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Record W4226261780 · doi:10.1139/cjc-2021-0319

Li-stuffed garnet electrolytes: structure, ionic conductivity, chemical stability, interface, and applications

2022· article· en· W4226261780 on OpenAlexafffundvenue
Bowen Chen, Subhajit Sarkar, Sanoop Palakkathodi Kammampata, Chengtian Zhou, Venkataraman Thangadurai

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteElectrochemical windowIonic conductivityLithium (medication)Fast ion conductorChemistryConductivityBattery (electricity)ElectrochemistryThermal stabilityChemical stabilityIonCeramicCathodeChemical engineeringElectrodePower (physics)Physical chemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Current lithium-ion batteries have been widely used in portable electronic devices, electric vehicles, and peak power demand. However, the organic liquid electrolytes used in the lithium-ion battery are flammable and not stable in contact with elemental lithium and at a higher voltage. To eliminate the safety and instability issues, solid-state (ceramic) electrolytes have attracted enormous interest worldwide, owing to their thermal and high voltage stability. Among all the solid-state electrolytes known today, the Li-stuffed garnet is one of the most promising electrolytes due to its physical and chemical properties such as high total Li-ion conductivity at room temperature, chemical stability with elemental lithium and high voltage lithium cathodes, and high electrochemical stability window (6 V vs. Li+/Li). In this short review, we provide an overview of Li-stuffed garnet electrolytes with a focus on their structure, ionic conductivity, transport mechanism, chemical stability, and battery applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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