Suppr超能文献

氮掺杂自组装多孔碳-金属氧化物复合材料的绿色合成及其在能源与环境领域的应用

Green synthesis of nitrogen-doped self-assembled porous carbon-metal oxide composite towards energy and environmental applications.

作者信息

Ghosh Arpita, Ghosh Sreetama, Seshadhri Garapati Meenakshi, Ramaprabhu Sundara

机构信息

Alternative Energy and Nanotechnology Laboratory (AENL), Nano Functional Materials Technology Center (NFMTC), Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India.

出版信息

Sci Rep. 2019 Mar 26;9(1):5187. doi: 10.1038/s41598-019-41700-5.

Abstract

Increasing environmental pollution, shortage of efficient energy conversion and storage devices and the depletion of fossil fuels have triggered the research community to look for advanced multifunctional materials suitable for different energy-related applications. Herein, we have discussed a novel and facile synthesis mechanism of such a carbon-based nanocomposite along with its energy and environmental applications. In this present work, nitrogen-doped carbon self-assembled into ordered mesoporous structure has been synthesized via an economical and environment-friendly route and its pore generating mechanism depending on the hydrogen bonding interaction has been highlighted. Incorporation of metal oxide nanoparticles in the porous carbon network has significantly improved CO adsorption and lithium storage capacity along with an improvement in the catalytic activity towards Oxygen Reduction Reaction (ORR). Thus our present study unveils a multifunctional material that can be used in three different fields without further modifications.

摘要

日益严重的环境污染、高效能量转换和存储设备的短缺以及化石燃料的枯竭,促使研究界去寻找适用于不同能源相关应用的先进多功能材料。在此,我们讨论了这种碳基纳米复合材料的一种新颖且简便的合成机制及其在能源和环境方面的应用。在本工作中,通过一种经济且环保的路线合成了自组装成有序介孔结构的氮掺杂碳,并强调了其取决于氢键相互作用的孔生成机制。在多孔碳网络中引入金属氧化物纳米颗粒显著提高了CO吸附和锂存储容量,同时提高了对氧还原反应(ORR)的催化活性。因此,我们目前的研究揭示了一种无需进一步改性即可用于三个不同领域的多功能材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c5e/6435743/17078006f416/41598_2019_41700_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验