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Record W2269961111 · doi:10.1149/2.0901603jes

Exploring Impedance Growth in High Voltage NMC/Graphite Li-Ion Cells Using a Transmission Line Model

2015· article· en· W2269961111 on OpenAlexafffund
D. W. Abarbanel, K. J. Nelson, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsElectrical impedanceCapacitanceMaterials scienceElectrolyteGraphiteElectrodeIonTransmission lineAnalytical Chemistry (journal)VoltageContact resistanceChemistryElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

A discrete transmission line model (TLM) for the impedance of the positive electrode in a Li-ion cell was studied to investigate causes of impedance increase for Li[Ni0.42Mn0.42Co0.16]O2 (NMC442) positive electrodes operated at high voltage (> 4.4 V vs. Li/Li+). The TLM included contact resistance between the conductive carbon and the active particles (Rc), electrical path resistance through the carbon network (Re), ionic path resistance through the bulk electrolyte (Ri) and transfer resistance/capacitance (Rs, C) through the SEI layers formed on the active particles. It was found that an increase in any of Re, Ri or Rc was necessary to increase the high frequency intercept of the impedance spectra. A limited increase in the spectrum diameter of the TLM was achievable by increasing Re or Ri, but an unlimited increase was only possible by increasing the SEI resistance Rs. Comparison with experiment concluded that the high voltage impedance growth observed in NMC442/graphite cells is primarily due to increases in Rs, while minor increases in Re, Ri or Rc may occur. A brief investigation of inhomogeneous SEI capacitance/resistance produced impedance spectra with a range of heights and asymmetries. This can explain in part the variety of shapes of impedance spectra from real impedance measurements of Li-ion cells.

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 score0.288
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.051
GPT teacher head0.265
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.

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

Citations52
Published2015
Admission routes2
Has abstractyes

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