杂原子
材料科学
吸附
兴奋剂
Atom(片上系统)
金属
分析化学(期刊)
扫描透射电子显微镜
选择性
带隙
透射电子显微镜
结晶学
纳米技术
化学
物理化学
催化作用
冶金
光电子学
戒指(化学)
生物化学
有机化学
色谱法
计算机科学
嵌入式系统
作者
Yihong Zhong,Guotao Yuan,Dequan Bao,Tao Yi,Zhenqiu Gao,Wei Zhao,Shuo Li,Yuting Yang,Pingping Zhang,Han Zhang,Xuhui Sun
出处
期刊:Nano-micro Letters
[Springer Science+Business Media]
日期:2025-05-25
卷期号:17 (1): 276-276
被引量:5
标识
DOI:10.1007/s40820-025-01770-9
摘要
Abstract Conventional gas sensing materials (e.g., metal oxides) suffer from deficient sensitivity and serve cross-sensitivity issues due to the lack of efficient adsorption sites. Herein, the heteroatom atomically doping strategy is demonstrated to significantly enhance the sensing performance of metal oxides-based gas sensing materials. Specifically, the Sn atoms were incorporated into porous Fe 2 O 3 in the form of atomically dispersed sites. As revealed by X-ray absorption spectroscopy and atomic-resolution scanning transmission electron microscopy, these Sn atoms successfully occupy the Fe sites in the Fe 2 O 3 lattice, forming the unique Sn–O–Fe sites. Compared to Fe–O–Fe sites (from bare Fe 2 O 3 ) and Sn–O–Sn sites (from SnO 2 /Fe 2 O 3 with high Sn loading), the Sn–O–Fe sites on porous Fe 2 O 3 exhibit a superior sensitivity ( R g / R a = 2646.6) to 1 ppm NO 2 , along with dramatically increased selectivity and ultra-low limits of detection (10 ppb). Further theoretical calculations suggest that the strong adsorption of NO 2 on Sn–O–Fe sites (N atom on Sn site, O atom on Fe site) contributes a more efficient gas response, compared to NO 2 on Fe–O–Fe sites and other gases on Sn–O–Fe sites. Moreover, the incorporated Sn atoms reduce the bandgap of Fe 2 O 3 , not only facilitating the electron release but also increasing the NO 2 adsorption at a low working temperature (150 °C). This work introduces an effective strategy to construct effective adsorption sites that show a unique response to specific gas molecules, potentially promoting the rational design of atomically modified gas sensing materials with high sensitivity and high selectivity.
科研通智能强力驱动
Strongly Powered by AbleSci AI