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Record W2888468048 · doi:10.1149/2.0491811jes

Dependence of Cell Failure on Cut-Off Voltage Ranges and Observation of Kinetic Hindrance in LiNi<sub>0.8</sub>Co<sub>0.15</sub>Al<sub>0.05</sub>O<sub>2</sub>

2018· article· en· W2888468048 on OpenAlexaff
Jing Li, Jessie Harlow, Nikolai Stakheiko, Ning Zhang, Jens Paulsen, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsQueen's UniversityDalhousie University
Fundersnot available
KeywordsAnalytical Chemistry (journal)Capacity lossKinetic energyElectrodeChemistryGraphiteMaterials scienceElectrochemistryPhysical chemistryChromatography

Abstract

fetched live from OpenAlex

The dependence of differential capacity versus voltage (dQ/dV) of Li/NCA half cells on temperature and testing current (C-rate) was studied. Kinetic hindrance of lithium diffusion at both low (∼3.5 V vs Li/Li+) and high states of charge (∼4.17 V) was observed. In-situ X-ray diffraction measured the volume changes of the NCA lattice versus state of charge. NCA/graphite pouch cells were cycled in various voltage ranges to explore the impacts of depth of discharge (DOD) ranges and the kinetic hindrance regions in NCA on cell failure. dV/dQ analysis, full cell impedance and symmetric cell impedance analysis as well as half-cell studies of recovered electrodes were performed after 0, ∼400 and 800 charge-discharge cycles. The contributions of active mass loss and shift loss (from loss of Li inventory) to the capacity fade of NCA/graphite cells under various testing conditions were determined. The increase in positive electrode charge transfer impedance with cycle number was proportional to the increase of positive electrode active mass loss. There was no strong correlation between positive electrode active mass loss and lattice volume change. NCA active mass loss during cycling can be minimized when the dQ/dV peaks at ∼3.5 and 4.17 V (vs. Li/Li+), that show kinetic hindrance, are partially or completely avoided.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.223
Teacher spread0.214 · 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

Citations141
Published2018
Admission routes1
Has abstractyes

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