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Record W2296837695 · doi:10.1149/ma2014-04/2/207

The Improved Electrochemistry of Single-Phase Layered Li-Mn-Ni-O Materials over That of Layered-Layered Nano-Composites

2014· article· en· W2296837695 on OpenAlexaff
Eric McCalla, Jing Li, Aaron Rowe, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials sciencePhase diagramLithium (medication)Phase (matter)ElectrochemistryQuenching (fluorescence)Composite materialPhase boundaryElectrodeAnalytical Chemistry (journal)ChemistryOptics

Abstract

fetched live from OpenAlex

The Li-Mn-Ni-O system has received much attention for potential positive electrode materials in lithium ion batteries. In recent work [1-4], the entire phase diagram has been mapped out. Using the phase diagram as a guide, it is possible to select compositions near the boundary of the layered region. These materials can either be single phase layered if prepared by quenching from high temperature or layered-layered nano-composites if cooled more slowly. Work presented here will compare the electrochemistry of materials made under various synthesis conditions at such compositions. Here, two compositions near LiNi0.5Mn0.5O2are studied under various oxygen partial pressures and cooling rates. Figure 1 (top) shows the phase diagram with the two compositions studied, A and B (B is slightly lithium rich compared to A). XRD patterns and peak width analysis will be used to show that the materials made at B are single-phase while the only material made at A that was single-phase was made in 2% oxygen and quenched. The samples made in air showed peak broadening attributed to phase separation and the non-quenched sample in 2% oxygen showed the smallest signs of phase separation (high angle peak broadening only). Figure 1 (bottom) shows that the capacities of the single-phase materials are all higher than those that show phase separation and the cycling stability is comparable. The poor performance of the layered-layered composites is attributed to the compositions of the two phases: they are not Li2MnO3 and LiNi0.5Mn0.5O2 as promoted in the literature [5]. Instead, the end-members both contain some nickel and one contains a high proportion of nickel on the lithium layer (as high as 30% depending on synthesis conditions). Interestingly, the sample that showed the smallest sign of phase separation in the XRD (2% O2, RC) had the largest drop in capacity (140 to 100 mAh/g) compared to the quenched sample. The dramatic decrease in capacity in the sample showing the first signs of forming a layered-layered nano-composite suggests that layered-layered nano-composities should be avoided in the Li-Mn-Ni-O system. The approach often used in research experiments of adding a small amount of excess lithium then serves to help keep the material single phase which improves the electrochemistry. Figure 1: Top: a partial Li-Mn-Ni-O phase diagram showing how the upper layered boundary moves with temperature, synthesis condition (Q is quench, RC is regular cooling at a rate of 5°C/min) and atmosphere (air versus 2% O2). For all conditions, the lower layered boundary is the curved solid line joining Li2MnO3 to LiNiO2. The red lines indicate the a lattice parameter contour plots (the corresponding cones will be shown also) while the blue dotted line is a rocksalt to layered phase transition. Bottom: capacity vs. cycle number for materials made at compositions A and B in the top panel. References: [1] E. McCalla and J.R. Dahn, Solid State Ionics 242, 1 (2013). [2] E. McCalla, A.W. Rowe, R. Shunmugasundaram, and J.R. Dahn, Chem. Mater. 25, 989 (2013). [3] E. McCalla, A.W. Rowe, C.R. Brown, L.R.P. Hacquebard and J.R. Dahn, J. Electrochem. Soc. 160, A1134 (2013). [4] E. McCalla, A.W. Rowe, J. Camardese and J.R. Dahn, Chem. Mater. 25, 2716 (2013). [5] M. M. Thackeray, C. S. Johnson, J. T. Vaughey and S. A. Hackney, J. Mater. Chem. 15, 2257 (2005).

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 score0.003

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 communication0.0000.001
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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
Published2014
Admission routes1
Has abstractyes

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