scholarly journals Removal of Formaldehyde Using Highly Active Pt/TiO2Catalysts without Irradiation

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Haibao Huang ◽  
Huiling Huang ◽  
Peng Hu ◽  
Xinguo Ye ◽  
Dennis Y. C. Leung

Formaldehyde (HCHO) is one of the major indoor air pollutants. TiO2supported Pt catalysts were prepared by sol-gel method and used to eliminate HCHO at room temperature without irradiation. The reduced Pt/TiO2catalyst (denoted as Pt/TiO2-H2) showed much higher activity than that calcined in air (denoted as Pt/TiO2-air). More than 96% of the conversion of HCHO was obtained over 0.5 wt% Pt/TiO2-H2, on which highly dispersed metallic Pt nanoparticles with very small size (~2 nm) were identified. Metallic Pt rather than cationic Pt nanoparticles provide the active sites for HCHO oxidation. Negatively charged metallic Pt nanoparticles facilitate the transfer of charge and oxygen species and the activation of oxygen.

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Shuaijie Wang ◽  
Xingxing Cheng

A type of iron and nitrogen codoped titania thin film was prepared by sol-gel method to degrade three typical indoor air pollutants: formaldehyde (HCHO), ammonia (NH3), and benzene (C6H6) under solar light. X-ray diffraction (XRD), UV-Vis spectroscopy, and energy dispersive spectra (EDS) were employed to characterize the photocatalysts. The results showed that the Fe/N codoped TiO2had a stronger absorption in the visible region than pure, Fe-doped, and N-doped TiO2and exhibited excellent photocatalytic ability for the degradation of indoor HCHO, NH3, and C6H6. When the three pollutants existed in indoor air at the same time, the removal percentages of HCHO, NH3, or C6H6after 6 h photocatalytic reaction under solar light reached 48.8%, 50.6%, and 32.0%. The degradation reaction of the three pollutants followed the pseudo-first-order kinetics with the reaction rate constants in the order of 0.110 h−1for ammonia, 0.109 h−1for formaldehyde, and 0.060 h−1for benzene. The reaction rate constant decreased with the increase of initial reactant concentration, which reflected that there was oxidation competition between the substrate and its intermediate during the photocatalytic process.


Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 105
Author(s):  
Haider A. Khwaja

The five papers included in this Special Issue represent a diverse selection of contributions [...]


1990 ◽  
Vol 11 (4) ◽  
pp. 295-302 ◽  
Author(s):  
J. J. McAughey ◽  
J. N. Pritchard ◽  
A. Black

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar

<p>The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.</p>


2017 ◽  
Vol 125 ◽  
pp. 528-555 ◽  
Author(s):  
Brandon E. Boor ◽  
Michal P. Spilak ◽  
Jelle Laverge ◽  
Atila Novoselac ◽  
Ying Xu

Indoor Air ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Toyib Olaniyan ◽  
Mohamed Aqiel Dalvie ◽  
Martin Röösli ◽  
Rajen Naidoo ◽  
Nino Künzli ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document