scholarly journals Study of size-related sensitivity of surface acoustic wave sensor towards particulate matter sized particles using finite element and experimental methods

AIP Advances ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 025324
Author(s):  
Jian Yang ◽  
Jianan Lu
Keyword(s):  
Finite Element ◽  
Particulate Matter ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Experimental Methods ◽  
Acoustic Wave Sensor

A 3-D Finite Element Model of Surface Acoustic Wave Sensor Response

2019 ◽  
Vol 1 (19) ◽  
pp. 19-27 ◽  
Author(s):  
Subramanian Sankaranarayanan ◽  
Venkat R. Bhethanabotla ◽  
Babu Joseph
Keyword(s):  
Finite Element ◽  
Acoustic Wave ◽  
Finite Element Model ◽  
Surface Acoustic Wave ◽  
Element Model ◽  
Sensor Response ◽  
Acoustic Wave Sensor

scholarly journals Sensitivity Analysis of the Surface Acoustic Wave Sensor towards Size-Distributed Particulate Matter

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jian Yang ◽  
Jianan Lu ◽  
Shanmeng Zhang ◽  
Dong Guan
Keyword(s):  
Particulate Matter ◽  
Acoustic Wave ◽  
Frequency Shift ◽  
Size Distribution ◽  
Surface Acoustic Wave ◽  
Saw Sensor ◽  
Lognormal Size Distribution ◽  
Acoustic Wave Sensor ◽  
Distribution Parameters ◽  
Relationship Of

To study the sensitivity of the surface acoustic wave (SAW) sensor towards particulate matter (PM), an analytic model has been built based on single particle perturbation theory of full size range and the lognormal size distribution of the PM. The sensitivity of the frequency shift to 1 nanogram of PM has been calculated. The model shows that the frequency shift is a result of the competition between the negative perturbation by mass loading and the positive perturbation by elastic coupling, determined by particle size distribution parameters, material, and SAW frequency. To verify the model, the relationship of the frequency shift of a 315 MHz SAW to the concentration of aerosols generated by two kinds of powders of different sizes was measured. The experiment is in agreement with the model: the sensor has shown negative sensitivity towards aerosols generated by the finer particles of 1 μm, 3 μm polytetrafluoroethylene (PTFE), and A1 Arizona dust and positive sensitivity towards aerosols generated by the coarser particles of 10 μm PTFE and A4 Arizona dust; and the negative sensitivity is about 1 order higher than the positive.

scholarly journals Detection of Particulate Matter of Size 2.5 μm with a Surface-Acoustic-Wave Sensor Combined with a Cyclone Separator

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 398 ◽  
Author(s):  
Fung-Yu Kuo ◽  
Ying-Chen Lin ◽  
Ling-Yi Ke ◽  
Chuen-Jinn Tsai ◽  
Da-Jeng Yao
Keyword(s):  
Particulate Matter ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Limit Of Detection ◽  
Cyclone Separator ◽  
Saw Sensor ◽  
Well Design ◽  
Microcentrifuge Tube ◽  
Acoustic Wave Sensor ◽  
Positive Linear Correlation

A device to monitor particulate matter of size 2.5 μm (PM2.5) that has been designed and developed includes a surface-acoustic-wave sensor operating in a shear horizontal mode (SH-SAW) combined with a cyclone separator. In our tests, aerosols generated as incense smoke were first separated and sampled inside a designed cyclone separator; the sampled PM2.5 was then introduced into the sensing area of an SH-SAW sensor for detection. The use of microcentrifuge tubes as a cyclone separator effectively decreases the size and power consumption of the device; the SAW sensor in a well design and operating at 122 MHz was fabricated with MEMS techniques. After an explanation of the design of the cyclone separator, a simulation of the efficiency and the SAW sensor detection are discussed. A microcentrifuge tube (volume 0.2 mL, inlet and outlet diameters 0.5 mm) as a separator has separation cutoff diameters 50% (d50) at 2.5 μm; the required rate of volumetric flow at the inlet is 0.125 LPM, according to simulation with computational fluid dynamics (CFD) software; the surface-acoustic-wave (SAW) sensor exhibits sensitivity approximately 9 Hz/ng; an experiment for PM2.5 detection conducted with the combined device shows a strong positive linear correlation with a commercial aerosol monitor. The limit of detection (LOD) is 11 μg/m3 with sample time 160 s and total detection duration about 5 min.

Investigation into surface acoustic wave sensor for DCM gas detection using COMSOL multiphysics

2021 ◽  
Vol 572 (1) ◽  
pp. 94-105
Author(s):  
Mohamed Moustafa ◽  
Ghaylen Laouini ◽  
Mostafa ElNaggar ◽  
Tariq AlZoubi
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Gas Detection ◽  
Comsol Multiphysics ◽  
Acoustic Wave Sensor

Boosting of NO2 Gas Sensing Performances Using GO-PEDOT:PSS Nanocomposite Chemical Interface Coated on Langasite-based Surface Acoustic Wave Sensor

2021 ◽  
pp. 130267
Author(s):  
Kedhareswara Sairam Pasupuleti ◽  
Maddaka Reddeppa ◽  
Dong-Jin Nam ◽  
Na-Hyun Bak ◽  
Koteswara Rao Peta ◽  
...  
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Gas Sensing ◽  
Acoustic Wave Sensor

Bilayer structure for hydrogen detection in a surface acoustic wave sensor system

2002 ◽  
Vol 82 (2-3) ◽  
pp. 265-271 ◽  
Author(s):  
Wieslaw P. Jakubik ◽  
Marian W. Urbańczyk ◽  
Stanislaw Kochowski ◽  
Jerzy Bodzenta
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Sensor System ◽  
Bilayer Structure ◽  
Hydrogen Detection ◽  
Acoustic Wave Sensor

The study of polyimide films and adhesion using a surface acoustic wave sensor

1991 ◽  
Vol 5 (1-4) ◽  
pp. 59-65 ◽  
Author(s):  
D.W. Galipeau ◽  
J.F. Vetelino ◽  
R. Lec ◽  
C. Feger
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Polyimide Films ◽  
Acoustic Wave Sensor

A passive wireless hydrogen surface acoustic wave sensor based on Pt-coated ZnO nanorods

2010 ◽  
Vol 21 (9) ◽  
pp. 095503 ◽  
Author(s):  
Ya-Shan Huang ◽  
Yung-Yu Chen ◽  
Tsung-Tsong Wu
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Zno Nanorods ◽  
Acoustic Wave Sensor

Anti-Interference Technology of Surface Acoustic Wave Sensor Based on K-Means Clustering Algorithm

2021 ◽  
Vol 21 (7) ◽  
pp. 8998-9007
Author(s):  
Yanping Fan ◽  
Yajun Liu ◽  
Hongli Qi ◽  
Feng Liu ◽  
Xiaojun Ji
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Clustering Algorithm ◽  
Acoustic Wave Sensor
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