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Record W2981478793 · doi:10.1007/s41918-019-00053-3

Degradation Mechanisms and Mitigation Strategies of Nickel-Rich NMC-Based Lithium-Ion Batteries

2019· article· en· W2981478793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueElectrochemical Energy Reviews · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Research Council CanadaUniversity of Victoria
FundersNational Research Council Canada
KeywordsElectrolyteGraphiteAnodeMaterials scienceLithium (medication)CathodeElectrochemistryDegradation (telecommunications)NickelChemical engineeringNanotechnologyComputer scienceElectrodeMetallurgyChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The demand for lithium-ion batteries (LIBs) with high mass-specific capacities, high rate capabilities and long-term cyclabilities is driving the research and development of LIBs with nickel-rich NMC (LiNi x Mn y Co 1− x − y O 2 , $$x \geqslant 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>x</mml:mi><mml:mo>⩾</mml:mo><mml:mn>0.5</mml:mn></mml:mrow></mml:math> ) cathodes and graphite (Li x C 6 ) anodes. Based on this, this review will summarize recently reported and widely recognized studies of the degradation mechanisms of Ni-rich NMC cathodes and graphite anodes. And with a broad collection of proposed mechanisms on both atomic and micrometer scales, this review can supplement previous degradation studies of Ni-rich NMC batteries. In addition, this review will categorize advanced mitigation strategies for both electrodes based on different modifications in which Ni-rich NMC cathode improvement strategies involve dopants, gradient layers, surface coatings, carbon matrixes and advanced synthesis methods, whereas graphite anode improvement strategies involve surface coatings, charge/discharge protocols and electrolyte volume estimations. Electrolyte components that can facilitate the stabilization of anodic solid electrolyte interfaces are also reviewed, and trade-offs between modification techniques as well as controversies are discussed for a deeper understanding of the mitigation strategies of Ni-rich NMC/graphite LIBs. Furthermore, this review will present various physical and electrochemical diagnostic tools that are vital in the elucidation of degradation mechanisms during operation to supplement future degradation studies. Finally, this review will summarize current research focuses and propose future research directions. Graphic Abstract The demand for lithium-ion batteries (LIBs) with high mass specific capacities, high rate capabilities and longterm cyclabilities is driving the research and development of LIBs with nickel-rich NMC (LiNi x Mn y Co 1− x − y O 2 , x ≥ 0.5) cathodes and graphite (Li x C 6 ) anodes. Based on this, this review will summarize recently reported and widely recognized studies of the degradation mechanisms of Ni-rich NMC cathodes and graphite anodes. And with a broad collection of proposed mechanisms on both atomic and micrometer scales, this review can supplement previous degradation studies of Ni-rich NMC batteries. In addition, this review will categorize advanced mitigation strategies for both electrodes based on different modifications in which Ni-rich NMC cathode improvement strategies involve dopants, gradient layers, surface coatings, carbon matrixes and advanced synthesis methods, whereas graphite anode improvement strategies involve surface coatings, charge/discharge protocols and electrolyte volume estimations. Electrolyte components that can facilitate the stabilization of anodic solid-electrolyte interfaces (SEIs) are also reviewed and tradeoffs between modification techniques as well as controversies are discussed for a deeper understanding of the mitigation strategies of Ni-rich NMC/graphite LIBs. Furthermore, this review will present various physical and electrochemical diagnostic tools that are vital in the elucidation of degradation mechanisms during operation to supplement future degradation studies. Finally, this review will summarize current research focuses and propose future research directions.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.213
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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