Multiscale modification of the conductive PEDOT:PSS polymer for the analysis of biological mixtures in a super-hydrophobic drop
Di Fabrizio, Enzo M.
KAUST DepartmentMaterials Science and Engineering Program
Physical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/602295
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AbstractConducting polymers are materials displaying high electrical conductivity, easy of fabrication, flexibility and biocompatibility, for this, they are routinely employed in organic electronics, printed electronics, and bioelectronics. Organic electrochemical transistors (OECTs) are a second generation of organic thin transistors, in which the insulator layer is an electrolyte medium and the conductive polymer is electrochemically active. OECT devices have been demonstrated in chemical and biological sensing: while accurate in determining the size of individual ions in solution, similar devices break down if challenged with complex mixtures. Here, we combine a conductive PEODOT:PSS polymer with a super-hydrophobic scheme to obtain a family of advanced devices, in which the ability to manipulate a biological solution couples to a precise texture of the substrate (which incorporates five micro-electrodes in a line, and each is a site specific measurement point), and this permits to realize time and space resolved analysis of a solution. While the competition between convection and diffusion in a super-hydrophobic drop operates the separation of different species based on their size and charge, the described device delivers the ability to register a similar difference. In the following, we demonstrate the device in the sensing of a solution in which CTAB and adrenaline are separated with good sensitivity, selectivity and reliability.
CitationMultiscale modification of the conductive PEDOT:PSS polymer for the analysis of biological mixtures in a super-hydrophobic drop 2016 Microelectronic Engineering
SponsorsThis work has been partially funded from the Italian Minister of Health (Project no. GR-2010-2320665). NC acknowledges Nicola Zambelli and Giacomo Benassi for technical support.