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Record W2549484929 · doi:10.1021/acs.jpclett.6b02351

Unusual Li-Ion Transfer Mechanism in Liquid Electrolytes: A First-Principles Study

2016· article· en· W2549484929 on OpenAlexaff
Zhen‐Kun Tang, John S. Tse, Limin Liu

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Saskatchewan
FundersGovernment of Guangdong ProvinceNatural Science Foundation of Hunan ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsElectrolyteIonDiffusionChemistryConductivityLithium (medication)Chemical physicsIonic conductivityMolecular dynamicsSalt (chemistry)Inorganic chemistryIonic bondingThermodynamicsComputational chemistryPhysical chemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Liquid electrolytes play an important role in commercial lithium-ion (Li-ion) batteries as a conduit for Li-ion transfer between anodes and cathodes. It is generally believed that the Li-ions move along with the salt ions; thus, Li-ion diffusion is only affected by the viscosity and salt concentration in the liquid electrolytes based on the Stokes-Einstein equation. In this study, a novel and faster Li-ion diffusion mechanism in electrolytes containing a cyanogen group is identified from first-principles molecular dynamics (FPMD) simulations. In this mechanism, the Li-ions are first detached from the Li-salt and then diffuse along with the solvent molecules, and the Li-ion diffusion does not obey the traditional Stokes-Einstein equation. The ionic conductivity of the electrolyte systems with this "solvent-assisted Li-ion diffusion" mechanism is further enhanced through Li-ion hopping. This novel Li-ion diffusion process explains recent findings of high Li-ion conductivity in electrolytes with cyanogen groups and furnishes a new paradigm for the design of fast-charging liquid electrolyte for Li-ion batteries.

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 score0.010
Threshold uncertainty score0.428

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations51
Published2016
Admission routes1
Has abstractyes

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