PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
December 5, 2025Angewandte Chemie International Edition6 citations

Molding of Li 5.5 PS 4.5 Cl 1.5 Particles Based on Regulating Li + Transport for All‐Solid‐State Li Metal Battery

View Full Paper
GLGuanwu LiDWDong WangBGBo Gao

Key Points

  • Long cycle lithium metal batteries achieved with over 80% capacity retention.
  • The model evaluates consistency and number influences on Li + transfer in solid-state electrolytes.
  • Machine learning techniques analyze Li + concentration based on particle molding.
  • Optimized particle size gradient design supports uniform Li + transport for better performance.

Abstract

Abstract All‐solid‐state Li metal batteries (ASSLBs) are coming with sulfide solid‐state electrolytes (S‐SSEs) for superior Li + conductivity, but irregular particles and interfaces lead to disorder Li + flux in S‐SSEs that hinder pure Li as an anode. Specially, its mesoscopic structure cannot be adequately described by average size, making it difficult to analyze Li + flux effectively. Herein, a model is constructed on the molding of Li 5.5 PS 4.5 Cl 1.5 (LPSC) particles and defined size as the number ( N ) and consistency ( σ ) to evaluate their effects on Li + transfer and concentration uniformity. Through machine learning of calculation data (Li + concentration with N and σ ) and experimental results, excessive interfaces can hinder Li + transport and local aggregation of irregular interfaces leads to uneven ion transport. Therefore, a particle size gradient S‐SSEs (induced by different size LPSC particles) is predicted to achieve fast and uniform Li + transport. Subsequently, this designed S‐SSE is applied in ASSLBs, which can complete a 1000 h cycle with capacity retention exceeding 80%. This study elucidates that the long cycle ASSLBs can be achieved by adjusting the molding of LPSC particles. Specifically, it demonstrates that the Li + flux of the whole S‐SSEs can be optimized through gradient size design.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/693231288e51979591dce72bhttps://doi.org/10.1002/anie.202520479
Ask AI
Helpful
Bookmark
Share
View Full Paper