A systematic review on gold nanoparticles based-optical biosensors for Influenza virus detection

生物传感器 纳米技术 材料科学 胶体金 检出限 纳米材料 纳米颗粒 化学 色谱法
作者
Letícia Tessaro,Adriano Aquino,Anna Paula Azevedo de Carvalho,Carlos Adam Conté-Júnior
出处
期刊:Sensors and actuators reports [Elsevier]
卷期号:3: 100060-100060 被引量:32
标识
DOI:10.1016/j.snr.2021.100060
摘要

Rapid and selective methods for detecting and diagnosing Influenza viruses can be useful to prevent a possible new pandemic. Biosensors are highly sensitive and can be a simpler, faster, and cheaper method for rapid detection of Influenza being an alternative to conventional ELISA and PCR techniques. This study systematically searched and overviewed literature data related to gold nanoparticles (AuNPs)-based optical biosensors towards Influenza virus, retrieved from four databases, published between 2010 and 2021. We extracted data and critically analyzed the effect of Au-based nanomaterial type, functionalization, size, and morphology of AuNPs on sensitivity, and the effect of transducer type on detection limit (LOD). Although a direct relationship between AuNP size and LOD has not been noted, we found authors reporting a large variation in LOD according to morphology and transducer type. Spherical AuNP with an average diameter (AD) ranging from 10 to 100 nm, with better results for AD 80 nm. On the other hand, the increase in particle size impairs sensitivity in the bipyramids. Despite AuNPs and colorimetric detection are receiving attention from researchers, the most sensitive biosensor (LOD of 0.1 pg.mL−1) used a hybrid system (AuNPs-decorated carbon nanotubes) and photoluminescent detection. Furthermore, we showed the high specificity of AuNPs-optical biosensors in point-of-care (POC) diagnostic devices as a faster and cheaper approach for clinical diagnosis. Finally, we presented perspectives, future research, and opportunities including computational biosensors, AuNPs-hybrids, and synthesis approaches to improve biosensor's stability, sensitivity, and selectivity for rapid detection of influenza in clinical samples.

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