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Ultrahigh sensitivity and layer-dependent sensing performance of phosphorene-based gas sensors

Author

Listed:
  • Shumao Cui

    (University of Wisconsin–Milwaukee)

  • Haihui Pu

    (University of Wisconsin–Milwaukee)

  • Spencer A. Wells

    (Northwestern University
    Northwestern University)

  • Zhenhai Wen

    (University of Wisconsin–Milwaukee)

  • Shun Mao

    (University of Wisconsin–Milwaukee)

  • Jingbo Chang

    (University of Wisconsin–Milwaukee)

  • Mark C. Hersam

    (Northwestern University
    Northwestern University)

  • Junhong Chen

    (University of Wisconsin–Milwaukee)

Abstract

Two-dimensional (2D) layered materials have attracted significant attention for device applications because of their unique structures and outstanding properties. Here, a field-effect transistor (FET) sensor device is fabricated based on 2D phosphorene nanosheets (PNSs). The PNS sensor exhibits an ultrahigh sensitivity to NO2 in dry air and the sensitivity is dependent on its thickness. A maximum response is observed for 4.8-nm-thick PNS, with a sensitivity up to 190% at 20 parts per billion (p.p.b.) at room temperature. First-principles calculations combined with the statistical thermodynamics modelling predict that the adsorption density is ∼1015 cm−2 for the 4.8-nm-thick PNS when exposed to 20 p.p.b. NO2 at 300 K. Our sensitivity modelling further suggests that the dependence of sensitivity on the PNS thickness is dictated by the band gap for thinner sheets ( 10 nm).

Suggested Citation

  • Shumao Cui & Haihui Pu & Spencer A. Wells & Zhenhai Wen & Shun Mao & Jingbo Chang & Mark C. Hersam & Junhong Chen, 2015. "Ultrahigh sensitivity and layer-dependent sensing performance of phosphorene-based gas sensors," Nature Communications, Nature, vol. 6(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9632
    DOI: 10.1038/ncomms9632
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    Cited by:

    1. Arnab Maity & Haihui Pu & Xiaoyu Sui & Jingbo Chang & Kai J. Bottum & Bing Jin & Guihua Zhou & Yale Wang & Ganhua Lu & Junhong Chen, 2023. "Scalable graphene sensor array for real-time toxins monitoring in flowing water," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Seung-Hyun Sung & Jun Min Suh & Yun Ji Hwang & Ho Won Jang & Jeon Gue Park & Seong Chan Jun, 2024. "Data-centric artificial olfactory system based on the eigengraph," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Jayraj V. Vaghasiya & Carmen C. Mayorga-Martinez & Jan Vyskočil & Martin Pumera, 2023. "Black phosphorous-based human-machine communication interface," Nature Communications, Nature, vol. 14(1), pages 1-8, December.

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