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Record W3113591955 · doi:10.1149/ma2020-02453811mtgabs

Understanding the High-Voltage Cycling Behavior of Ni-Rich Cathode

2020· article· en· W3113591955 on OpenAlexaff
Hanshuo Liu, Zhong Xie, Wei Qu

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCathodeMaterials scienceVoltageHigh voltageCyclingElectrodePower densityEngineering physicsNanotechnologyElectrical engineeringPower (physics)ChemistryEngineering

Abstract

fetched live from OpenAlex

Nowadays, lithium-ion batteries (LIBs) have been widely used in the automotive industry as power sources for electric vehicles (EVs). Nevertheless, the rapid development of EVs requires LIBs to enable higher energy density, enhanced safety, lower cost and longer cycle life. Ni-rich LiNixMnyCo1-x-yO2 (NMC) layered oxides have attracted great attention due to their high specific capacity (≈ 200 mAh/g), which are promising cathode materials for high-energy-density LIBs. The capability of high-voltage operation of the cathode can largely influence the energy density of LIB. Whereas, it is reported that Ni-rich cathode materials are unstable at high-voltage cycling conditions, leading to severe performance deterioration. (1, 2) Attempts have been made in developing synthesis and modification methods of Ni-rich cathode materials in order to improve their high-voltage performance.(3, 4) However, the design of better materials relies intensively on the deep understanding of the cycling behavior of cathode. There are a few studies looking into the degradation process of Ni-rich cathodes upon high-voltage cycling. Most of the work mainly focuses on investigating structural changes of the active material, for example, phase transformation at the surface,(2) crack generation within active particles.(5) Whereas, less attention was paid to the evolutions within the whole electrode. Herein, we study the cycling behavior of LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode at high cut-off voltages and look into the structural evolutions of cathode. In this work, Ni-rich NMC811 cathodes were cycled at different upper cut-off voltages to investigate their charge-discharge behavior under high-voltage cycling conditions. The NMC811 cathodes were assembled into coin cells with Li foil as counter electrode. The cells were cycled under different upper cut-off voltages of 4.3V, 4.6V and 4.8V with a C/10 cycling rate. Electrochemical impedance spectroscopy (EIS) measurements were conducted to analyze the ionic conduction and charge transfer at the electrolyte/particle interface of coin cells cycled at different cut-off voltages. The microstructure of pristine and cycled NMC811 cathodes were characterized in order to understand the structural evolution among different phases during high-voltage cycling. Our results show that high cut-off voltages have a negative influence on cell performance. The specific capacity of the cells cycled at 4.6V upper cut-off voltage showed a faster decrease compared to the 4.3V cells. In addition, more severe voltage decay is observed from cells cycled at 3.0V-4.6V. Whereas, when increasing the upper cut-off voltage from 4.6V to 4.8V, the cells exhibited less pronounced voltage decay, comparing to the difference observed between 4.3V and 4.6V cells. Significant increases in the charge transfer resistance (Rct) of cells cycling under higher voltages were observed from EIS measurements, which may indicate an elevated irreversible phase transformation of the active materials under high voltage cycling.(2) The microstructural evolution of NMC811 cathodes were observed under higher cycling voltages which could contribute to the poor performance of the high-voltage cells. The different behaviors of NMC811 cathode at high-voltages were analyzed in order to understand the degradation mechanisms of NMC811 cathodes under increasing cycling voltages. References: J. Li, L. E. Downie, L. Ma, W. Qiu and J. R. Dahn, Journal of The Electrochemical Society, 162, A1401 (2015). S.-K. Jung, H. Gwon, J. Hong, K.-Y. Park, D.-H. Seo, H. Kim, J. Hyun, W. Yang and K. Kang, Advanced Energy Materials, 4 (2014). X. Dong, J. Yao, W. Zhu, X. Huang, X. Kuai, J. Tang, X. Li, S. Dai, L. Shen, R. Yang, L. Gao and J. Zhao, Journal of Materials Chemistry A, 7, 20262 (2019). J. Zhang, J. Zhang, X. Ou, C. Wang, C. Peng and B. Zhang, ACS Appl Mater Interfaces, 11, 15507 (2019). Y. Mao, X. Wang, S. Xia, K. Zhang, C. Wei, S. Bak, Z. Shadike, X. Liu, Y. Yang, R. Xu, P. Pianetta, S. Ermon, E. Stavitski, K. Zhao, Z. Xu, F. Lin, X. Q. Yang, E. Hu and Y. Liu, Advanced Functional Materials, 29 (2019).

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.078
GPT teacher head0.286
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Published2020
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