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Record W2517878834 · doi:10.1039/c6cp05262b

Spatially resolved surface valence gradient and structural transformation of lithium transition metal oxides in lithium-ion batteries

2016· article· en· W2517878834 on OpenAlexafffund
Hanshuo Liu, Matthieu Bugnet, Matteo Z. Tessaro, Kristopher J. Harris, Mark J. R. Dunham, Meng Jiang, Gillian R. Goward, Gianluigi A. Botton

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrochemistryLithium (medication)Transition metalValence (chemistry)IonMaterials scienceOxideCathodeMetalInorganic chemistryLithium metalChemical physicsChemistryElectrodePhysical chemistryMetallurgyElectrolyteCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Li nuclear magnetic resonance (NMR) measurements, we show that the local ordering of transition metal and Li layers (R3[combining macron]m structure) is well retained in the bulk material upon cycling. In complement to the bulk measurements, we locally probe the valence state distribution of cations and the surface structure of NMC particles using EELS and scanning transmission electron microscopy (STEM). The results reveal that the surface evolution of NMC is initiated in the first-charging step with a surface reduction layer formed at the particle surface. The NMC surface undergoes phase transformation from the layered structure to a poor electronic and ionic conducting transition-metal oxide rock-salt phase (R3[combining macron]m → Fm3[combining macron]m), accompanied by irreversible lithium and oxygen loss. In addition to the electrochemical cycling effect, electrolyte exposure also shows non-negligible influence on cathode surface degradation. These chemical and structural changes of the NMC cathode could contribute to the first-cycle coulombic inefficiency, restrict the charge transfer characteristics and ultimately impact the cell capacity.

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 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 score0.007
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.222
Teacher spread0.213 · 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.

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

Citations66
Published2016
Admission routes2
Has abstractyes

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