Plasma-enabled carbon nanostructures for early diagnosis of neurodegenerative diseases

Pineda, Shafique, Han, Zhao Jun, & Ostrikov, Kostya (2014) Plasma-enabled carbon nanostructures for early diagnosis of neurodegenerative diseases. Materials, 7(7), pp. 4896-4929.

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Abstract

Carbon nanostructures (CNs) are amongst the most promising biorecognition nanomaterials due to their unprecedented optical, electrical and structural properties. As such, CNs may be harnessed to tackle the detrimental public health and socio-economic adversities associated with neurodegenerative diseases (NDs). In particular, CNs may be tailored for a specific determination of biomarkers indicative of NDs. However, the realization of such a biosensor represents a significant technological challenge in the uniform fabrication of CNs with outstanding qualities in order to facilitate a highly-sensitive detection of biomarkers suspended in complex biological environments. Notably, the versatility of plasma-based techniques for the synthesis and surface modification of CNs may be embraced to optimize the biorecognition performance and capabilities. This review surveys the recent advances in CN-based biosensors, and highlights the benefits of plasma-processing techniques to enable, enhance, and tailor the performance and optimize the fabrication of CNs, towards the construction of biosensors with unparalleled performance for the early diagnosis of NDs, via a plethora of energy-efficient, environmentally-benign, and inexpensive approaches.

Impact and interest:

1 citations in Scopus
1 citations in Web of Science®
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ID Code: 88830
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: low-temperature plasmas; carbon nanostructures; plasma nanoscience; graphene; nanotubes; nanosheets; nanowalls; biosensor; neurodegenerative diseases
DOI: 10.3390/ma7074896
ISSN: 1996-1944
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 by the authors; licensee MDPI, Basel, Switzerland
Copyright Statement: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Deposited On: 23 Oct 2015 01:00
Last Modified: 23 Oct 2015 01:00

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