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Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments.
Cao, Yunqi; Fu, Mengyao; Fan, Shuyu; Gao, Chenyang; Ma, Zhiqiang; Hou, Dibo.
Afiliação
  • Cao Y; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Fu M; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Fan S; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Gao C; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Ma Z; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
  • Hou D; College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
ACS Appl Mater Interfaces ; 16(6): 7721-7731, 2024 Feb 14.
Article em En | MEDLINE | ID: mdl-38289237
ABSTRACT
Metal-organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm-1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article