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Record W2981622896 · doi:10.1149/ma2019-02/5/291

Investigating the Decomposition of Delithiated LiNi<sub>1-X</sub>M<sub>x</sub>O<sub>2 </sub>(M = Al, Co, Mn or Mg, x = 0 or 0.05)

2019· article· en· W2981622896 on OpenAlexaff
Aaron Liu, Hongyang Li, Ning Zhang, Julie Inglis, Marc M. E. Cormier, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster UniversityDalhousie University
Fundersnot available
KeywordsThermal runawayElectrolyteElectrodeThermal decompositionLithium (medication)Exothermic reactionMaterials scienceIonBattery (electricity)Analytical Chemistry (journal)Chemical engineeringChemistryThermodynamicsPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

As lithium ion battery technology expands into more demanding applications such as electric vehicles, attention has shifted towards nickel-rich positive electrode materials, namely LiNi1-x-yMnxCoyO2 (NMC) and LiNi1-x-yCoxAlyO2 (NCA).1 Aims to improve energy density and reduce costs of NMC and NCA can be achieved by increasing the Ni content of the material, and the compositions of the two materials will invariably converge towards LiNiO2 (LNO). However, there are several issues that LNO and Ni-rich materials experience, chief among them being the thermal instability of the material when delithiated.1 The safety of Li ion batteries is a big concern for manufacturers and consumers. When a short circuit occurs, a large current flows through the short, generating a large amount of heat in a short time. If the cell cannot dissipate the generated heat quickly enough, the cell overheats. As the internal temperature of the cell rises, it triggers several reactions.2 On the negative electrode side, the metastable components of the negative electrode solid electrolyte interphase (SEI) decompose and the intercalated lithium at the negative electrode reacts with the electrolyte. On the positive electrode side, the delithiated positive electrode material decomposes when heated sufficiently. This exothermic reaction releases oxygen, which reacts further with the electrolyte to release more heat, causing more decomposition. This positive feedback loop is known as a thermal runaway, and the temperature and pressure rises uncontrollably. Thermal runaway is the major contributor to Li-ion battery safety incidents, as the rapidly rising temperature and pressure may result in cells catching fire and/or exploding. Understanding the decomposition of delithiated positive electrodes may help reduce the likelihood of thermal runaway. Increasing the decomposition onset temperature may allow reactions at earlier temperatures to burn out before triggering the next set of chain reactions. Additionally, the chain may be broken if the decomposition reactions occur slowly enough that the heat dissipation can offset heat generation. As such, the thermal instability of a candidate positive electrode material is a factor when considering its suitability. Multiple studies have shown that delithiated LNO and Ni-rich derivatives are more thermally unstable than other materials such as LiCoO2 (LCO), LiFePO4 (LFP) and LiMn2O4 (LMO).2–4 The state of charge, or degree of delithiation, also factors into the decomposition temperature.3 Recent work studied the thermal instability of LNO and Al, Mg, Mn or Co doped derivatives.4 It was found that LNO with 5% Co did not reduce the reactivity of the delithiated material with the electrolyte. Conversely, LNO with 5% Al, Mg or Mn all had reduced reactivity, with the Al and Mg doped materials reducing the reactivity the most. It is not certain why some dopants reduce the reactivity of the material with electrolyte, or why 5% dopant concentrations can affect the thermal behavior. In this work, LiNi1-xMxO2 (M = Al, Mg, Mn or Co, x = 0 or 0.05) electrode materials were delithiated and heated to study the decompositions of the materials. Thermogravimetric analysis (TGA) was used to track the mass loss of the decomposition as shown in Figure 1, showing that Al and Mg doped materials have increased thermal stability. X-ray diffraction (XRD) was used to characterize materials decomposed at various temperatures. Delithiated materials were decomposed at various heating rates to study decomposition kinetics. Electrode materials were also delithiated to a lesser degree and decomposed to study the effect of Li content. (1) Xu, J.; Lin, F.; Doeff, M. M.; Tong, W. J. Mater. Chem. A 2017, 5, 874–901. (2) Liu, K.; Liu, Y.; Lin, D.; Pei, A.; Cui, Y. Sci. Adv. 2018, 4, eaas9820(1-11). (3) Dahn, J. R.; Fuller, E. W.; Obrovac, M.; von Sacken, U. Solid State Ionics 1994, 69, 265–270. (4) Li, H.; Cormier, M.; Zhang, N.; Inglis, J.; Li, J.; Dahn, J. R. J. Electrochem. Soc. 2019, 166, A429–A439. Figure 1

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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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.285
Teacher spread0.262 · 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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Published2019
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