MétaCan
Menu
Back to cohort
Record W4285400107 · doi:10.1149/ma2022-012230mtgabs

Improved Air Stability of Sulfide Electrolytes for All-Solid-State Li Batteries

2022· article· en· W4285400107 on OpenAlexaff
Feipeng Zhao, Jianwen Liang, Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsSulfideElectrolyteIonic conductivityMaterials scienceIonic bondingInorganic chemistryElectrochemistryChemistryChemical engineeringIonPhysical chemistryElectrodeMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Sulfide-based solid electrolytes (SEs) are receiving increasing attention due to their high ionic conductivities (up to 10-2 S cm-1 at room temperature) that can be comparable to the liquid electrolytes.1 However, the air stability of sulfide SEs is very poor.2, 3 Most developed sulfide SEs are prone to turn bad when exposed to the moisture. The generated H2S is dangerous, which places the commercialization of sulfide SEs into a challenging situation. 2, 3 In our work, to improve the air stability of sulfide SEs, we employed element substitution (Sn and Sb) for the problematic element (P) in conventional sulfide SEs (Li3PS4, Li6PS5I, Li10GeP2S12) based on the hard and soft acid and base theory (HSAB).4, 5, 6 Our results suggest that Sn and Sb-substituted sulfide SEs show significantly improved air stability compared with the pristine sulfides. Meanwhile, it is found that the Sn or Sb element substitution effectively enhance the ionic conductivity as well as Li metal compatibility in some cases. Various structural (e.g., X-ray diffraction, X-ray absorption near edge spectroscopy, solid-state nuclear magnetic resonance) and electrochemical characterizations are employed to identify the mechanism of the improvements are related to the Sn/Sb substitution-derived crystal structure, reinforced bonding energy, and the optimized electrode/electrolyte interfaces. Our studies provide a new idea of designing functional sulfide composition to realize improved air stability coupling with other essential properties. Re ferences N. Kamaya, R. Kanno*, et al. A lithium superionic conductor, Nat. Mater. 2011, 10, 682-686. C. Yu, F. Zhao, J. Luo, X. Sun*. Recent Development of lithium argyrodite solid-state electrolytes for solid-state batteries: synthesis, structure, stability and dynamics. Nano Energy 2021, 83, 105858. F. Zhao, S. Zhang, Y. Li*, X. Sun*. Emerging characterization techniques to understand electrode interfaces in all-solid-state lithium batteries. Small Struct. 2021, DOI: 10.1002/sstr.202100146. F. Zhao, J. Liang, X. Sun*, et al. A Versatile Sn-Substituted Argyrodite Sulfide Electrolyte for All-Solid-State Li Metal Batteries, Adv. Energy Mater. 2020, 10, 1903422. F. Zhao, X. Sun*, et al. An Air-Stable and Li-Metal-Compatible Glass-Ceramic Electrolyte enabling High-Performance All-Solid-State Li-Metal Batteries. Adv. Mater. 2021, 33, 2006577. J. Liang, X. Sun*, et al. Li10Ge(P1–xSbx)2S12 Lithium-Ion Conductors with Enhanced Atmospheric Stability, Chem. Mater. 2020, 32, 2664-2672.

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 score0.004

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 communication0.0000.001
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 head0.012
GPT teacher head0.234
Teacher spread0.221 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207