High-Response Room-Temperature NO2 Sensor and Ultrafast Humidity Sensor Based on SnO2 with Rich Oxygen Vacancy

材料科学 湿度 氧气 氧传感器 超短脉冲 空位缺陷 光电子学 纳米技术 光学 核磁共振 热力学 物理 有机化学 化学 激光器
作者
Yujia Zhong,Weiwei Li,Xuanliang Zhao,Xin Jiang,Shuyuan Lin,Zhen Zhen,Wenduo Chen,Dan Xie,Hongwei Zhu
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:11 (14): 13441-13449 被引量:128
标识
DOI:10.1021/acsami.9b01737
摘要

SnO2 nanosheets with abundant vacancies (designated as SnO2–x) have been successfully prepared by annealing SnSe nanosheets in Argon. The transmission electron microscopy results of the prepared SnO2 nanosheets indicated that high-density SnO2–x nanoplates with the size of 5–10 nm were distributed on the surface of amorphous carbon. After annealing, the acquired SnO2–x/amorphous carbon retained the square morphology. The stoichiometric ratio of Sn/O = 1:1.55 confirmed that oxygen vacancies were abundant in SnO2 nanosheets. The prepared SnO2–x exhibited excellent performance of sensing NO2 at room temperature. The response of the SnO2–x-based sensor to 5 ppm NO2 was determined to be 16 with the response time and recovery time of 331 and 1057 s, respectively, which is superior to those of most reported room-temperature NO2 sensors based on SnO2 and other materials. When the humidity varied from 30 to 40%, ΔR/R was 0.025. The ultrafast humidity response (52 ms) and recovery (140 ms) are competitive compared with other state-of-art humidity sensors. According to the mechanistic study, the excellent sensing performance of SnO2–x is attributed to its special structure.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
1秒前
2秒前
胡萝卜发布了新的文献求助10
2秒前
gww发布了新的文献求助10
2秒前
5秒前
5秒前
5秒前
李lll发布了新的文献求助10
5秒前
yuii完成签到,获得积分10
6秒前
爆米花应助邪恶韩孜采纳,获得10
7秒前
8秒前
8秒前
gww完成签到,获得积分10
8秒前
justonce完成签到,获得积分10
8秒前
科研通AI2S应助LNdOjk采纳,获得10
9秒前
Owen应助温婉的凝雁采纳,获得10
11秒前
陌路发布了新的文献求助30
11秒前
Akim应助高LL采纳,获得10
11秒前
楠D发布了新的文献求助10
14秒前
16秒前
17秒前
大个应助岸久舞若衣采纳,获得10
18秒前
会撒娇的绮兰完成签到,获得积分10
19秒前
田様应助坚定青槐采纳,获得10
19秒前
希望天下0贩的0应助lee采纳,获得10
19秒前
烟花应助humble采纳,获得10
21秒前
22秒前
wyl发布了新的文献求助10
22秒前
岸久舞若衣完成签到,获得积分20
22秒前
22秒前
23秒前
slbytxs完成签到,获得积分10
23秒前
小冯完成签到,获得积分10
24秒前
烟花应助李振聪采纳,获得10
24秒前
充电宝应助李振聪采纳,获得10
24秒前
李爱国应助李振聪采纳,获得200
24秒前
我是老大应助李振聪采纳,获得10
24秒前
华仔应助李振聪采纳,获得10
24秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443580
求助须知:如何正确求助?哪些是违规求助? 8257418
关于积分的说明 17586894
捐赠科研通 5502274
什么是DOI,文献DOI怎么找? 2900939
邀请新用户注册赠送积分活动 1877987
关于科研通互助平台的介绍 1717534