Integrated micro-optofluidic platform for real-time detection of airborne microorganisms

Choi, Jeongan; Kang, Mi Jeong; Jung, Jae Hee

doi:10.1038/srep15983

Cited by 31 publications

(30 citation statements)

References 43 publications

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“…The chip has been successfully employed to detect a single bacteriophage Qβ virus without the need for particle immobilization [ 70 ]. The achieved sensitivity is approaching detection of virus on single level and is comparable to sensitivity reported with other microfluidic chips [ 71 , 72 , 73 ] and other integrated techniques, such as evanescent waveguide sensors [ 74 , 75 , 76 ], electrical nanowire arrays [ 77 ], and nanoelectromechanical cantilevers [ 78 ]. More recently, a solid-state nanopore has been integrated in the optofluidic chip.…”

Section: Arrow-based Devices and Applicationssupporting

confidence: 66%

Liquid Core ARROW Waveguides: A Promising Photonic Structure for Integrated Optofluidic Microsensors

2016

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In this paper, we introduce a liquid core antiresonant reflecting optical waveguide (ARROW) as a novel optofluidic device that can be used to create innovative and highly functional microsensors. Liquid core ARROWs, with their dual ability to guide the light and the fluids in the same microchannel, have shown great potential as an optofluidic tool for quantitative spectroscopic analysis. ARROWs feature a planar architecture and, hence, are particularly attractive for chip scale integrated system. Step by step, several improvements have been made in recent years towards the implementation of these waveguides in a complete on-chip system for highly-sensitive detection down to the single molecule level. We review applications of liquid ARROWs for fluids sensing and discuss recent results and trends in the developments and applications of liquid ARROW in biomedical and biochemical research. The results outlined show that the strong light matter interaction occurring in the optofluidic channel of an ARROW and the versatility offered by the fabrication methods makes these waveguides a very promising building block for optofluidic sensor development.

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Section: Arrow-based Devices and Applicationssupporting

confidence: 66%

Liquid Core ARROW Waveguides: A Promising Photonic Structure for Integrated Optofluidic Microsensors

2016

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“…Phase-contrast microscopy allows to count bacterial endospores due to their phase-bright appearance in contrast to darker vegetative cells. Recently, investigations focused on health effects following exposure to harmful bioaerosols, led to a demand for accurate and reliable monitoring systems (Choi, Kang & Jung, 2015). Molecular techniques such as polymerase chain reaction (PCR) amplification of 16 S rDNA, followed by its sequencing and DNA-DNA hybridization allow to increase sensitivity and specificity, while decreasing the time required for analysis (Stetzenbac, Buttner, & Cruz, 2004).…”

Section: Bioaerosol Monitoring: Air Sampling Techniques and Methods Omentioning

confidence: 99%

Airborne contamination in the food industry: An update on monitoring and disinfection techniques of air

Masotti

Cattaneo

Stuknytė

et al. 2019

Trends in Food Science & Technology

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Background: Hygienic and safe production is a high priority in the food industry. During processing, food may be subjected to bio-contamination. Accordingly, preservation of overall quality by keeping a clean environment is a goal to pursue. Among microbial vectors, air is considered a contributing factor to crosscontamination. Scope and approach: Nowadays, in food plants emphasis is paid to the assessment of air bioload in view of prevention of recontamination. Normally, air entering a processing plant is chilled and filtered to remove undesired microorganisms from outside. Nevertheless, apart from clean-room environments, uncontrolled factors (processes, personnel, structures, etc.) contribute to the release of microorganisms in indoor environments, resulting in generation of bioaerosols highly variable within and among plants, and on a daily basis within the same plant. Key findings and conclusions: This review focuses on the relevance of bioaerosol monitoring in the food industry, providing an update of air sampling techniques and methods of analysis in view to strengthen preventive hygienic actions. Disinfection procedures to minimize microbial counts in the air as additional safeguard to the standard chemical sanitation protocols are reviewed. Benefits and limitations of air treatment by chemical fogging, ozonation, UV irradiation or cold plasma are outlined. Air bioload monitoring and the implementation of subsequent air disinfection procedures are a feasible and a routinely exploitable strategy to satisfy hygienic requirements in food plants. Further research is required to face technical challenges and optimize the feasibility of some disinfection technologies for the real-world of food environments.

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“…There are several methods to detect specific species of bioaerosol in real-time [ 36 , 37 , 38 ], and in this study, the level of total culturable bioaerosol could be predicted. We suggest that a surrogate measurement is needed to detect pathogenic species and approach the level of bioaerosols.…”

Section: Discussionmentioning

confidence: 99%

Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment

Seo

Jeon

Choi

et al. 2020

IJERPH

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Indoor microbiological air quality, including airborne bacteria and fungi, is associated with hospital-acquired infections (HAIs) and emerging as an environmental issue in hospital environment. Many studies have been carried out based on culture-based methods to evaluate bioaerosol level. However, conventional biomonitoring requires laborious process and specialists, and cannot provide data quickly. In order to assess the concentration of bioaerosol in real-time, particles were subdivided according to the aerodynamic diameter for surrogate measurement. Particle number concentration (PNC) and meteorological conditions selected by analyzing the correlation with bioaerosol were included in the prediction model, and the forecast accuracy of each model was evaluated by the mean absolute percentage error (MAPE). The prediction model for airborne bacteria demonstrated highly accurate prediction (R2 = 0.804, MAPE = 8.5%) from PNC1-3, PNC3-5, and PNC5-10 as independent variables. Meanwhile, the fungal prediction model showed reasonable, but weak, prediction results (R2 = 0.489, MAPE = 42.5%) with PNC3-5, PNC5-10, PNC > 10, and relative humidity. As a result of external verification, even when the model was applied in a similar hospital environment, the bioaerosol concentration could be sufficiently predicted. The prediction model constructed in this study can be used as a pre-assessment method for monitoring microbial contamination in indoor environments.

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Integrated micro-optofluidic platform for real-time detection of airborne microorganisms

Cited by 31 publications

References 43 publications

Liquid Core ARROW Waveguides: A Promising Photonic Structure for Integrated Optofluidic Microsensors

Liquid Core ARROW Waveguides: A Promising Photonic Structure for Integrated Optofluidic Microsensors

Airborne contamination in the food industry: An update on monitoring and disinfection techniques of air

Prediction Model for Airborne Microorganisms Using Particle Number Concentration as Surrogate Markers in Hospital Environment

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