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Record W4405107838 · doi:10.1016/j.xcrp.2024.102314

Liquid-like solid-state diffusion of lithium ions in super-halide-rich argyrodite

2024· article· en· W4405107838 on OpenAlexaff
Yübo Wang, David Bazak, Laidong Zhou, Qiang Zhang, Baltej Singh, Linda F. Nazar

Bibliographic record

VenueCell Reports Physical Science · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHalideLithium (medication)IonMaterials scienceDiffusionInorganic chemistrySolid-stateChemistryPhysical chemistryPhysicsThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

The development of solid electrolytes with high ionic conductivity is essential for advancing safer, high-energy-density solid-state batteries, where lithium site distribution in the sublattice strongly affects ion transport. Here, we report a super-halide-rich argyrodite, Li5.3PS4.3Cl1.7, with remarkable room-temperature ionic conductivity (11.4 ± 0.7 mS cm-1) due to population of two additional interstitial lithium sites induced by vacancy redistribution. Prominent lithium density between lithium sites and elevated atomic displacement parameters indicate liquid-like diffusive behavior resembling sublattice melting. Combining electrochemical impedance spectroscopy, pulsed-field gradient NMR, and T1 relaxation methods, we demonstrate that the augmented conductivity partly arises from a low energy barrier (0.08 eV) at the local scale, attributed to a three-site lithium distribution that drives correlated lithium dynamics. This work advances our understanding of the structure-dynamics interplay in super-halide-rich argyrodites, and highlighting their potential as solid-state battery electrolytes in cells with a coated single-crystal NMC82 cathode that achieve 170 mAh/g capacity at a 0.2 C rate .

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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