Enable High Reversibility of Fe/Cu Based Fluoride Conversion Batteries Via Interfacial Gas Release and Detergency of Garnet Electrolytes
Materials Today(2022)
State Key Laboratory of High Performance Ceramics and Superfine Microstructure
Abstract
Garnet-type Ta-doped Li7La3Zr2O12 (LLZTO) electrolyte suffers from unstable chemical passivation under air exposure, responsible for the poor interfacial wettability and conductivity with Li metal. Instead of conventional methods to remove surface contaminants by mechanical polishing, acid etching and high temperature reduction, herein we propose a simple strategy of interfacial gas release and detergency to smartly convert Li2CO3 passivation layer into ion-conductive Li3PO4 domains at mild temperature (-200 celcius). The in-situ formation of PH3 vapor and its phosphorization enables a dramatic decrease of Li/garnet interfacial resistance down to 2 X cm2 at room temperature (RT). The improved interfacial wettability and conductivity endow the symmetric cells with ultra-stable galvanostatic cycling over 1500 h and high critical current density of 2.6 mA/cm2. The high coulombic efficiency of Li plating enables a high reversibility of solid-state NCM811/Li cells even under a low N/P ratio (-4) and high cut-off voltage of 4.5 V at RT. The prototype of fluoride-garnet solid-state batteries are successfully driven as rechargeable system (rather than widely known primary battery) with high conversion capacity (400 -500 mAh/g) and high-rate performance (251.2 mAh/g at 3 C). This interface infiltration-detergency approach provides a practical solution to the achievement of high-energy solid-state Li metal batteries.
MoreTranslated text
Key words
Gas cleaning,Interface modulation,Solid state batteries,Garnet electrolyte,Fluoride conversion cathodes
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined