Title |
Finite Element Analysis on Nanomechanical Detection of Small Particles: Toward Virus Detection
|
---|---|
Published in |
Frontiers in Microbiology, April 2016
|
DOI | 10.3389/fmicb.2016.00488 |
Pubmed ID | |
Authors |
Gaku Imamura, Kota Shiba, Genki Yoshikawa |
Abstract |
Detection of small particles, including viruses and particulate matter (PM), has been attracting much attention in light of increasing need for environmental monitoring. Owing to their high versatility, a nanomechanical sensor is one of the most promising sensors which can be adapted to various monitoring systems. In this study, we present an optimization strategy to efficiently detect small particles with nanomechanical sensors. Adsorption of particles on the receptor layer of nanomechanical sensors and the resultant signal are analyzed using finite element analysis (FEA). We investigate the effect of structural parameters (e.g., adsorption position and embedded depth of a particle and thickness of the receptor layer) and elastic properties of the receptor layer (e.g., Young's modulus and Poisson's ratio) on the sensitivity. It is found that a membrane-type surface stress sensors (MSS) has the potential for robust detection of small particles. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 5 | 21% |
Researcher | 3 | 13% |
Student > Bachelor | 2 | 8% |
Lecturer | 1 | 4% |
Professor | 1 | 4% |
Other | 1 | 4% |
Unknown | 11 | 46% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 3 | 13% |
Engineering | 3 | 13% |
Computer Science | 2 | 8% |
Nursing and Health Professions | 1 | 4% |
Immunology and Microbiology | 1 | 4% |
Other | 3 | 13% |
Unknown | 11 | 46% |