Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142085
Title: Microfluidic and micromachined/MEMS devices for separation, discrimination and detection of airborne particles for pollution monitoring
Authors: Poenar, Daniel Puiu
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Poenar, D. P. (2019). Microfluidic and micromachined/MEMS devices for separation, discrimination and detection of airborne particles for pollution monitoring. Micromachines, 10(7), 483-. doi:10.3390/mi10070483
Journal: Micromachines 
Abstract: Most of the microfluidics-related literature describes devices handling liquids, with only a small part dealing with gas-based applications, and a much smaller number of papers are devoted to the separation and/or detection of airborne inorganic particles. This review is dedicated to this rather less known field which has become increasingly important in the last years due to the growing attention devoted to pollution monitoring and air quality assessment. After a brief introduction summarizing the main particulate matter (PM) classes and the need for their study, the paper reviews miniaturized devices and/or systems for separation, detection and quantitative assessment of PM concentration in air with portable and easy-to-use platforms. The PM separation methods are described first, followed by the key detection methods, namely optical (scattering) and electrical. The most important miniaturized reported realizations are analyzed, with special attention given to microfluidic and micromachined or micro-electro-mechanical systems (MEMS) chip-based implementations due to their inherent capability of being integrated in lab-on-chip (LOC) type of smart microsystems with increased functionalities that can be portable and are easy to use. The operating principles and (when available) key performance parameters of such devices are presented and compared, also highlighting their advantages and disadvantages. Finally, the most relevant conclusions are discussed in the last section.
URI: https://hdl.handle.net/10356/142085
ISSN: 2072-666X
DOI: 10.3390/mi10070483
Rights: © 2019 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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