Room-temperature synthesis and CO 2 -gas sensitivity of bismuth oxide nanosensors.
Pritamkumar V ShindeNanasaheb M ShindeShoyebmohamad Fattemohamad ShaikhDamin LeeJe Moon YunDuck Hyun LeeAbdullah M Al-EnizicRajaram S ManeKwang Ho KimPublished in: RSC advances (2020)
Room-temperature (27 °C) synthesis and carbon dioxide (CO 2 )-gas-sensor applications of bismuth oxide (Bi 2 O 3 ) nanosensors obtained via a direct and superfast chemical-bath-deposition method (CBD) with different surface areas and structures, i.e. , crystallinities and morphologies including a woollen globe, nanosheet, rose-type, and spongy square plate on a glass substrate, are reported. Moprhologies of the Bi 2 O 3 nanosensors are tuned through polyethylene glycol, ethylene glycol, and ammonium fluoride surfactants. The crystal structure, type of crystallinity, and surface appearance are determined from the X-ray diffraction patterns, X-ray photoelectron spectroscopy spectra, and high-resolution transmission electron microscopy images. The room-temperature gas-sensor applications of these Bi 2 O 3 nanosensors for H 2 , H 2 S, NO 2 , SO 2, and CO 2 gases are monitored from 10 to 100 ppm concentrations, wherein Bi 2 O 3 nanosensors of different physical properties demonstrate better performance and response/recovery time measurement for CO 2 gas than those for the other target gases employed. Among various sensor morphologies, the nanosheet-type Bi 2 O 3 sensor has exhibited at 100 ppm concentration of CO 2 gas, a 179% response, 132 s response time, and 82 s recovery time at room-temperature, which is credited to its unique surface morphology, high surface area, and least charge transfer resistance. This suggests that the importance of the surface morphology, surface area, and crystallinity of the Bi 2 O 3 nanosensors used for designing room-temperature operable CO 2 gas sensors for commercial benefits.
Keyphrases
- room temperature
- high resolution
- ionic liquid
- electron microscopy
- crystal structure
- carbon dioxide
- magnetic resonance imaging
- mass spectrometry
- machine learning
- computed tomography
- drinking water
- deep learning
- magnetic resonance
- single molecule
- convolutional neural network
- density functional theory
- contrast enhanced