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Lithium Ytterbium-Based Halide Solid Electrolytes for High Voltage All-Solid-State Batteries

2021· article· en· W3172826361 on OpenAlexafffund
Se Young Kim, Kavish Kaup, Kern Ho Park, Abdeljalil Assoud, Laidong Zhou, Jue Liu, Xiaohan Wu, Linda F. Nazar

Bibliographic record

VenueACS Materials Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsBASF
KeywordsElectrolyteFast ion conductorMaterials scienceHalideIonic conductivityElectrochemistryCathodeSulfideInorganic chemistryLithium (medication)Chemical engineeringChemistryPhysical chemistryMetallurgyElectrode

Abstract

fetched live from OpenAlex

All-solid-state Li-ion batteries that utilize nonflammable solid electrolytes are considered potential candidates for sustainable energy storage systems. Although sulfide solid electrolytes have been widely explored, their lack of electrochemical stability above 2.7 V requires the application of protective coating layer on 4 V-class cathode materials, whereas the superior oxidative stability of chloride solid electrolytes enables their direct use with such high voltage cathodes. Here, we report a metastable trigonal phase of Li 3 YbCl 6 with an ionic conductivity of 1.0 × 10 –4 S·cm –1 and mixed-metal halide solid electrolytes, Li 3– x Yb 1– x Zr x Cl 6, with conductivities up to 1.1 mS·cm –1 at room temperature. Combined neutron, single-crystal, and powder X-ray diffraction methods reveal that Zr-substitution for Yb in Li 3 YbCl 6 triggers a trigonal-to-orthorhombic phase transition and forms new, lower energy pathways for Li-ion migration. All-solid-state cell cycling with uncoated >4 V-class cathodes is enabled by the high electrochemical oxidation stability of the mixed-metal halide solid electrolyte.

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 science0.0000.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations199
Published2021
Admission routes2
Has abstractyes

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Same venueACS Materials LettersSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207