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Record W4386866591 · doi:10.1149/ma2023-012671mtgabs

Uncovering the Mysterious Transformations during Cycling in Li-Ni-Mn-O Cathode Materials

2023· article· en· W4386866591 on OpenAlexaff
Alex Hebert, Michelle Ting, Ning Chen, David Muir, Eric McCalla

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsCanadian Light Source (Canada)McGill University
Fundersnot available
KeywordsXANESX-ray photoelectron spectroscopyMaterials scienceCathodeExtended X-ray absorption fine structureCobaltTransition metalBattery (electricity)Phase (matter)AnodeMetalAbsorption (acoustics)CyclingAnalytical Chemistry (journal)SpectroscopyAbsorption spectroscopyElectrodeChemical engineeringMetallurgyChemistryPhysical chemistryComposite materialThermodynamicsCatalysis

Abstract

fetched live from OpenAlex

Layered transition metal oxides, specifically LiNixMnyCozO2, lead the market for Li-ion battery cathode materials. Cobalt is a problematic metal to include in such a high-volume product like Li-ion batteries, as it is expensive, and it is mined using unethical practices. Contemporary electrodes use metal compositions with < 0.33 Co. However, even as the least abundant element in the material, eliminating Co entirely remains an important objective, but is so far necessary as it increases material stability leading to improved cycle life. Thus, research has been active in the Li-Ni-Mn-O system, looking for potential replacements for Co-containing layered oxides. The Li-Ni-Mn-O pseudo ternary system has been investigated in the past decade, revealing some interesting materials. Several compositions (including Li0.568Ni0.073Mn0.359O2 studied here) have been uncovered that show anomalous increasing capacity while cycling over 100 cycles, with some materials reaching 300 % capacity compared to their first cycle.1 It has been hypothesized that this is due to a phase transition occurring in the material, but to date the precise mechanism remains elusive. Other materials (including Li0.586Ni0.071Mn0.343O2 studied here), though very near to these compositions, show relatively stable capacities but dramatic voltage fade.2 In both materials, structural transformations are clearly at play during extended cycling. This work leverages X-ray absorption spectroscopy (XANES/EXAFS) and X-ray photoemission spectroscopy (XPS) to uncover the subtleties in the local environments of Ni and Mn before, during, and after cycling. XANES/XPS reveal to what extent there is a contrast between the oxidation states of the Ni/Mn cations at the surface vs. the bulk of the particles, which has been shown to be important in Ni containing cathodes.3 Interestingly, in both materials, no important changes are seen in the X-ray diffraction patterns, while the pair distribution functions from EXAFS evolve dramatically as shown in the figure here for the Ni environment. Interestingly, the material that shows capacity growth shows a less reversible , while there is no significant difference in the Mn site between the materials before and after charging. Analysis of the final structures shows conversion towards spinel-like structures in both cases, despite the dramatic differences in progression of the capacities, hinting that the spinel-like phases can behave very differently depending on composition. 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 science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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
Published2023
Admission routes1
Has abstractyes

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