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Record W1490120740 · doi:10.1002/er.3114

Effect of electrode physical and chemical properties on lithium-ion battery performance

2013· article· en· W1490120740 on OpenAlexafffund
Victor Chabot, Siamak Farhad, Zhongwei Chen, Alan S. Fung, Aiping Yu, Feridun Hamdullahpur

Bibliographic record

VenueInternational Journal of Energy Research · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsElectrodeLithium (medication)Thermal diffusivityIonBattery (electricity)Materials scienceAnalytical Chemistry (journal)ChemistryLithium-ion batteryThermodynamicsPower (physics)ChromatographyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of physical and chemical properties on the performance of both positive and negative electrodes is studied for lithium-ion (Li-ion) batteries. These properties include the lithium diffusivity in the active electrode material, the electrical conductivity of the electrode, and the reaction rate constant at electrode active sites. The specific energy and power of the cells are determined at various discharge rates for electrodes with different properties. In addition, this study is conducted across various cell design cases. The results reveal that at moderate discharge rates, lithium diffusivity in the active negative-electrode material has the highest impact on cell performance. The specific energy and power of the cell are improved ~11% by increasing the lithium diffusivity in the active negative-electrode material by one order of magnitude. Around 4% improvement in the cell performance is achieved by increasing the reaction rate constant at the active sites of either electrodes by one order of magnitude. Copyright © 2013 John Wiley & Sons, Ltd.

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 score0.013
Threshold uncertainty score0.246

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 head0.020
GPT teacher head0.308
Teacher spread0.289 · 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

Citations41
Published2013
Admission routes2
Has abstractyes

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