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Record W4393856275 · doi:10.1002/batt.202400005

Emerging Halide Solid Electrolytes for Sodium Solid‐State Batteries: Structure, Conductivity, Paradigm of Applications

2024· article· en· W4393856275 on OpenAlexafffund
Zhixuan Wei, Linda F. Nazar, Jürgen Janek

Bibliographic record

VenueBatteries & Supercaps · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHalideFast ion conductorElectrolyteSolid-stateConductivitySodiumMaterials scienceChemical engineeringInorganic chemistryChemistryNanotechnologyMetallurgyPhysical chemistryEngineeringElectrode

Abstract

fetched live from OpenAlex

Abstract Solid‐state sodium batteries (SSSBs) hold great promise for the development of safe, low‐cost energy storage devices. Developing solid electrolyte (SE) materials with high ionic conductivity, high chemical and electrochemical stability, as well as good mechanical properties is the most critical step. As a promising category of SEs, halide‐based solid electrolytes combine good ionic conductivity and good electrochemical stability, and they have been already widely studied for their use in lithium‐based solid‐state batteries. In contrast, knowledge of their sodium analogues is still limited. Here, we provide a comprehensive overview of halide SEs for sodium ion conduction with a perspective of both experimental and theoretical studies, including the correlation of structure and properties; sodium ion conductivity and diffusivity; as well as compatibility with electrode materials. We hope this perspective can stimulate more research in developing halide‐based SSSBs.

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 categoriesMeta-epidemiology (narrow)
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 score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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.

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

Citations31
Published2024
Admission routes2
Has abstractyes

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