Suppr超能文献

基于绿色发光 CdTe 量子点的荧光纳米传感器用于灵敏检测砷(III)。

Green Luminescent CdTe Quantum Dot Based Fluorescence Nano-Sensor for Sensitive Detection of Arsenic (III).

机构信息

School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur, CG, 492010, India.

Department of Chemistry, Govt. Madhav Science P. G. College, Ujjain, MP, 456010, India.

出版信息

J Fluoresc. 2017 May;27(3):781-789. doi: 10.1007/s10895-016-2011-0. Epub 2016 Dec 28.

Abstract

Arsenic (As) is a hazardous and ubiquitous element; hence the quantitative detection of arsenic in various kinds of environmental sample is an important issue. Herein, we reported L-cysteine capped CdTe Quantum dot based optical sensor for the fluorometric detection of arsenic (III) in real water sample. The method is based on the fluorescence quenching of QDs with the addition of arsenic solution that caused the reduction in fluorescence intensity due to strong interaction between As and L-cysteine to form As(Cys). The calibration curve was linear over 2.0 nM-0.5 μM arsenic with limit of detection (LOD) of 2.0 nM, correlation coefficient (r) of 0.9698, and relative standard deviation (RSD %) of 5.2%. The Stern-Volmer constant for the quenching of CdTe QDs with As at optimized condition was evaluated to be 1.17 × 10 L mol s. The feasibility of the sensor has been analyzed by checking the inference of common metal ions available in the water such as K, Na, Mg, Ca, Ba, Cu, Ni, Zn, Al, Co, Cr, Fe and its higher oxidation state As. Graphical Abstract Schematic representation of As detection by L-Cysteine capped CdTe QDs.

摘要

砷(As)是一种有害且普遍存在的元素;因此,定量检测各种环境样品中的砷是一个重要问题。在此,我们报道了基于半胱氨酸保护的 CdTe 量子点的光学传感器,用于实际水样中砷(III)的荧光检测。该方法基于加入砷溶液后量子点的荧光猝灭,由于砷与半胱氨酸之间的强相互作用,导致荧光强度降低,形成 As(Cys)。在 2.0 nM-0.5 μM 砷范围内,校准曲线呈线性,检测限(LOD)为 2.0 nM,相关系数(r)为 0.9698,相对标准偏差(RSD%)为 5.2%。在优化条件下,用砷猝灭 CdTe QDs 的 Stern-Volmer 常数被评估为 1.17×10 L mol s。通过检查水中常见金属离子(如 K、Na、Mg、Ca、Ba、Cu、Ni、Zn、Al、Co、Cr、Fe 及其较高氧化态 As)的干扰,分析了传感器的可行性。

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