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Record W2307941583 · doi:10.1149/ma2016-03/2/1084

Solid State Electrolytes for Next-Generation Lithium Ion Batteries

2016· article· en· W2307941583 on OpenAlexaff
Sumaletha Narayanan, Venkataraman Thangadurai

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLithium (medication)ElectrolyteMaterials scienceIonic conductivityFast ion conductorDielectric spectroscopyConductivityElectrochemistryElectrical resistivity and conductivityCrystal structureIonic bondingIonDopingElectrical conductorAnalytical Chemistry (journal)ChemistryElectrodeCrystallographyPhysical chemistryOptoelectronicsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Lithium ion batteries achieve much attention because of their wide applications ranging from portable electronics to transportation and grid storage. The next-generation lithium ion batteries require a highly conducting solid electrolyte which ensures safe operation. Garnet-type oxides with nominal chemical formula, Li5La3Ta2O12 are promising Li+ conductors which show high ionic conductivity, low electronic conductivity and electrochemical stability.1 It is important to understand the fundamental electrical transport mechanism of these garnet-type solid-state Li+ conductors with respect to change in temperature and lithium content in the structure. Li+ stuffing into Li5La3Ta2O12 have proven to improve the ionic conductivity of garnet-type oxides.2 The present study reports highly conducting lithium-stuffed Li5+2xLa3Ta2-xYxO12 (0.05 ≤ x ≤ 0.75) electrolytes.3 Effect of Y3+ and Li+ doping in Li5La3Ta2O12 on the structural, morphological and electrical properties are studied in this work. Detailed analysis of crystal structure, and electrical and dielectric properties are also performed, in order to investigate the Li+ migration pathways in the crystal structure, using different techniques such as powder X-ray diffraction, solid state 7Li MAS NMR, and electrochemical impedance spectroscopy.3-4 The x = 0.50 and 0.75 members in the series of Li5+2xLa3Ta2-xYxO12 have exhibited highest conductivity of ~ 10-4 Scm-1 at 23 ºC. In addition, their stability in aqueous LiCl solution make them suitable candidate as protective layers for lithium electrodes in lithium-air batteries.3 References 1.Thangadurai, V.; Kaack, H.; Weppner, W. J. F. J. Am. Ceram. Soc. 2003, 86, 437-440. 2. Thangadurai, V.; Narayanan, S., Pinzaru, D. Chem. Soc. Rev. 2014,43, 4714-4727. 3. Narayanan, S.; Ramezanipour, F.; Thangadurai, V. Inorg. Chem. 2015, 54(14), 6968-6977. 4. Baral, A. K.; Narayanan, S.; Ramezanipour, F.; Thangadurai, V. Phys. Chem. Chem. Phys. 2014, 16, 11356-11365.

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: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.006

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 head0.246
Teacher spread0.223 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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