Smartphone-Enabled Aerosol Particle Analysis Device

Li, Yanna; Sun, Chen; Zhou, Qiang; Lin, Zuzeng; Chang, Ye; Li, Quanning; Zhang, Menglun; Duan, Xuexin

doi:10.1109/access.2019.2930776

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…The shortcomings of current air-quality monitoring devices, such as high cost, low portability, and low density (number of air-quality monitoring devices per number of people), have fueled many alternative portable low-cost air-quality monitoring devices to enable people to monitor their personal exposure levels , based on their unique habits and locations. Many of these low-cost devices integrate smartphone technology and machine learning to image collections of PM. − However, these smartphone-based devices are not able to perform long-term monitoring in the same area, as people travel with their phones throughout the day.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, lensfree holographic devices have been used for air-quality monitoring and volatile aerosol characterization of e-cigarette smoke . However, currently, both these smartphone-based and lensfree holographic devices are limited in resolution to particles larger than 1 μm, − , or in some cases, particles larger than 500 nm . In some of our prior work, we have optimized lensfree holographic microscopy for nanoparticle detection, but have not applied this to aerosolized particulate matter sensing. − …”

Section: Introductionmentioning

confidence: 99%

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Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Baker

Gollier

Melzer

et al. 2023

ACS Appl. Nano Mater.

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Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m −3 . A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vaporcondensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Baker

Gollier

Melzer

et al. 2023

ACS Appl. Nano Mater.

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“…The air microfluidic circuit realizes the separation of desired particles by the principle of different inertial forces. Since then, there have been many types of miniaturized air microfluidic circuits [1,[20][21][22][23][24][25][26][27][28]. In addition, many virtual impactors are relatively large [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Design and Characterization of a Microfluidic Circuit for Air Particulate Matter Separation

Jiang

Zhu

et al. 2022

Micromachines

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Air microfluidic circuits have been widely concerned in the separation of atmospheric particulate matter, especially for portable particulate matter separation detection devices. Currently, no systematic approach for the design and optimization of an air-microfluidic system for PM separation has been reported in the literature. In this paper, a two-stage air microfluidic circuit is designed. The design process is divided into two stages: first, the preliminary design of the structure is completed according to aerodynamic theory. Then, the influences of various factors (such as flow channel width, tilt angle, flow rate, etc.) on the collection efficiency and particle wall loss are explored through numerical analysis to complete the optimization design of the structure. Finally, the air microfluidic circuit is prepared by MEMS processing technology and the particulate matter separation experiments are carried out. The developed two-stage air microfluidic circuit can realize the efficient separation of PM10 and PM2.5. Thus, the important factors affecting the collection efficiency and particle wall loss of air microfluidic circuit are clarified, and a systematic design theory method is formed.

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“…poorer countries), non-existent air quality control. For this reason, a system that is, at the same time, reliable from the sensing point of view and allowing a dense diffusion with low costs is the goal of many research activities [5], [6]. The interest of the scientific community is focused on either the sensing technology or the data analysis.…”

Section: Introductionmentioning

confidence: 99%

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

et al. 2020

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In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

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Smartphone-Enabled Aerosol Particle Analysis Device

Cited by 8 publications

References 38 publications

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Design and Characterization of a Microfluidic Circuit for Air Particulate Matter Separation

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

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