Application of Tetraethylsulfamide (TES) As a Cathode Additive in Cylindrical Cells
Bibliographic record
Abstract
Recently, sulfonamides have been shown to be promising electrolyte components due to their high chemical and electrochemical stability in lithium batteries [1, 2]. The electrolyte stability becomes critical when applying high voltage and/or utilizing Ni-rich layered oxides in high energy density lithium-ion batteries. Another approach to successful Ni-rich cathode performance is to develop a stable and effective cathode electrolyte interphase (CEI). Given the success of sultones and sulfates in this regard [3, 4], it is hypothesized that nitrogen analogs, like sulfonamides, could be tailored to provide a similar benefit. Indeed, Yim et al. [5, 6] have shown that N,N,N’,N’-tetraethylsulfamide (TES) forms a CEI on NMC811 that imparts high voltage cycling stability and less cathode corrosion. Our earlier studies of TES with Ni-rich NCA also formed a favorable CEI and these results are the topic of this presentation. Herein, we examine the performance of 0 - 4 wt.% TES in our commercially available, high power INR18650-P28A. These cells contain a composite SiO/graphite anode in addition to a Ni-rich cathode. As shown in Fig 1, TES significantly decreased the impedance of the cathode interface after conditioning compared to the control electrolyte. Thereafter, cells containing up to 2%TES show improved capacity retention during long-term high-rate cycling (+1C/-80W). Part of this success was due to a suppression of resistance growth during cycling by TES. Fast charge cycling (+3C/-2C), however, was moderately impaired with increased TES. Considering the largely reduced impedance of the cathode, fast-charge performance may have suffered due to anode rate limitations. These results will be discussed as well as gas generation, storage performance, and additional rate and cycling tests. [1] Shuting Feng, Mingjun Huang, Jessica R. Lamb, Wenxu Zhang, Ryoichi Tatara, Yirui Zhang, Yun Guang Zhu, Collin F. Perkinson, Jeremiah A. Johnson, Yang Shao-Horn. Chem, 5, 2630-2641 (2019) [2] Weijiang Xue, Mingjun Huang, Yutao Li, Yun Guang Zhu, Rui Gao, Xianghui Xiao, Wenxu Zhang, Sipei Li, Guiyin Xu, Yang Yu, Peng Li, Jeffrey Lopez, Daiwei Yu, Yanhao Dong, Weiwei Fan, Zhe Shi, Rui Xiong, Cheng-Jun Sun, Inhui Hwang, Wah-Keat Lee, Yang Shao-Horn, Jeremiah A. Johnson, Ju Li. Nature Energy, 6, 495-505 (2021) [3] Koji Abe, Manuel Colera, Kei Shimamoto, Masahide Kondo, Kazuhiro Miyoshi. Journal of Electrochemical Society, 161 (6) A863-A870 (2014) [4] Jian Xia, N. N. Sinha, L. P. Chen, J. R. Dahn. Journal of Electrochemical Society, 161 (3) A264-A274 (2014) [5] Kwangeun Jung, Taeeun Yim. Journal of Alloys and Compounds, 834,155155 (2020) [6] Ji Won Kim, Kwangeun Jung, Taeeun Yim. Journal of Mater. Sci & Tech. 86, 70-76 (2021) 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 | 0.001 | 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".