Issue 46, 2021

Integration of Ni/NiO nanoparticles and a microfluidic ELISA chip to generate a sensing platform for Streptococcus pneumoniae detection

Abstract

Enzyme-linked immunosorbent assays (ELISAs) are tests that uses antibody recognition and enzyme catalytic activity to identify a substance, and they have been widely used as a diagnostic tool in the clinic. However, performing an ELISA requires various liquid handling steps and long binding times. To solve this problem, we developed a magnetic microfluidic ELISA system (MMF-ELISA). Integration with nickel magnetic nanoparticles can streamline the ELISA process in a fully automated manner for Streptococcus pneumoniae detection. First, we synthesized paramagnetic surface-oxidized nickel nanoparticles (Ni/NiO NPs) to carry protein G. Then, we assembled a SUM290 (UlaG)-specific antibody on protein G. Finally, we integrated the NPs on a microfluidics chip for S. pneumoniae detection. The chip contains three different layers to trap the solutions; the bottom layer SiO2 is patterned on hydrophobic polymers and integrated with the middle layer PDMS and the top layer PMMA. With Arduino and motor IC, we developed an automated platform for S. pneumoniae detection. Microfluidic ELISAs can reduce the manual handling and operation time. Furthermore, the developed system can be extended to multiple areas for ELISA-related assays. This economical, rapid and portable system may become a promising platform for sensing S. pneumoniae in clinical applications.

Graphical abstract: Integration of Ni/NiO nanoparticles and a microfluidic ELISA chip to generate a sensing platform for Streptococcus pneumoniae detection

Supplementary files

Article information

Article type
Paper
Submitted
15 Jun 2021
Accepted
16 Aug 2021
First published
24 Aug 2021
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2021,11, 28551-28556

Integration of Ni/NiO nanoparticles and a microfluidic ELISA chip to generate a sensing platform for Streptococcus pneumoniae detection

C. Weng, C. Chao, S. Wu, P. Tsou, W. Chen, B. Li and Y. Li, RSC Adv., 2021, 11, 28551 DOI: 10.1039/D1RA04631D

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