4.7 Article

Revealing humidity-enhanced NH3 sensing effect by using resonant microcantilever

期刊

SENSORS AND ACTUATORS B-CHEMICAL
卷 257, 期 -, 页码 488-495

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2017.10.179

关键词

Ammonia sensor; Microcantilever; Mesoporous silica; Environmental protection; Cancer early diagnosis

资金

  1. MOST of China [2016YFA0200803]
  2. NSF of China [61604163, 61527818, 61571430, 61321492]
  3. NSF of Shanghai [15ZR1447300]
  4. Youth Innovation Promotion Association CAS [2016213]

向作者/读者索取更多资源

High performance NH3 sensor is of great importance in various application fields such as environmental protection and early diagnosis of stomach cancer. Herein, carboxyl group functionalized mesoporous silica nanoparticles (C-MSNs) with ultrahigh specific surface area are developed as high performance mass-type sensing material for trace-level NH3 detection. By using inkjet printing technology, C-MSNs material is precisely loaded on our lab-made integrated resonant microcantilever to construct a mass type gas sensor. Based on the specific acid-base interaction between Bronsted acidic C-MSNs material and basic NH3 molecules, our sensor exhibits satisfactory NH3 sensing performance. The experimentally observed detection limit of our sensor reaches parts per billion (ppb) level. Exposed to NH3 with identical concentration, our sensor outputs a higher response in wet air than that of in dry atmosphere. In real air, hydrate NH3 molecule (i.e. NH3 center dot xH(2)O) has a larger molecular weight than anhydrous NH3 in absolute dry experimental atmosphere. Therefore, our gravimetric sensor exhibits a higher response in real air. In order to in-depth elucidate the humidity-enhanced sensing effect, the micro-gravimetric sensing data obtained in wet air and in dry experimental environment are compared together. Based on our quantitatively measurement data, the molecular formula of hydrate NH3 center dot xH(2)O existing in wet air (with 50-80% relative humidity) can be obtained as NH3 2 similar to 2.4H(2)O. (C) 2017 Elsevier B.V. All rights reserved.

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