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Record W2307173150 · doi:10.1149/ma2014-04/2/246

Characterization and Development of High Energy Density & Lifetime Li-Ion Cathode Materials Via Core-Shell Structured Cathodes

2014· article· en· W2307173150 on OpenAlexaff
John Camardese, Eric McCalla, Aaron Rowe, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCathodeElectrolyteMaterials scienceBattery (electricity)Faraday efficiencyLithium (medication)OxideIonChemical engineeringNanotechnologyChemistryElectrodeMetallurgyPhysical chemistryThermodynamicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Current state of the art cathode materials such as LiCoO2, Li[NixMnxCo(1-2x)]O2 or Li[Li(1/3-2x)Ni(x)Mn(2/3-x/3)]O2 ,0≤x≤½, cannot individually meet the needs of next generation cathodes for lithium-ion battery applications. New electrolyte systems or new cathode chemistries beyond the traditional layered oxides have been extensively researched to meet the demands of next generation materials. Improving electrolytes with additives has been shown to improve lifetime, but fail to address energy density, while new high potential cathode chemistries exhibit the possibility of increased energy density, but at the expense of the electrolyte and lifetime. A complementary approach to electrolyte additives and high potential cathodes is core-shell cathodes using the extensively studied library of layered oxides as candidates for the core and shell. For high energy density the core of a core-shell material has superior energy density. Often, high energy density materials exhibit extensive parasitic reactions with the electrolyte, reducing lifetime. These parasitic reactions might be greatly reduced by encapsulating the core in different cathode compositions that show less parasitic reactions with the electrolyte. The extent of the parasitic reactions on layered oxides was previously quantified with high precision chargers (HPC) providing precise measurements of coulombic efficiency (CE). Figure 1 shows the CE and discharge capacity versus cycle number for many common layered oxide cathode materials previously measured with HPC.1–3 This data was used to screen for candidate materials for the core and shell of core-shell cathodes. The effect of the shell thickness was examined in this study. Optimally, the shell coating should be as thin as possible. If the shell of the particles is too thick then energy density is sacrificed; alternatively, if the shell coverage is thin and incomplete then parasitic reactions between the electrolyte and the core can occur, reducing lifetime. Cathode materials were developed in a standard two-step process.4 The thickness of the shell in the precursor materials was controlled by altering the precipitation time for the core and shell. The thickness of the shell was determined by modeling the absorption of X-rays by the shell coating observed in the diffraction patterns of core-shell materials.5 Figure 2 shows precursors with a Ni0.5Mn0.5(OH)2 core and Ni0.17Mn0.83(OH)2shell developed with a core to shell mole ratio of 1:1 The shell thickness was determined to be 1.77 μm for a 7.9 μm diameter particle. The precursors were developed into core-shell cathode materials via calcination at various temperatures, times and lithium content. Discussion will include electrochemical results of coin cells utilizing core-shell cathodes and standard electrolyte formulations tested using HPC. XRD, SEM and EDS results showing the effects of high temperature calcination on the morphology and core and shell composition will also be presented to show the effects of calcination on the core and shell of the cathode materials. References 1. A. J. Smith, J. C. Burns, D. Xiong, and J. R. Dahn, J. Electrochem. Soc., 158, A1136 (2011). 2. A. J. Smith, J. C. Burns, S. Trussler, and J. R. Dahn, J. Electrochem. Soc., 157, A196 (2010). 3. Aaron W. Rowe, Eric McCalla, John Camardese, and Jeff R. Dahn, J. Electrochem. Soc.(Submitted). 4. John Camardese, Eric McCalla, Daniel W. Abarbanel, and Jeff R. Dahn, Chem. Mater.(to be Submitted). 5. John Camardese, Eric McCalla, Daniel W. Abarbanel, and Jeff R. Dahn, Chem. Mater. (to be Submitted).

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 score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
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 head0.014
GPT teacher head0.223
Teacher spread0.208 · 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
Published2014
Admission routes1
Has abstractyes

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