In situ fabrication of porous polymer films embedded with perovskite nanocrystals for flexible superhydrophobic piezoresistive sensors.

J Colloid Interface Sci

Jiangsu Engineering Research Center for Dust Control and Occupational Protection, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China. Electronic address:

Published: September 2024

AI Article Synopsis

  • Scientists created a special pressure sensor that can work well even in really humid conditions by making it super water-repellent.
  • This sensor is flexible and super sensitive, able to detect tiny pressure changes and has a fast response time.
  • It can be used to track things like facial expressions, heartbeats, breathing, and wind speed really accurately.

Article Abstract

The application of pressure sensors based on perovskite in high-humidity environments is limited by the effect of water on their stability. Endowing sensors with superhydrophobicity is an effective strategy to overcome the issue. In this work, MAPbBr/Polyvinylidene Fluoride-TFSI composite was prepared by a one-step in-situ strategy to form a flexible superhydrophobic pressure sensor, which exhibited a contact angle of 150.25°. The obtained sensor exhibited a sensitivity of 0.916 in 1 kPa, a detection limit of 0.2 Pa, a precision of 0.1 Pa, and a response/recovery of ∼100 ms, along with good thermal stability. Through density functional theory calculations, it is revealed that the formation of the porosity is attributed to the interaction between the polymer and EMIM TFSI, which further leads to superhydrophobicity. And, the perovskite structure is easy to change under pressure, affecting the carrier transport and electrical signals output, which explains the sensing mechanism. In addition, the sensor performed well in monitoring facial expression, pulse, respiration, finger bending, and wind speed ranging from 1 m/s to 6 m/s. With both the Linear Regression and the Random Forest algorithm, the sensor can monitor the wind speed with an R greater than 0.977 in 60 tests.

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Source
http://dx.doi.org/10.1016/j.jcis.2024.04.140DOI Listing

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