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Record W4320727005 · doi:10.34133/energymatadv.0021

Current Status and Future Directions in Environmental Stability of Sulfide Solid-State Electrolytes for All-Solid-State Batteries

2023· article· en· W4320727005 on OpenAlexaff
Jianwen Liang, Xiaona Li, Changhong Wang, Jung Tae Kim, Rong Yang, Jiantao Wang, Xueliang Sun

Bibliographic record

VenueEnergy Material Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsSulfideContext (archaeology)CommercializationBiochemical engineeringNanotechnologyMaterials scienceEngineeringBusinessMetallurgy

Abstract

fetched live from OpenAlex

Recently, sulfide-based solid-state electrolytes (SSEs) have attracted much attention owing to their high ionic conductivity and feasible mechanical features. The environmental stability of sulfide-based SSEs is one of the critical aspects due to the possible decomposition, and ionic conductivity change will affect the fabrication and electrochemical performance of the batteries. Thus, important efforts have been made to reveal and improve their environmental stability, and a timely summary of the progress is urgently needed. In this review, we first clarify the definition of environmental stability and its significance in the context of practical use. After indicating the degradation mechanisms of sulfide-based SSEs, we summarize several effective strategies to improve their stability and also highlight the related theoretical studies. The stability of organic solvents of sulfide SSEs is also summarized and discussed, which may help reliable sulfide SSEs in the battery system. The main target of this review is to gain insights and provide useful guidance to further improve the environmental stability of sulfide SSEs, which will finally promote the commercialization of sulfide-based all-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 score0.107
Threshold uncertainty score0.942

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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

Citations97
Published2023
Admission routes1
Has abstractyes

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