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Record W2334193441 · doi:10.1021/cm504583y

High Capacity Li-Rich Positive Electrode Materials with Reduced First-Cycle Irreversible Capacity Loss

2015· article· en· W2334193441 on OpenAlexafffund
Ramesh Shunmugasundaram, Rajalakshmi Senthil Arumugam, J. R. Dahn

Bibliographic record

VenueChemistry of Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada3M
KeywordsCapacity lossMetalAnalytical Chemistry (journal)SpinelVacancy defectMaterials scienceTransition metalPhase (matter)OxygenOxidation stateElectrodeChemistryCrystallographyElectrochemistryMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

The first charge–discharge cycling behaviors of two sets of Li–Ni–Mn–Co type positive electrode materials were compared. The samples in each set have similar Ni–Mn–Co ratios but different Li-to-total metal ratio (Li/M). The samples that were Li-rich with a Li[Li x M (1– x ) ]O 2 structure showed a typical 4.5 V “oxygen loss” plateau and a typical irreversible capacity loss near 25%. Surprisingly, other samples with lower Li/M ratios that still exhibited a 4.5 V “oxygen loss” plateau exhibited an irreversible capacity loss as low as 4.0% of their first charge capacity. XRD analysis revealed that all samples were single-phase layered oxides. A separate and a detailed XRD analysis combined with d Q /d V analysis showed that the reduced irreversible capacity loss was not caused by the admixture of a spinel phase. ICP-OES results and the oxidation state versus atomic occupancy rules suggested the presence of metal site vacancies in the pristine materials with low IRC, which were confirmed by densities measured with a helium pycnometer. The results presented here show that the small irreversible capacity is a consequence of (a) metal site vacancies, leading to Li[□ q M (1– q ) ]O 2 structures, where □ is a metal site vacancy, which leads to (b) no Li atoms in the transition metal layer. These materials still have Li/M > 1, so they are “Li-rich”, but they are “traditional layered materials” with no Li in the transition metal layer. This study identifies a new route for fabricating high capacity Li-rich positive electrode materials with small irreversible capacity loss.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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 teacher head, not a consensus.

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

Citations132
Published2015
Admission routes2
Has abstractyes

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