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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 OpenAlex
Lina Truong, Sumaletha Narayanan, Venkataraman Thangadurai

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.040
Threshold uncertainty score0.638

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 head0.013
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