Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.


Issue 14, 2019
Previous Article Next Article

A chip-based potentiometric sensor for a Zika virus diagnostic using 3D surface molecular imprinting

Author affiliations

Abstract

The latest Zika virus (ZIKV) pandemic caused great international concern from explosively proliferating throughout the Americas. Currently, there is no vaccine to prevent Zika virus infection and available tests rely on antibodies or RNA. Unfortunately, antibody-based detection systems can result in false positive results and RNA-based detection systems are costly, time-consuming, and impractical for testing in remote regions. In this study, a potential point-of-care (POC) diagnostic system was developed using a chip-based potentiometric sensor to detect Zika virus using a 3D molecular imprinting technique. This chip-based potentiometric sensor system was able to detect 10−1 PFU mL−1 ZIKV in a buffered solution under 20 minutes without any sample manipulation. This sensor was tested against Dengue virus at clinical viral loads and showed no sign of cross-reactivity. When tested against human saliva samples containing clinical viral loads, this sensor was able to detect 10 PFU mL−1 ZIKV among the pool of bio-macromolecules. The high sensitivity and high selectivity demonstrated here proved that this lab-on-a-chip diagnostic has the potential to become a POC detection system for rapid and accurate screening of flaviviruses.

Graphical abstract: A chip-based potentiometric sensor for a Zika virus diagnostic using 3D surface molecular imprinting

Back to tab navigation

Supplementary files

Article information


Submitted
30 Mar 2019
Accepted
15 May 2019
First published
10 Jun 2019

Analyst, 2019,144, 4266-4280
Article type
Paper
Author version available

A chip-based potentiometric sensor for a Zika virus diagnostic using 3D surface molecular imprinting

V. Ricotta, Y. Yu, N. Clayton, Y. Chuang, Y. Wang, S. Mueller, K. Levon, M. Simon and M. Rafailovich, Analyst, 2019, 144, 4266
DOI: 10.1039/C9AN00580C

Social activity

Search articles by author

Spotlight

Advertisements