Challenges in Zinc Electrodes for Alkaline Zinc–Air Batteries: Obstacles to Commercialization
ACS energy letters(2019)SCI 1区
Key Laboratory of Advanced Ceramics and Machining Technology (Ministry of Education) | Argonne Natl Lab
Abstract
Alkaline zinc–air batteries are promising energy storage technologies with the advantages of low cost, ecological friendliness, and high energy density. However, the rechargeable zinc–air battery has not been used on a commercial scale because the zinc electrode suffers from critical problems such as passivation, dendrite growth, and hydrogen evolution reaction, which limit the practical applications of zinc–air batteries. Herein, the Perspective summaries the solutions to minimize the negative effects of zinc electrodes on discharge performance, cycling life, and shelf life. The future direction of academic research based on current studies of the existing challenges is proposed.
MoreTranslated text
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
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2006
被引用134 | 浏览
2011
被引用54 | 浏览
The Inhibition of the Dendritic Electrocrystallization of Zinc from Doped Alkaline Zincate Solutions
1972
被引用98 | 浏览
2009
被引用53 | 浏览
2015
被引用90 | 浏览
2015
被引用271 | 浏览
2015
被引用27 | 浏览
2017
被引用116 | 浏览
2018
被引用80 | 浏览
2016
被引用28 | 浏览
2016
被引用275 | 浏览
2017
被引用42 | 浏览
2018
被引用51 | 浏览
2017
被引用36 | 浏览
2017
被引用1225 | 浏览
2017
被引用743 | 浏览
2017
被引用111 | 浏览
2018
被引用31 | 浏览
2018
被引用104 | 浏览
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
去 AI 文献库 对话