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Record W2255566575 · doi:10.1149/ma2015-02/6/489

Influence of Core and Shell Properties in Core-Shell Positive Electrode Materials for Li Ion Batteries

2015· article· en· W2255566575 on OpenAlexaff
Rajalakshmi Senthil Arumugam, Ramesh Shunmugasundaram, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteShell (structure)Core (optical fiber)Materials scienceElectrochemistryCoprecipitationIonChemical engineeringElectrodeChemistryComposite materialInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Introduction Core-shell materials with a Ni-rich core and a Mn-rich shell are possible next generation positive electrodes for Li-ion cells. A Ni-rich core can deliver high energy density and a Mn-rich shell can minimize the electrolyte oxidation. For example, Jing Li et al. have recently developed core-shell material that exhibits high reversible capacity, low irreversible capacity loss as well as reduced voltage fade1. This study examines the contribution from core and shell materials to the resultant electrochemical properties of the core-shell material. Synthesis Core-shell transition metal carbonate precursors were made using coprecipitation in a continuously stirred tank reactor (CSTR). Appropriate temperature and pH conditions were established for the core and shell precipitations. The core was precipitated first and then shell was precipitated subsequently on the surface of the core. Then appropriate amounts of Li2CO3were mixed with the core-shell precursors and fired at high temperature to yield the final product. Results and Discussion The compositions of the materials selected for the core or shell were: A - Ni0.166Mn0.5Co0.333CO3 and H - Ni0.4Mn0.5Co0.1CO3. Two core-shell precursors (CS-AH and CS-HA) with 80:20 core to shell ratio were synthesized using A and H. In CS-AH, precursor A is the core and H is the shell whereas in CS-HA the core and shell compositions were interchanged. Figures 1a and 1c show SEM images of CS-AH and CS-HA respectively and Figures 1b and 1d show cross-sectional EDS elemental maps. In Figures 1b and 1d, the bluish-red and bluish-green regions indicate the Co-rich and Ni-rich phases. LiMO2type materials were synthesized from A, H, CS-AH and CS-HA precursors and they were labelled as A2, H1, CS-A2H1 and CS-H1A2. Figure 2 shows the first cycle charge-discharge profiles of the samples in coin type cells. Sample A2, which was synthesized by following reference 2, exhibits a relatively low IRC (9.25%) compared to sample H1 (20.6%). For sample CS-A2H1, which has 80% A2 core and 20% H1 shell, the IRC was 10.8%. This IRC value is approximately the linear combination of the individual IRCs obtained from A2 and H1. Similarly, sample CS-H1A2, that has 80% H1 core and 20% A2 shell exhibits an IRC of 17.7%. Conclusion Core-shell positive electrode materials are promising due to the complementary benefits from the core and shell properties. Two core-shell type positive electrode materials with core and shell compositions interchanged from one another were synthesized and compared with core and shell only materials. The results suggest that the IRC of core-shell materials are simply the linear combination of those of the core and shell only values. Other electrochemical results, in particular, ultra high precision coulometry, to probe the impact of the shell phase on electrolyte oxidation, will be discussed in detail. References: Li, Jing; Camardese, John; Shunmugasundaram, Ramesh; Glazier, Stephen; Lu, Zhonghua; Dahn, J R, accepted for publication in Chemistry of Materials Shunmugasundaram, Ramesh; Senthil Arumugam, Rajalakshmi; Dahn J R, Chem. Mater., 2015, 27 (3), pp 757–767 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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.030
GPT teacher head0.253
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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