journal article·2020-09

The photocatalytic selectivity between molecularly imprinted TiO2 and target contaminants

Xian Liu, Lei Zhu, Xun Wang, Xide Meng, Lingjun Zhong

Journal of Nanoparticle Research· Volume 22· Issue 9
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Abstract

In this paper, it is proposed that the photocatalysis selectivity exists between the catalysts and the target contaminants. Therefore, the selectivity of the two catalysts, MIP-TiO2 and NIP-TiO2, can be distinguished by using the same test wavelength to measure the absorbance of different pollutants and different test wavelength to measure the absorbance of the pollutants. At first, MIP-TiO2 was obtained in one step to degrade ethyl parahydroxybenzoate wastewater. Then, the effects of test wavelength and test time on the standard curve, reaction rate, selectivity coefficient, and selectivity factor, as well as the effect of the amount of molecular imprinting on photocatalytic activity and selectivity are studied through photocatalytic experiments. XRD, FT-IR, and TEM were used to characterize the catalysts. The results show that the test wavelength and reaction time have impact on the reaction rate, selectivity coefficient, while selectivity factor, and have no effect on the simulation of the standard curve. Furthermore, the selectivity factor does not change its applicability to calculate the selectivity due to the different selectivity coefficients. In addition, the degradation rate of target contaminants in 2 h by MIP-TiO2 is 81%, while NIP-TiO2 is only 74%. Moreover, MIP-TiO2 has a selectivity factor of 2.680, indicating that it has a higher selectivity for target contaminants than NIP-TiO2. This paper provides a further understanding of the photocatalytic selectivity between molecularly imprinted TiO2 and target contaminants.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Photocatalysis Techniques · TiO2 Photocatalysis and Solar Cells · Catalytic Processes in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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