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Investigation of Plasmonic Detection of Human Respiratory Virus.

ABSTRACT: The COVID-19 virus has been recently identified as a new species of virus that can cause severe infections such as pneumonia. The sudden outbreak of this disease is being considered a pandemic. Given all this, it is essential to develop smart biosensors that can detect pathogens with minimum time delay. Surface plasmon resonance (SPR) biosensors make use of refractive index (RI) changes as the sensing parameter. In this work, based on actual data taken from previous experimental works done on plasmonic detection of viruses, a detailed simulation of the SPR scheme that can be used to detect the COVID-19 virus is performed and the results are extrapolated from earlier schemes to predict some outcomes of this SPR model. The results indicate that the conventional Kretschmann configuration can have a limit of detection (LOD) of 2E-05 in terms of RI change and an average sensitivity of 122.4 degRIU-1 at a wavelength of 780 nm.

SUBMITTER: Das CM

PROVIDER: S-EPMC7300606 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Investigation of Plasmonic Detection of Human Respiratory Virus.

Das Chandreyee Manas CM Guo Yan Y Kang Lixing L Ho Ho-Pui HP Yong Ken-Tye KT

Advanced theory and simulations 20200608 7

The COVID-19 virus has been recently identified as a new species of virus that can cause severe infections such as pneumonia. The sudden outbreak of this disease is being considered a pandemic. Given all this, it is essential to develop smart biosensors that can detect pathogens with minimum time delay. Surface plasmon resonance (SPR) biosensors make use of refractive index (RI) changes as the sensing parameter. In this work, based on actual data taken from previous experimental works done on pl ...[more]

PMID: 32838127

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Project description:After World Health Organization (WHO) announced COVID-19 outbreak a pandemic, we all again realized the importance of developing rapid diagnostic kits. In this article, we introduced a lightweight and field-portable biosensor employing a plasmonic chip based on nanohole arrays integrated to a lensfree-imaging framework for label-free detection of viruses in field-settings. The platform utilizes a CMOS (complementary metal-oxide-semiconductor) camera with high quantum efficiency in the spectral window of interest to monitor diffraction field patterns of nanohole arrays under the uniform illumination of an LED (light-emitting diode) source which is spectrally tuned to the plasmonic mode supported by the nanohole arrays. As an example for the applicability of our biosensor for virus detection, we could successfully demonstrate the label-free detection of H1N1 viruses, e.g., swine flu, with medically relevant concentrations. We also developed a low-cost and easy-to-use sample preparation kit to prepare the surface of the plasmonic chip for analyte binding, e.g., virus-antibody binding. In order to reveal a complete biosensor technology, we also developed a user friendly Python™ - based graphical user interface (GUI) that allows direct access to biosensor hardware, taking and processing diffraction field images, and provides virus information to the end-user. Employing highly sensitive nanohole arrays and lensfree-imaging framework, our platform could yield an LOD as low as 103 TCID50/mL. Providing accurate and rapid sensing information in a handheld platform, weighing only 70 g and 12 cm tall, without the need for bulky and expensive instrumentation, our biosensor could be a very strong candidate for diagnostic applications in resource-poor settings. As our detection scheme is based on the use of antibodies, it could quickly adapt to the detection of different viral diseases, e.g., COVID-19 or influenza, by simply coating the plasmonic chip surface with an antibody possessing affinity to the virus type of interest. Possessing this ability, our biosensor could be swiftly deployed to the field in need for rapid diagnosis, which may be an important asset to prevent the spread of diseases before turning into a pandemic by isolating patients from the population.

Dataset Information

Investigation of Plasmonic Detection of Human Respiratory Virus.

Publications

Investigation of Plasmonic Detection of Human Respiratory Virus.

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