MétaCan
Menu
Back to cohort
Record W4395023625 · doi:10.1021/jacs.4c02170

Lithium Metal-Compatible Antifluorite Electrolytes for Solid-State Batteries

2024· article· en· W4395023625 on OpenAlexaff
Pengcheng Yu, Haochang Zhang, Fiaz Hussain, Jing Luo, Wen Tang, Jiuwei Lei, Lei Gao, Denys S. Butenko, Changhong Wang, Jinlong Zhu, Wen Yin, Hao Zhang, Songbai Han, Ruqiang Zou, Wei Chen, Yusheng Zhao, Wei Xia, Xueliang Sun

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Science Foundation of NingboSouthern University of Science and TechnologyNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsChemistryLithium metalElectrolyteLithium (medication)Solid-stateFast ion conductorMetalInorganic chemistryOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Lithium (Li) metal solid-state batteries feature high energy density and improved safety and thus are recognized as promising alternatives to traditional Li-ion batteries. In practice, using Li metal anodes remains challenging because of the lack of a superionic solid electrolyte that has good stability against reduction decomposition at the anode side. Here, we propose a new electrolyte design with an antistructure (compared to conventional inorganic structures) to achieve intrinsic thermodynamic stability with a Li metal anode. Li-rich antifluorite solid electrolytes are designed and synthesized, which give a high ionic conductivity of 2.1 × 10 –4 S cm –1 at room temperature with three-dimensional fast Li-ion transport pathways and demonstrate high stability in Li–Li symmetric batteries. Reversible full cells with a Li metal anode and LiCoO 2 cathode are also presented, showing the potential of Li-rich antifluorites as Li metal-compatible solid electrolytes for high-energy-density solid-state batteries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 teacher head, 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

Citations32
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207