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Record W2390522995 · doi:10.1149/ma2015-03/2/424

Lithium-Rich Core-Shell Cathodes with Low Irreversible Capacity and Mitigated Voltage Fade

2015· article· en· W2390522995 on OpenAlexaff
Jing Li, John Camardese, Ramesh Shunmugasundaram, Stephen Glazier, Zhonghua Lu, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceElectrolyteHydroxideLithium (medication)Metal hydroxideThermal stabilityOxideElectrochemistryTransition metalChemical engineeringAnalytical Chemistry (journal)Inorganic chemistryChemistryMetallurgyElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Lithium-rich layered Ni-Mn-Co oxide materials have been intensely studied in the last decade. Mn-rich materials have serious voltage fade issues and the Ni-rich materials have poor thermal stability and readily oxidize the organic carbonate-based electrolyte.1 Core-shell (CS) strategies that use Ni-rich material as the core and Mn-rich materials as the shell can balance the pros and cons of these materials in a hybrid system.2 The lithium-rich CS materials synthesized through co-precipitation method introduced here showed much improved overall electrochemical performance compared to the core-only (Ni-rich) and shell-only (Mn-rich) samples. In this work, CS samples with 67 mol% of core with the composition Li1+x(Ni0.67Mn0.33)1-xO2 (C) and 33 mol% of shell with the compositions Li1+x(Ni0.2Mn0.6Co0.2)1-xO2 (S1) or Li1+x(Ni0.4Mn0.5Co0.1)1-xO2 (S2) with varied lithium content were synthesized and studied. Figure 1 shows the energy dispersive spectroscopy results of the CS precursor and lithiated samples. Figure 1 shows that there was diffusion of transition metals between the core and shell phases after sintering at 900oC compared to the prepared hydroxide precursors. A Mn-rich shell was still maintained whereas the Co which was only in the shell in the precursor was approximately homogeneous throughout the particles. The CS samples with optimal lithium content showed low irreversible capacity (IRC), as well as high capacity and excellent capacity retention. Sample CS2-3 had a reversible capacity of ~218 mAh/g with 12.3% (~30 mAh/g) IRC and 98% capacity retention after 40 cycles to 4.6 V at 30oC at a rate of ~C/20. Figure 2 shows the average discharge potential (a) and the difference between the average charge and discharge potentials, delta V (b), of cells with the core-only (C), CS and shell-only (S) samples. Figure 2 shows that cells of the CS samples have stable impedance (delta V is stable) as well as a very stable average voltage as compared to cells of the core-only and shell-only samples. Ultra-high precision coulometry (UHPC) measurements confirmed that the CS samples with optimal lithium content had distinctively better columbic efficiency. Apparently, the Mn-rich shell can effectively protect the Ni-rich core from reactions with the electrolyte while the Ni-rich core renders a high and stable average voltage. Reference 1. E.-J. Lee, H.-J. Noh, C. S. Yoon, and Y.-K. Sun, J. Power Sources, 273, 663–669 (2015). 2. J. Camardese, J. Li, D. W. Abarbanel, a. T. B. Wright, and J. R. Dahn, J. Electrochem. Soc., 162, A269–A277 (2014). 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)0.000
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 communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.237
Teacher spread0.207 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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