Suppr超能文献

用过氧化苯甲酰通过缺陷工程增强单壁碳纳米管的近红外光致发光。

Enhancing near-infrared photoluminescence from single-walled carbon nanotubes by defect-engineering using benzoyl peroxide.

机构信息

Department of Organic Chemistry, Bioorganic Chemistry and Biotechnology, Silesian University of Technology, B. Krzywoustego 4, 44-100, Gliwice, Poland.

Institute of Physics-CSE, Silesian University of Technology, Konarskiego 22B, 44-100, Gliwice, Poland.

出版信息

Sci Rep. 2020 Nov 16;10(1):19877. doi: 10.1038/s41598-020-76716-9.

Abstract

Single-walled carbon nanotubes (SWCNTs) have been modified with ester groups using typical organic radical chemistry. Consequently, traps for mobile excitons have been created, which enhanced the optical properties of the material. The proposed methodology combines the benefits of mainstream approaches to create luminescent defects in SWCNTs while it simultaneously avoids their limitations. A step change was achieved when the aqueous medium was abandoned. The selection of an appropriate organic solvent enabled much more facile modification of SWCNTs. The presented technique is quick and versatile as it can engage numerous reactants to tune the light emission capabilities of SWCNTs. Importantly, it can also utilize SWCNTs sorted by chirality using conjugated polymers to enhance their light emission capabilities. Such differentiation is conducted in organic solvents, so monochiral SWCNT can be directly functionalized using the demonstrated concept in the same medium without the need to redisperse the material in water.

摘要

单壁碳纳米管 (SWCNT) 已通过典型的有机自由基化学被修饰上酯基。因此,产生了可捕获迁移激子的陷阱,从而增强了材料的光学性质。所提出的方法结合了主流方法的优势,在 SWCNT 中创造了发光缺陷,同时避免了它们的局限性。当放弃水介质时,就取得了重大突破。选择适当的有机溶剂使 SWCNT 的修饰变得更加容易。所提出的技术快速且多功能,因为它可以使用多种反应物来调整 SWCNT 的发光能力。重要的是,它还可以使用共轭聚合物对通过手性进行分类的 SWCNT 进行调整,以增强它们的发光能力。这种区分是在有机溶剂中进行的,因此可以在同一介质中直接使用所展示的概念对单手性 SWCNT 进行功能化,而无需将材料重新分散在水中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ddf/7669876/82486859816a/41598_2020_76716_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