Suppr超能文献

基于多级异质结纤维状 Ag-ZnO/InO 的高响应甲醛传感器。

A high-response formaldehyde sensor based on fibrous Ag-ZnO/InO with multi-level heterojunctions.

机构信息

State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, PR China; Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Xianhu Hydrogen Valley, Foshan 528200, PR China.

State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, PR China.

出版信息

J Hazard Mater. 2021 Jul 5;413:125352. doi: 10.1016/j.jhazmat.2021.125352. Epub 2021 Feb 18.

Abstract

Timely detection of formaldehyde is pivotal because formaldehyde is slowly released from the indoor decorative materials, jeopardizing our healthy. Herein, a high-response formaldehyde gas sensor based on Ag-ZnO/InO nanofibers was successfully fabricated. Compared with all the control samples, the hybrid exhibits superior sensitivity (0.65 ppm), excellent selectivity (≥ 12.5) and durable stability (the deviation value ≤ 3%). Particularly, an ultra-high response value of about 186 towards 100 ppm of formaldehyde at 260 °C was achieved, heading the list of outstanding candidates. Additionally, the limit of detection is as low as 9 ppb. The enhanced gas sensing properties can be mainly attributed to multi-level heterojunctions (n-n heterojunction and Ohmic junction) and the "spill-over" effect of Ag, ultimately increasing the adsorption of gas molecules on the surface of sensing material. This work verifies that proper design of multi-level heterojunctions significantly upgrade the sensing performance.

摘要

及时检测甲醛至关重要,因为甲醛会从室内装饰材料中缓慢释放,危害我们的健康。在此,成功制备了基于 Ag-ZnO/InO 纳米纤维的高响应甲醛气体传感器。与所有对照样品相比,该混合样品表现出优异的灵敏度(0.65 ppm)、出色的选择性(≥12.5)和持久的稳定性(偏差值≤3%)。特别是,在 260°C 下,对 100 ppm 的甲醛的超高高响应值约为 186,位居前列。此外,检测限低至 9 ppb。增强的气体传感性能主要归因于多级异质结(n-n 异质结和欧姆结)和 Ag 的“溢出”效应,最终增加了气体分子在传感材料表面的吸附。这项工作验证了多级异质结的合理设计可以显著提升传感性能。

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