4.8 Article

Multifunctional respiration-driven triboelectric nanogenerator for self-powered detection of formaldehyde in exhaled gas and respiratory behavior

Journal

NANO ENERGY
Volume 102, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.nanoen.2022.107711

Keywords

Self -powered sensor; Triboelectric nanogenerator; Ti3C2TX MXene; Formaldehyde sensor; Respiratory monitoring

Funding

  1. National Natural Science Foundation of China [51777215]
  2. Original Innovation Special Project of Science and Technology Plan of Qingdao West Coast New Area [2020- 85]

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A self-powered triboelectric nanogenerator has been developed for the detection of exhaled gas and disease diagnosis. The device exhibits excellent gas-sensing response and fast response/recovery time, and can identify different respiratory types.
Self-powered sensing system has great application prospect in environmental detection and human health. In this work, a triboelectric nanogenerator (TENG) prepared by Ti3C2Tx MXene/amino-functionalized multi-walled carbon nanotubes (MXene/NH2-MWCNTs) was designed for self-powered detection of exhaled gas and disease diagnosis. The TENG driven by respiration can be used as both a power source and a sensor for the self-powered system. MXene/NH2-MWCNTs composite sensitive to formaldehyde gas act as both friction layer and electrode of the TENG. The peak-to-peak value of open-circuit voltage and output power from the TENG can reach up to 136 V and 27 mu W, respectively. As a self-powered formaldehyde sensor, the device has excellent gas-sensing response (35% @ 5 ppm), low detection limit (10 ppb) and fast response/recovery time (51/57 s). In addition, the respiration-driven TENG can detect formaldehyde in the exhaled gas of smokers and identify respiratory types, which has potential application value in diagnosing diseases related to exhaled gas. Sampling tests were per-formed for a variety of respiratory types associated with different diseases. Different respiratory types can be identified by using support vector machine model, and the average prediction accuracy is 100%.

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