Chrome Extension
WeChat Mini Program
Use on ChatGLM

Porous Co3O4 Nanoplates As the Active Material for Rechargeable Zn-air Batteries with High Energy Efficiency and Cycling Stability

Energy(2018)SCI 1区SCI 2区

Univ Sci & Technol China | Hong Kong Polytech Univ

Cited 31|Views18
Abstract
Efficient electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are crucial for rechargeable Zn-air batteries. We report porous Co3O4 nanoplates with the average size and thickness of similar to 100 and similar to 20 nm, respectively, and a surface area of 98.65 m(2) g(-1). The mesoporous nano structure shortens the lengths for ion/electron transport and provides abundant reaction sites. In the alkaline solution, the Co3O4 nanoplates exhibit a comparable limiting current density to that of Pt/C in the ORR and a superior activity in the OER. Redox reactions corresponding to the oxidation reduction of cobalt species with a high pseudocapacitance and stability are observed, indicating the multifunctional properties. Using Co3O4 nanoplates in the air electrode, the Zn-air battery delivers a maximum power density of 59.7 mW cm(-2). At a current density of 1 mA cm(-2), a gravimetric energy density of 901.6 Wh kg(Zn)(-1) and an energy efficiency of 67.3% are achieved. Moreover, the voltage gaps between discharge and charge as well as the energy efficiency of 58% at 10 mA cm(-2) are maintained for 100 cycles. The porous Co3O4 nanoplate is a promising active material for efficient Zn-air batteries with excellent cycling stability and high energy density. (C) 2018 Elsevier Ltd. All rights reserved.
More
Translated text
Key words
Zn-air battery,Cobalt oxide,Porous nanoplate,Multifunctional material,Energy efficiency
PDF
Bibtex
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
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
GPU is busy, summary generation fails
Rerequest