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Record W2743345333 · doi:10.1021/acs.jpcc.7b05837

Formulation of a Statistical Mechanical Theory To Understand the Li Ion Conduction in Crystalline Electrolytes: A Case Study on Li-Stuffed Garnets

2017· article· en· W2743345333 on OpenAlexaff
Reginald Paul, Venkataraman Thangadurai

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIonic conductivityDielectric spectroscopyConductivityElectrolyteIonIonic bondingThermal conductionMaterials scienceFast ion conductorCeramicThermodynamicsChemical physicsChemistryElectrochemistryPhysical chemistryPhysicsComposite materialElectrode

Abstract

fetched live from OpenAlex

Ionic conductivity in solids is being computed using a wide range of computational methods such as molecular dynamics simulations and is measured using experimental methods, including electrochemical impedance spectroscopy and dc methods, and solid-state nuclear magnetic resonance spectroscopy. We report for the first time a statistical mechanical approach to estimate Li ion conductivity in the crystalline Li-stuffed garnet-type structure Li 5 La 3 Ta 2 O 12, Li 5.5 La 2.5 Ba 0.5 Ta 2 O 12, and Li 6 La 2 BaTa 2 O 12 . The estimated conductivity and activation energy for ionic conduction were found to be very consistent with experimental values for all three investigated garnets. The ionic conductivity was computed from the electrostatic friction coefficient of the Li ion using a combination of nonequilibrium statistical mechanics and electrostatics. The developed theory is derived from the fundamental transport equations that can be adapted to a wide range of crystalline ceramics electrolytes where crystallographic information is available, and sophisticated computational software and equipment may not be needed.

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 score0.046
Threshold uncertainty score0.229

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 head0.024
GPT teacher head0.291
Teacher spread0.268 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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