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A Highly Efficient Sensor Platform Using Simply Manufactured Nanodot Patterned Substrates

Overview of attention for article published in Scientific Reports, August 2015
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Title
A Highly Efficient Sensor Platform Using Simply Manufactured Nanodot Patterned Substrates
Published in
Scientific Reports, August 2015
DOI 10.1038/srep13270
Pubmed ID
Authors

Sozaraj Rasappa, Tandra Ghoshal, Dipu Borah, Ramsankar Senthamaraikannan, Justin D. Holmes, Michael A. Morris

Abstract

Block copolymer (BCP) self-assembly is a low-cost means to nanopattern surfaces. Here, we use these nanopatterns to directly print arrays of nanodots onto a conducting substrate (Indium Tin Oxide (ITO) coated glass) for application as an electrochemical sensor for ethanol (EtOH) and hydrogen peroxide (H2O2) detection. The work demonstrates that BCP systems can be used as a highly efficient, flexible methodology for creating functional surfaces of materials. Highly dense iron oxide nanodots arrays that mimicked the original BCP pattern were prepared by an 'insitu' BCP inclusion methodology using poly(styrene)-block-poly(ethylene oxide) (PS-b-PEO). The electrochemical behaviour of these densely packed arrays of iron oxide nanodots fabricated by two different molecular weight PS-b-PEO systems was studied. The dual detection of EtOH and H2O2 was clearly observed. The as-prepared nanodots have good long term thermal and chemical stability at the substrate and demonstrate promising electrocatalytic performance.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 46%
Student > Master 2 15%
Professor 1 8%
Student > Bachelor 1 8%
Lecturer 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Chemistry 5 38%
Materials Science 2 15%
Engineering 2 15%
Physics and Astronomy 1 8%
Agricultural and Biological Sciences 1 8%
Other 1 8%
Unknown 1 8%