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Record W2041870926 · doi:10.1039/c3cp55132f

The use of<sup>6</sup>Li{<sup>7</sup>Li}-REDOR NMR spectroscopy to compare the ionic conductivities of solid-state lithium ion electrolytes

2013· article· en· W2041870926 on OpenAlexaff
T. Leigh Spencer, N. W. Plagos, Darren H. Brouwer, Gillian R. Goward

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsRedeemer University CollegeMcMaster University
Fundersnot available
KeywordsLithium (medication)IonIonic conductivityElectrolyteChemistryIonic bondingPhase (matter)Fast ion conductorConductivityAnalytical Chemistry (journal)Solid-state nuclear magnetic resonanceIsotopes of lithiumDipoleActivation energyMaterials scienceChemical physicsPhysical chemistryNuclear magnetic resonanceIon exchangeElectrode

Abstract

fetched live from OpenAlex

Garnet-like solid-state electrolyte materials for lithium ion batteries are promising replacements for the currently-used liquid electrolytes. This work compares the temperature dependent Li(+) ion hopping rate in Li6BaLa2M2O12 (M = Ta, Nb) using solid-state (6)Li{(7)Li}-REDOR NMR. The slope of the (6)Li{(7)Li}-REDOR curve is highly temperature dependent in these two phases, and a comparison of the changes of the REDOR slopes as a function of temperature has been used to evaluate the Li(+) ion dynamics. Our results indicate that the Nb phase has a higher overall ionic conductivity in the range of 247 K to 350 K, as well as a higher activation energy for lithium ion hopping than the Ta counterpart. For appropriate relative timescales of the dipolar couplings and ion transport processes, this is shown to be a facile method to compare ion dynamics among similar structures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2013
Admission routes1
Has abstractyes

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