Molecular-scale Interaction Between Sub-1 Nm Cluster Chains and Polymer for High-Performance Solid Electrolyte
Energy storage materials(2024)
Hubei Longzhong Laboratory | Al Azhar Univ
Abstract
Organic-inorganic interface in composite solid electrolyte could lead to increased ion transport for solid-state lithium batteries; however, most inorganic fillers have much larger size than polymer chains, which results in severe aggregation of inorganic fillers and poor ionic conductivity. Herein, functional sub-1nm inorganic cluster chains were integrated with polymer chains to fabricate composite solid electrolyte with enhanced ionic conductivity. Different from all other inorganic fillers, the sub-1nm inorganic cluster chains with diameter <1 nm have similar size and geometry compared with polymer chains, exhibiting polymer-like solution properties. A transparent sub-1nm inorganic filler/polymer mixed solution was generated to realize monodispersion of cluster chains as functional fillers in polymer matrix. Meanwhile, abundant oxygen vacancies on cluster chains interact with polymer chains and lithium salts at molecular-scale, which decreases the complexation of polymer segments with Li+ and promote the dissociation of lithium salts, thereby improving Li+ transport. As a result, the composite solid electrolyte exhibits high ionic conductivity (0.4 mS cm−1) and large mobile Li+ distribution (50.8%). This work pushes the size of nanofillers down to <1 nm, which is a unique approach to the molecular-scale interaction between nanofillers and polymers to boost ion transport in solid electrolytes.
MoreTranslated text
Key words
Sub -1 nm cluster chain,Molecular -scale interaction,Monodispersion,Organic -inorganic interface,Composite solid electrolyte
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
Related Papers
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