extensive research, the large-scale production and deployment of LNMO remain formidable challenges. In this study an industrially applicable co-precipitation method-based scalable synthesis strategy is introduced. The LNMO spheres with controlled various sizes and morphologies were successfully fabricated by adjusting the pH environment during synthesis carefully (Fig. 1A). Scanning electron microscopy (SEM) revealed the presence of uniformly spherical particles with dimensions of appr. 6, 9, and 14 µm and unique morphological characteristics (Fig. 1Ad-f). Synchrotron X-ray diffraction (SXRD) of LNMO samples revealed a deviation from the optimal Ni-to-Mn ratio of 1:3 (Fig. 1B)[4,5]. This variation results from the pH-dependent metal ion precipitation dynamics during synthesis, introducing a fascinating dimension to LNMO material fabrication. As potential cathode materials for lithium-ion batteries, these samples underwent a thorough electrochemical evaluation as part of our research. Among the synthesized LNMO samples, LNMO-9 demonstrated the most promising specific capacity, surpassing approximately 138 mAh/g and working under high voltage of 4.75 V (Fig. 1C). Furthermore, the characterizations of the materials were also thoroughly investigated by a variety of advanced characterization techniques, including Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). This research reveals a strategy for synthesizing LNMO materials with certain regulated features and presents a successful strategy for industrial manufacturing as well. Particularly in the context of the EV industry, this research acquires critical significance in answering the growing demand for high-performance, sustainable energy storage systems. Fig. 1 (A) Illustration of the synthesis process of LNMO-x samples with SEM images of (a‒c) NiMn-x precursors and (d‒f) LNMO-x products. (B) SXRD patterns with Rietveld refinements and (C) charge/discharge profile of LNMO-x samples. Reference [1] H.-H. Ryu, G.-C. Kang, R. Ismoyojati, G.-T. Park, F. Maglia, Y.-K. Sun, Intrinsic weaknesses of Co-free Ni–Mn layered cathodes for electric vehicles, Materials Today. (2022) S1369702122000645. https://doi.org/10.1016/j.mattod.2022.03.005. [2] T. Liu, A. Dai, J. Lu, Y. Yuan, Y. Xiao, L. Yu, M. Li, J. Gim, L. Ma, J. Liu, C. Zhan, L. Li, J. Zheng, Y. Ren, T. Wu, R. Shahbazian-Yassar, J. Wen, F. Pan, K. Amine, Correlation between manganese dissolution and dynamic phase stability in spinel-based lithium-ion battery, Nat Commun. 10 (2019) 4721. https://doi.org/10.1038/s41467-019-12626-3. [3] Y. Xue, L.-L. Zheng, J. Wang, J.-G. Zhou, F.-D. Yu, G.-J. Zhou, Z.-B. Wang, Improving Electrochemical Performance of High-Voltage Spinel LiNi 0.5 Mn 1.5 O 4 Cathode by Cobalt Surface Modification, ACS Appl. Energy Mater. 2 (2019) 2982–2989. https://doi.org/10.1021/acsaem.9b00564. [4] A. Gomez, G. Dina, S. Kycia, The high-energy x-ray diffraction and scattering beamline at the Canadian Light Source, Review of Scientific Instruments. 89 (2018) 063301. https://doi.org/10.1063/1.5017613. [5] H.M. Rietveld, A profile refinement method for nuclear and magnetic structures, J Appl Crystallogr. 2 (1969) 65–71. https://doi.org/10.1107/S0021889869006558. 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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".