CenBRAIN's New Publication in MDPI Biosensors

Titled “Label-Free LSPR-Vertical Microcavity Biosensor for On-Site SARS-CoV-2 Detection”, this contribution has been published in MDPI Biosensors Journal. In this publication, we present a high-throughput biosensing system for sensitive SARS-CoV-2 detection in artificial saliva. Congratulations to Yuqiao Zheng and to this paper’s co-authors for this excellent achievement.

Citation

Yuqiao Zheng, Sumin Bian, Jiacheng Sun, Liaoyong Wen, Guoguang Rong, and Mohamad Sawan, "Label-Free LSPR-Vertical Microcavity Biosensor for On-Site SARS-CoV-2 Detection", MDPI Biosensors, Vol. 12, no. 3, pp. 151, 2022.

Abstract

Cost-effective, rapid, and sensitive detection of SARS-CoV-2, in high-throughput, is crucial in controlling the COVID-19 epidemic. In this study, we proposed a vertical microcavity and localized surface plasmon resonance hybrid biosensor for SARS-CoV-2 detection in artificial saliva and assessed its efficacy. The proposed biosensor monitors the valley shifts in the reflectance spectrum, as induced by changes in the refractive index within the proximity of the sensor surface. A low-cost and fast method was developed to form nanoporous gold (NPG) with different surface morphologies on the vertical microcavity wafer, followed by immobilization with the SARS-CoV-2 antibody for capturing the virus. Modeling and simulation were conducted to optimize the microcavity structure and the NPG parameters. Simulation results revealed that NPG-deposited sensors performed better in resonance quality and in sensitivity compared to gold-deposited and pure microcavity sensors. The experiment confirmed the effect of NPG surface morphology on the biosensor sensitivity as demonstrated by simulation. Pre-clinical validation revealed that 40% porosity led to the highest sensitivity for SARS-CoV-2 pseudovirus at 319 copies/mL in artificial saliva. The proposed automatic biosensing system delivered the results of 100 samples within 30 min, demonstrating its potential for on-site coronavirus detection with sufficient sensitivity.

More information can be found at the following link:

https://doi.org/10.3390/bios12030151

Fig.1: Schematic diagram of the proposed biosensor and the detection principle.