Constructing Layered/Tunnel Biphasic Structure Via Trace W-Substitution in Tunnel-Type Cathode for Elevating Sodium Ion Storage
Molecules (Basel, Switzerland)（2025）
Abstract
Tunnel-type Na0.44MnO2 is extensively regarded as an appealing cathode for sodium-ion batteries due to its cost-effectiveness and excellent cycling performance. However, low theoretical capacity, resulting from insufficient Na+ storage sites, hinders its practical application. Herein, the strategy of constructing a tunnel-phase-dominated layered/tunnel biphasic compound was proposed via trace W-substitution and the co-precipitation method. Experimental analysis reveals that W-introduction can effectively redistribute electronic configuration, induce tunnel-to-layered structure evolution, accelerate Na+ (de)intercalation kinetics, and enhance structural stability. The optimized layered/tunnel Na0.44Mn0.99W0.01O2 cathode integrates the superiorities of the layered and tunnel structures, delivering a high capacity of 153.1 mAh g−1 at 0.1 C and outstanding cycle life, with 71% capacity retention over 600 cycles at 5 C. Significantly, the full cell assembled with the Na0.44Mn0.99W0.01O2 cathode and a commercial hard carbon anode exhibits a competitive energy density of 183.2 Wh kg−1, along with a remarkable capacity retention of 75.5% over 200 cycles at 1 C. This work not only highlights the superior sodium storage performance of biphasic composites owing to the synergistic effects between layered and tunnel structures, but also unveils new possibilities for constructing high-performance hybrid cathodes that predominantly consist of the tunnel phase using a suitable design strategy.
MoreTranslated text
Key words
oxide cathode,layered/tunnel biphasic structure,tungsten substitution,phase transition,sodium-ion batteries
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