Your browser doesn't support javascript.
loading
Intelligent Gas Detection: g-C3N4/Polypyrrole Decorated Alginate Paper as Smart Selective NH3/NO2 Sensors at Room Temperature.
Liang, Junxuan; Zou, Zongsheng; Zhao, Zhihui; Hui, Bin; Tian, Weiliang; Zhang, Kewei.
Afiliación
  • Liang J; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China.
  • Zou Z; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China.
  • Zhao Z; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China.
  • Hui B; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China.
  • Tian W; College of Chemistry and Chemical Engineering, Tarim University, Alar 843300, PR China.
  • Zhang K; State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China.
Inorg Chem ; 63(27): 12516-12524, 2024 Jul 08.
Article en En | MEDLINE | ID: mdl-38917357
ABSTRACT
Chemiresistive NH3/NO2 sensors are attracting considerable attention for use in air-conditioning systems. However, the existing sensors suffer from cross-sensitivity, detection limit, and power consumption, owing to the inadequate charge-transfer ability of gas-sensing materials. Herein, we develop a flexible NH3/NO2 sensor based on graphitic carbon nitride/polypyrrole decorated alginate paper (AP@g-CN/PPy). The flexible sensor can work at room temperature and exhibits a positive response of 23-246% and a negative response of 37-262% toward 0.1-5 ppm of NH3 and NO2, which is ∼4.5 times and ∼7.0 times higher than a pristine PPy sensor. Moreover, the sensor exhibits flexibility, reproducibility, long-term stability, anti-interference, and high resilience to humidity, indicating its promising potential in real applications. Using the 9 feature parameters extracted from the transient response, a matched deep learning model was developed to achieve qualitative recognition of different types of gases with distinguished decision boundaries. This work not only provides an alternative gas-sensing material for dual NH3/NO2 sensing but also establishes an intelligent strategy to identify hazardous gases under an interfering atmosphere.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Inorg Chem Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Inorg Chem Año: 2024 Tipo del documento: Article