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Record W3028309092 · doi:10.1149/ma2019-03/2/196

Is Cobalt Needed in Ni-Rich Positive Electrode Materials for Lithium Ion Batteries?

2019· article· en· W3028309092 on OpenAlexaff
Hongyang Li, Marc M. E. Cormier, Ning Zhang, Julie Inglis, Jing Li, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcMaster UniversityDalhousie University
Fundersnot available
KeywordsCobaltLithium (medication)Materials scienceMetalThermal stabilityPhase (matter)MetallurgyChemical engineeringChemistry

Abstract

fetched live from OpenAlex

LiNi1-x-yCoxAlyO2 (NCA) was developed from LiNiO2 by partially substituting Ni with Co and Al, and it has been successfully commercialized and used in electric vehicles by Panasonic and Tesla, respectively. Because Co has a relatively high price of $29.98 USD/lb as of Aug. 3, 2018, while the prices of Ni and Al are only $6.00 and $0.92 USD/lb, respectively, as reported on InfoMine (http://www.infomine.com/investment/metal-prices), reducing the Co content in NCA materials has become a priority.1 , 2 For NCA materials, substitution of Al for Ni was shown to improve the thermal stability and safety.3,4 Partial replacement of Ni with Co was thought to improve structural stability by hindering the mixing between Ni2+ and Li+ 5,6, and suppressing the multiple phase transitions during charge and discharge. Partially substituting Ni with other elements such as Mn and Mg has been investigated as well.7 , 8 However, it is hard to make a head to head comparison between the different substituents because of various synthesis conditions and analysis methods chosen by different researchers. With the increasing demand for reducing Co content, it is important to go “back to basics” and systematically study the impact of different cation substitutions. In this work, cations including Al, Co, Mn, and Mg, were selected for investigations. LixNi1-nMnO2 (M=Al, Co, Mn or Mg, n=0.05 or 0.1) were synthesized and studied with differential capacity versus voltage (dQ/dV vs. V) methods. In-situ X-ray diffraction (XRD) measurements were carried out on selected samples, and the unit cell parameters and unit cell volumes were carefully measured versus x. Figure 1a shows the clipped dQ/dV vs. V of LiNiO2, in which the peaks corresponding to the phase transitions have been circled. Figures 1b – 1d show that 5% Al, 5% Mn, or 5% Mg substitutions diminish these dQ/dV vs. V peaks, suggesting an effective suppression of the multiple phase transitions observed in LiNiO2. Figure 1e shows dQ/dV vs. V of 5% Co substitution, and it shows almost identical peak features as LiNiO2 (Figure 1a), indicating the existence of multiple phase transitions. However, the dQ/dV curves for the 5% Co and LiNiO2 samples are partially off-scale in Figures 1e and 1a, respectively. Therefore, Figures 1f – 1g show the same dQ/dV vs. V with a larger y-axis scale. Figures 1f and 1j show that both LiNiO2 and LiNi0.95Co0.05O2 have similar sharp and intense dQ/dV peaks compared to the other samples. The conclusions from dQ/dV vs. V analysis were supported by in-situ XRD measurements. The studies on 5% and 10% cation doped series showed trends in the changes in material structure and specific capacities. Based on the observed trends, a preliminary theory of how the various cations impact LiNi1-xMxO2 has been developed and will be reported. References Y. K. Sun, D. J. Lee, Y. J. Lee, Z. Chen, and S. T. Myung, ACS Appl. Mater. Interfaces, 5, 11434–11440 (2013). K. Ghatak, S. Basu, T. Das, V. Sharma, H. Kumar, and D. Datta, Phys. Chem. Chem. Phys., 120, 22805-22817 (2018). T. Ohzuku, A. Ueda, and M. Kouguchi, J. Electrochem. Soc., 142, 4033–4039 (1995). M. Guilmard, A. Rougier, M. Grüne, L. Croguennec, and C. Delmas, J. Power Sources, 115, 305–314 (2003). S. T. Myung, F. Maglia, K. J. Park, C. S. Yoon, P. Lamp, S. J. Kim, and Y. K. Sun, ACS Energy Lett., 2, 196–223 (2017). C. Delmas, I. Saadoune, and A. Rougier, J. Power Sources, 44, 595–602 (1993). H. Arai, S. Okada, Y. Sakurai, and J. Yamaki, J. Electrochem. Soc. , 144 , 3117–3125 (1997). C. C. Chang, J. Y. Kim, and P. N. Kumta, J. Electrochem. Soc., 147, 1722–1729 (2000). Figure 1. Cell voltage as a function of specific capacity (V vs. Q) of LiNiO2 (A), LiNi0.95Al0.05O2 (B), LiNi0.95Mn0.05O2 (C), LiNi0.95Mg0.05O2 (D), and LiNi0.95Co0.05O2 (E); Differential capacity as a function of cell voltage (dQ/dV vs. V) of 2nd charge and discharge of LiNiO2 (a), LiNi0.95Al0.05 O2 (b), LiNi0.95Mn0.05O2 (c), LiNi0.95Mg0.05O2 (d), and LiNi0.95Co0.05O2 (e); The same dQ/dV vs. V curves with larger Y axis scale (f – j). Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.246
Teacher spread0.236 · 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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Citations8
Published2019
Admission routes1
Has abstractyes

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