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Record W2768168102 · doi:10.1149/2.0401714jes

Synthesis of Single Crystal LiNi<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>O<sub>2</sub>for Lithium Ion Batteries

2017· article· en· W2768168102 on OpenAlexafffund
Jing Li, Hongyang Li, Will Stone, Rochelle Weber, Sunny Hy, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMagna International (Canada)Dalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLithium (medication)Single crystalMaterials scienceSinteringCrystal (programming language)GraphiteElectrochemistryCrystal structureElectrodeNanotechnologyCrystallographyChemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Single crystal Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 materials in NMC532/artificial graphite cells have excellent long term charge-discharge cycle lifetime which greatly exceeds that of conventional NMC532 materials. There are a few patents from industry regarding the synthesis of single crystal NMC. In addition, there have only been a few reports in the academic literature showing that single crystal NMC with a grain size of ∼2–5 μm having good electrochemical performance was successfully synthesized, but these workers used complex approaches. This work systematically studies the steps required to synthesize single crystal NMC materials. The key synthesis steps including the impact of the Li to transition metal ratio, sintering temperature, precursor size and sintering time are discussed. This work provides guidance for the synthesis of single crystal NMC positive electrode materials that may be suitable for lithium-ion cells with high energy density and long lifetime.

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)0.000
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 score0.001
Threshold uncertainty score0.004

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)0.0010.001

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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".

Quick stats

Citations199
Published2017
Admission routes2
Has abstractyes

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Same venueJournal of The Electrochemical SocietySame topicAdvancements in Battery MaterialsFrench-language works237,207