International Journal of Nanomedicine (Aug 2015)

Core-shell nanostructured hybrid composites for volatile organic compound detection

  • Tung TT,
  • Losic D,
  • Park SJ,
  • Feller JF,
  • Kim TY

Journal volume & issue
Vol. 2015, no. Special Issue on diverse applications in Nano-Theranostics
pp. 203 – 214

Abstract

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Tran Thanh Tung,1,2 Dusan Losic,1 Seung Jun Park,3 Jean-Francois Feller,2 TaeYoung Kim3 1School of Chemical Engineering, The University of Adelaide, North Terrace, Adelaide, SA, Australia; 2Smart Plastics Group, European University of Brittany (UEB), LIMATB-UBS, Lorient, France; 3Department of Bionanotechnology, Gachon University, Sujeong-gu, Seongnam-si, Gyeonggi-do South Korea Abstract: We report a high-performance chemiresistive sensor for detection of volatile organic compound (VOC) vapors based on core-shell hybridized nanostructures of Fe3O4 magnetic nanoparticles (MNPs) and poly(3,4-ethylenedioxythiophene) (PEDOT)-conducting polymers. The MNPs were prepared using microwave-assisted synthesis in the presence of polymerized ionic liquids (PILs), which were used as a linker to couple the MNP and PEDOT. The resulting PEDOT–PIL-modified Fe3O4 hybrids were then explored as a sensing channel material for a chemiresistive sensor to detect VOC vapors. The PEDOT–PIL-modified Fe3O4 sensor exhibited a tunable response, with high sensitivity (down to a concentration of 1 ppm) and low noise level, to VOCs; these VOCs include acetone vapor, which is present in the exhaled breath of potential lung cancer patients. The present sensor, based on the hybrid nanostructured sensing materials, exhibited a 38.8% higher sensitivity and an 11% lower noise level than its PEDOT–PIL-only counterpart. This approach of embedding MNPs in conducting polymers could lead to the development of new electronic noses, which have significant potential for the use in the early diagnosis of lung cancer via the detection of VOC biomarkers. Keywords: hybrid nanomaterials, nanoparticle, conducting polymer, electronic nose, lung cancer detection