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Record W2331093337 · doi:10.1149/04527.0021ecst

Recent Progress in Garnet-Type Structure Solid Li Ion Electrolytes: Composition – Structure – Ionic Conductivity Relationship and Chemical Stability Focused

2013· article· en· W2331093337 on OpenAlexaff
Lina Truong, Sumaletha Narayanan, Venkataraman Thangadurai

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConductivityIonic conductivityElectrolyteIonChemical stabilityIonic bondingMaterials scienceElectrochemistryFast ion conductorInorganic chemistryElectrical resistivity and conductivityDopingChemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Garnet-type oxides, Li 5 La 3 M 2 O 12 (M = Nb, Ta) are a group of materials that exhibit high electrochemical stability and high conductivity for Li ions, making them promising electrolyte materials for all-solid-state Li-ion batteries. Li ion conductivity can continue to be increased by substituting alkaline earth and Li ions for La 3+ in the structure. Our recent work has shown a simple empirical relation between concentration of Li and ionic conductivity in several garnet-type compounds. The occupation of Li ions in various crystallographic sites also appears to control Li ion conductivity in the garnet-type structures. In the present paper, we report current progress in garnet-based Li ion electrolytes and also discuss the effect of chemical doping on ionic conductivity and chemical stability of Li 5 La 3 Nb 2 O 12 in water and organic acids.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.225
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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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