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Record W2322195509 · doi:10.1149/1.3655696

Fast Lithium-Ion Conducting Garnet-Like Electrolytes for Potential Application in Lithium Ion Batteries

2011· article· en· W2322195509 on OpenAlexafffund
Sumaletha Narayanan, Venkataraman Thangadurai

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsUniversity of Calgary
FundersAUTO21 Network of Centres of ExcellenceNetworks of Centres of Excellence of Canada
KeywordsLithium (medication)IonMaterials scienceConductivityScanning electron microscopeImpurityMicrostructureAnalytical Chemistry (journal)Powder diffractionDopingElectrical resistivity and conductivityElectrolyteChemistryCrystallographyPhysical chemistryMetallurgyElectrodeComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

In this paper, we report the effect of Y-doping for Nb in the garnet-like Li5La3Nb2O12 on Li-ion conductivity. The phase formation, microstructure and electrical conductivity studies were done using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), 7Li nuclear magnetic resonance (Li-NMR) and AC impedance spectroscopy. The formation of cubic garnet-like structure was observed for x up to 0.25 and additional impurity peaks were observed above x = 0.25 in Li5La3Nb2-xYxO12-δ. 7Li MAS NMR exhibits a single peak close to a chemical shift value of 0 ppm with respect to LiCl which could be due to the fast conduction of Li ions. Lithium ion conductivity of 1.34 x 10-5 and 1.44 x 10-5 Scm-1 was observed for x = 0.05 and 0.1 members, respectively at 23 °C in air.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.213
Teacher spread0.191 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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