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A review on electronic bio-sensing approaches based on non-antibody recognition elements

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journal contribution
posted on 2016-04-15, 10:41 authored by Hu Chen, Jingfeng Huang, Alagappan Palaniappan, Yi Wang, Bo Liedberg, Mark PlattMark Platt, Alfred I.Y. Tok
In this review, recent advances in the development of electronic detection methodologies based on non-antibody recognition elements such as functional liposomes, aptamers and synthetic peptides are discussed. Particularly, we highlight the progress of field effect transistor (FET) sensing platforms where possible as the number of publications on FET-based platforms has increased rapidly. Biosensors involving antibody-antigen interactions have been widely applied in diagnostics and healthcare in virtue of their superior selectivity and sensitivity, which can be attributed to their high binding affinity and extraordinary specificity, respectively. However, antibodies typically suffer from fragile and complicated functional structures, large molecular size and sophisticated preparation approaches (resource-intensive and time-consuming), resulting in limitations such as short shelf-life, insufficient stability and poor reproducibility. Recently, bio-sensing approaches based on synthetic elements have been intensively explored. In contrast to existing reports, this review provides a comprehensive overview of recent advances in the development of biosensors utilizing synthetic recognition elements and a detailed comparison of their assay performances. Therefore, this review would serve as a good summary of the efforts for the development of electronic bio-sensing approaches involving synthetic recognition elements.

Funding

This research was funded by the Institute for Sports Research, Nanyang Technological University and Loughborough University.

History

School

  • Science

Department

  • Chemistry

Published in

The Analyst

Citation

CHEN, H. ... et al., 2016. A review on electronic bio-sensing approaches based on non-antibody recognition elements. Analyst, 141, pp.2335-2346.

Publisher

© RSC

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016-03-09

Notes

This paper was accepted for publication in the journal Analyst and the definitive published version is available at http://dx.doi.org/10.1039/C5AN02623G.

ISSN

0003-2654

eISSN

1364-5528

Language

  • en

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