Environmental Science and Pollution Research - Photovoltaic power generation is an important clean energy alternative to fossil fuels. To reduce CO2 emissions, the Chinese government has ordered... 相似文献
In this work, hexadecyltrimethylammonium-bromide (HTAB)-modified polythiophene (PTh)/TiO2 nanocomposite (HTAB/PTh/TiO2) was applied to remove uranyl ions (UO22+). FT-IR, XRD, ζ potential, TGA, SEM, and XPS were utilized to obtain the chemical and physical properties of HTAB/PTh/TiO2. The effects of HTAB content, preparation temperature, and adsorption conditions on UO22+ removal were investigated comprehensively. And the UO22+ adsorption process on HTAB/PTh/TiO2 was fitted to the Sips model with a saturated adsorption capacity of 234.74 mg/g, which was 6 times over TiO2. The results suggested that the surfactant of HTAB can significantly improve the adsorption ability of TiO2 for UO22+ ions. This work provides a strategy of surfactant modification for enhancing the separation and recovery ability of adsorbent toward UO22+ in the radioactive wastewater.
Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t2 (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories. 相似文献