Relative humidity sensing properties of doped polyaniline-encased multiwall carbon nanotubes: wearable and flexible human respiration monitoring application
Source abstract
Abstract
Relative humidity (RH) sensors have been fabricated with polyaniline-encased multiwall carbon nanotube (MWCNT) matrix. MWCNT matrices were grown on oxidized Si substrates by catalytic chemical vapour deposition technique. The MWCNT matrices were made hydrophilic by acid treatment. The sensing properties of MWCNTs were improved by encasing the acid-treated MWCNT matrix with PANI. PANI was synthesized via in situ chemical oxidation technique using three acid dopants, named as 5-sulfosalicylic acid, 4-nitrobenzoic acid and hydrochloric acid (HCl). A growth time variation of the PANI was also performed from 1.5 to 2.5 h. The time-varying sensor resistance was recorded with RH change, and RH sensing properties of the corresponding sensors were investigated with the measured data. Doping variation exhibited that CNT–PANI sensor, fabricated with HCl dopant, showed the highest response (42.6% at 90% RH). Synthesis time variation of PANI from 1.5 to 2.5 h exhibited that with an increase in synthesis time, the sensor responsivities degraded from 42.6 to 19.5% at 90% RH. Excellent repeatability, linear response behaviour (R2 = 0.992), low hysteresis loss (± 2.68%) were exhibited by the fabricated RH sensing devices. A facile transfer technology was developed in order to transfer the MWCNT on PET substrate for fabrication of flexible RH sensor. A practical application on human respiration monitoring was demonstrated with the wearable and flexible RH sensors. The humidity sensing mechanism and electron transfer process were schematically explained with the aid of energy band diagram.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Conducting polymers and applications · Advanced Sensor and Energy Harvesting Materials · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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